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https://paperswithcode.com/paper/generalized-robust-bayesian-committee-machine
|
1806.00720
| null | null |
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
|
In order to scale standard Gaussian process (GP) regression to large-scale
datasets, aggregation models employ factorized training process and then
combine predictions from distributed experts. The state-of-the-art aggregation
models, however, either provide inconsistent predictions or require
time-consuming aggregation process. We first prove the inconsistency of typical
aggregations using disjoint or random data partition, and then present a
consistent yet efficient aggregation model for large-scale GP. The proposed
model inherits the advantages of aggregations, e.g., closed-form inference and
aggregation, parallelization and distributed computing. Furthermore,
theoretical and empirical analyses reveal that the new aggregation model
performs better due to the consistent predictions that converge to the true
underlying function when the training size approaches infinity.
|
In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts.
|
http://arxiv.org/abs/1806.00720v1
|
http://arxiv.org/pdf/1806.00720v1.pdf
|
ICML 2018 7
|
[
"Haitao Liu",
"Jianfei Cai",
"Yi Wang",
"Yew-Soon Ong"
] |
[
"Distributed Computing",
"regression"
] | 2018-06-03T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1958
|
http://proceedings.mlr.press/v80/liu18a/liu18a.pdf
|
generalized-robust-bayesian-committee-machine-1
| null |
[
{
"code_snippet_url": null,
"description": "**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.\r\n\r\nImage Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams",
"full_name": "Gaussian Process",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "Gaussian Process",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/an-interpretable-deep-hierarchical-semantic
|
1806.00712
| null | null |
An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification
|
While deep learning methods are increasingly being applied to tasks such as
computer-aided diagnosis, these models are difficult to interpret, do not
incorporate prior domain knowledge, and are often considered as a "black-box."
The lack of model interpretability hinders them from being fully understood by
target users such as radiologists. In this paper, we present a novel
interpretable deep hierarchical semantic convolutional neural network (HSCNN)
to predict whether a given pulmonary nodule observed on a computed tomography
(CT) scan is malignant. Our network provides two levels of output: 1) low-level
radiologist semantic features, and 2) a high-level malignancy prediction score.
The low-level semantic outputs quantify the diagnostic features used by
radiologists and serve to explain how the model interprets the images in an
expert-driven manner. The information from these low-level tasks, along with
the representations learned by the convolutional layers, are then combined and
used to infer the high-level task of predicting nodule malignancy. This unified
architecture is trained by optimizing a global loss function including both
low- and high-level tasks, thereby learning all the parameters within a joint
framework. Our experimental results using the Lung Image Database Consortium
(LIDC) show that the proposed method not only produces interpretable lung
cancer predictions but also achieves significantly better results compared to
common 3D CNN approaches.
|
While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box."
|
http://arxiv.org/abs/1806.00712v1
|
http://arxiv.org/pdf/1806.00712v1.pdf
| null |
[
"Shiwen Shen",
"Simon X. Han",
"Denise R. Aberle",
"Alex A. T. Bui",
"Willliam Hsu"
] |
[
"Computed Tomography (CT)",
"Diagnostic",
"General Classification"
] | 2018-06-02T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "Interpretability",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images for downstream tasks such as classification and object detection. The most popular subcategory are convolutional neural networks. Below you can find a continuously updated list of image models.",
"name": "Image Models",
"parent": null
},
"name": "Interpretability",
"source_title": "CAM: Causal additive models, high-dimensional order search and penalized regression",
"source_url": "http://arxiv.org/abs/1310.1533v2"
}
] |
https://paperswithcode.com/paper/learning-and-generalizing-motion-primitives
|
1806.00711
| null | null |
Learning and Generalizing Motion Primitives from Driving Data for Path-Tracking Applications
|
Considering the driving habits which are learned from the naturalistic
driving data in the path-tracking system can significantly improve the
acceptance of intelligent vehicles. Therefore, the goal of this paper is to
generate the prediction results of lateral commands with confidence regions
according to the reference based on the learned motion primitives. We present a
two-level structure for learning and generalizing motion primitives through
demonstrations. The lower-level motion primitives are generated under the path
segmentation and clustering layer in the upper-level. The Gaussian Mixture
Model(GMM) is utilized to represent the primitives and Gaussian Mixture
Regression (GMR) is selected to generalize the motion primitives. We show how
the upper-level can help to improve the prediction accuracy and evaluate the
influence of different time scales and the number of Gaussian components. The
model is trained and validated by using the driving data collected from the
Beijing Institute of Technology (BIT) intelligent vehicle platform. Experiment
results show that the proposed method can extract the motion primitives from
the driving data and predict the future lateral control commands with high
accuracy.
| null |
http://arxiv.org/abs/1806.00711v2
|
http://arxiv.org/pdf/1806.00711v2.pdf
| null |
[
"Boyang Wang",
"Zirui Li",
"Jianwei Gong",
"Yidi Liu",
"Huiyan Chen",
"Chao Lu"
] |
[
"Clustering"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/bandit-learning-with-positive-externalities
|
1802.05693
| null | null |
Bandit Learning with Positive Externalities
|
In many platforms, user arrivals exhibit a self-reinforcing behavior: future
user arrivals are likely to have preferences similar to users who were
satisfied in the past. In other words, arrivals exhibit positive externalities.
We study multiarmed bandit (MAB) problems with positive externalities. We show
that the self-reinforcing preferences may lead standard benchmark algorithms
such as UCB to exhibit linear regret. We develop a new algorithm, Balanced
Exploration (BE), which explores arms carefully to avoid suboptimal convergence
of arrivals before sufficient evidence is gathered. We also introduce an
adaptive variant of BE which successively eliminates suboptimal arms. We
analyze their asymptotic regret, and establish optimality by showing that no
algorithm can perform better.
| null |
http://arxiv.org/abs/1802.05693v5
|
http://arxiv.org/pdf/1802.05693v5.pdf
|
NeurIPS 2018 12
|
[
"Virag Shah",
"Jose Blanchet",
"Ramesh Johari"
] |
[] | 2018-02-15T00:00:00 |
http://papers.nips.cc/paper/7740-bandit-learning-with-positive-externalities
|
http://papers.nips.cc/paper/7740-bandit-learning-with-positive-externalities.pdf
|
bandit-learning-with-positive-externalities-1
| null |
[] |
https://paperswithcode.com/paper/long-short-term-memory-for-japanese-word
|
1709.08011
| null | null |
Long Short-Term Memory for Japanese Word Segmentation
|
This study presents a Long Short-Term Memory (LSTM) neural network approach
to Japanese word segmentation (JWS). Previous studies on Chinese word
segmentation (CWS) succeeded in using recurrent neural networks such as LSTM
and gated recurrent units (GRU). However, in contrast to Chinese, Japanese
includes several character types, such as hiragana, katakana, and kanji, that
produce orthographic variations and increase the difficulty of word
segmentation. Additionally, it is important for JWS tasks to consider a global
context, and yet traditional JWS approaches rely on local features. In order to
address this problem, this study proposes employing an LSTM-based approach to
JWS. The experimental results indicate that the proposed model achieves
state-of-the-art accuracy with respect to various Japanese corpora.
| null |
http://arxiv.org/abs/1709.08011v3
|
http://arxiv.org/pdf/1709.08011v3.pdf
|
PACLIC 2018 12
|
[
"Yoshiaki Kitagawa",
"Mamoru Komachi"
] |
[
"Chinese Word Segmentation",
"Japanese Word Segmentation",
"Segmentation"
] | 2017-09-23T00:00:00 |
https://aclanthology.org/Y18-1033
|
https://aclanthology.org/Y18-1033.pdf
| null | null |
[] |
https://paperswithcode.com/paper/a-note-on-belief-structures-and-s
|
1805.10672
| null | null |
A note on belief structures and S-approximation spaces
|
We study relations between evidence theory and S-approximation spaces. Both theories have their roots in the analysis of Dempster's multivalued mappings and lower and upper probabilities and have close relations to rough sets. We show that an S-approximation space, satisfying a monotonicity condition, can induce a natural belief structure which is a fundamental block in evidence theory. We also demonstrate that one can induce a natural belief structure on one set, given a belief structure on another set if those sets are related by a partial monotone S-approximation space.
| null |
https://arxiv.org/abs/1805.10672v4
|
https://arxiv.org/pdf/1805.10672v4.pdf
| null |
[
"Ali Shakiba",
"Amir Kafshdar Goharshady",
"MohammadReza Hooshmandasl",
"Mohsen Alambardar Meybodi"
] |
[] | 2018-05-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/nlp-assisted-software-testing-a-systematic
|
1806.00696
| null | null |
NLP-assisted software testing: A systematic mapping of the literature
|
Context: To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large amount of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area. Objective: Our objective is to summarize the state-of-the-art in NLP-assisted software testing which could benefit practitioners to potentially utilize those NLP-based techniques. Moreover, this can benefit researchers in providing an overview of the research landscape. Method: To address the above need, we conducted a survey in the form of a systematic literature mapping (classification). After compiling an initial pool of 95 papers, we conducted a systematic voting, and our final pool included 67 technical papers. Results: This review paper provides an overview of the contribution types presented in the papers, types of NLP approaches used to assist software testing, types of required input requirements, and a review of tool support in this area. Some key results we have detected are: (1) only four of the 38 tools (11%) presented in the papers are available for download; (2) a larger ratio of the papers (30 of 67) provided a shallow exposure to the NLP aspects (almost no details). Conclusion: This paper would benefit both practitioners and researchers by serving as an "index" to the body of knowledge in this area. The results could help practitioners utilizing the existing NLP-based techniques; this in turn reduces the cost of test-case design and decreases the amount of human resources spent on test activities. After sharing this review with some of our industrial collaborators, initial insights show that this review can indeed be useful and beneficial to practitioners.
| null |
https://arxiv.org/abs/1806.00696v3
|
https://arxiv.org/pdf/1806.00696v3.pdf
| null |
[
"Vahid Garousi",
"Sara Bauer",
"Michael Felderer"
] |
[
"software testing"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-feature-aggregation-and-image-re-ranking
|
1805.08587
| null | null |
Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval
|
Image retrieval based on deep convolutional features has demonstrated
state-of-the-art performance in popular benchmarks. In this paper, we present a
unified solution to address deep convolutional feature aggregation and image
re-ranking by simulating the dynamics of heat diffusion. A distinctive problem
in image retrieval is that repetitive or \emph{bursty} features tend to
dominate final image representations, resulting in representations less
distinguishable. We show that by considering each deep feature as a heat
source, our unsupervised aggregation method is able to avoid
over-representation of \emph{bursty} features. We additionally provide a
practical solution for the proposed aggregation method and further show the
efficiency of our method in experimental evaluation. Inspired by the
aforementioned deep feature aggregation method, we also propose a method to
re-rank a number of top ranked images for a given query image by considering
the query as the heat source. Finally, we extensively evaluate the proposed
approach with pre-trained and fine-tuned deep networks on common public
benchmarks and show superior performance compared to previous work.
|
We show that by considering each deep feature as a heat source, our unsupervised aggregation method is able to avoid over-representation of \emph{bursty} features.
|
http://arxiv.org/abs/1805.08587v5
|
http://arxiv.org/pdf/1805.08587v5.pdf
| null |
[
"Shanmin Pang",
"Jin Ma",
"Jianru Xue",
"Jihua Zhu",
"Vicente Ordonez"
] |
[
"Image Retrieval",
"Re-Ranking",
"Retrieval"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/hierarchical-attention-based-recurrent
|
1806.00685
| null | null |
Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction
|
Time series prediction has been studied in a variety of domains. However, it
is still challenging to predict future series given historical observations and
past exogenous data. Existing methods either fail to consider the interactions
among different components of exogenous variables which may affect the
prediction accuracy, or cannot model the correlations between exogenous data
and target data. Besides, the inherent temporal dynamics of exogenous data are
also related to the target series prediction, and thus should be considered as
well. To address these issues, we propose an end-to-end deep learning model,
i.e., Hierarchical attention-based Recurrent Highway Network (HRHN), which
incorporates spatio-temporal feature extraction of exogenous variables and
temporal dynamics modeling of target variables into a single framework.
Moreover, by introducing the hierarchical attention mechanism, HRHN can
adaptively select the relevant exogenous features in different semantic levels.
We carry out comprehensive empirical evaluations with various methods over
several datasets, and show that HRHN outperforms the state of the arts in time
series prediction, especially in capturing sudden changes and sudden
oscillations of time series.
|
Time series prediction has been studied in a variety of domains.
|
http://arxiv.org/abs/1806.00685v1
|
http://arxiv.org/pdf/1806.00685v1.pdf
| null |
[
"Yunzhe Tao",
"Lin Ma",
"Weizhong Zhang",
"Jian Liu",
"Wei Liu",
"Qiang Du"
] |
[
"Prediction",
"Time Series",
"Time Series Analysis",
"Time Series Prediction"
] | 2018-06-02T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/kefirski/pytorch_Highway/blob/70b75db7a2d029f4bbe08fd4c7d69e36bf7b6d3a/highway/highway.py#L35",
"description": "A **Highway Layer** contains an information highway to other layers that helps with information flow. It is characterised by the use of a gating unit to help this information flow. \r\n\r\nA plain feedforward neural network typically consists of $L$ layers where the $l$th layer ($l \\in ${$1, 2, \\dots, L$}) applies a nonlinear transform $H$ (parameterized by $\\mathbf{W\\_{H,l}}$) on its input $\\mathbf{x\\_{l}}$ to produce its output $\\mathbf{y\\_{l}}$. Thus, $\\mathbf{x\\_{1}}$ is the input to the network and $\\mathbf{y\\_{L}}$ is the network’s output. Omitting the layer index and biases for clarity,\r\n\r\n$$ \\mathbf{y} = H\\left(\\mathbf{x},\\mathbf{W\\_{H}}\\right) $$\r\n\r\n$H$ is usually an affine transform followed by a non-linear activation function, but in general it may take other forms. \r\n\r\nFor a [highway network](https://paperswithcode.com/method/highway-network), we additionally define two nonlinear transforms $T\\left(\\mathbf{x},\\mathbf{W\\_{T}}\\right)$ and $C\\left(\\mathbf{x},\\mathbf{W\\_{C}}\\right)$ such that:\r\n\r\n$$ \\mathbf{y} = H\\left(\\mathbf{x},\\mathbf{W\\_{H}}\\right)·T\\left(\\mathbf{x},\\mathbf{W\\_{T}}\\right) + \\mathbf{x}·C\\left(\\mathbf{x},\\mathbf{W\\_{C}}\\right)$$\r\n\r\nWe refer to T as the transform gate and C as the carry gate, since they express how much of the output is produced by transforming the input and carrying it, respectively. In the original paper, the authors set $C = 1 − T$, giving:\r\n\r\n$$ \\mathbf{y} = H\\left(\\mathbf{x},\\mathbf{W\\_{H}}\\right)·T\\left(\\mathbf{x},\\mathbf{W\\_{T}}\\right) + \\mathbf{x}·\\left(1-T\\left(\\mathbf{x},\\mathbf{W\\_{T}}\\right)\\right)$$\r\n\r\nThe authors set:\r\n\r\n$$ T\\left(x\\right) = \\sigma\\left(\\mathbf{W\\_{T}}^{T}\\mathbf{x} + \\mathbf{b\\_{T}}\\right) $$\r\n\r\nImage: [Sik-Ho Tsang](https://towardsdatascience.com/review-highway-networks-gating-function-to-highway-image-classification-5a33833797b5)",
"full_name": "Highway Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "The following is a list of miscellaneous components used in neural networks.",
"name": "Miscellaneous Components",
"parent": null
},
"name": "Highway Layer",
"source_title": "Highway Networks",
"source_url": "http://arxiv.org/abs/1505.00387v2"
},
{
"code_snippet_url": "https://github.com/kefirski/pytorch_Highway/blob/70b75db7a2d029f4bbe08fd4c7d69e36bf7b6d3a/highway/highway.py#L5",
"description": "A **Highway Network** is an architecture designed to ease gradient-based training of very deep networks. They allow unimpeded information flow across several layers on \"information highways\". The architecture is characterized by the use of gating units which learn to regulate the flow of information through a network. Highway networks with hundreds of layers can be trained directly using stochastic gradient descent and with a variety of activation functions.",
"full_name": "Highway Network",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Highway Network",
"source_title": "Highway Networks",
"source_url": "http://arxiv.org/abs/1505.00387v2"
}
] |
https://paperswithcode.com/paper/deep-pepper-expert-iteration-based-chess
|
1806.00683
| null | null |
Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting
|
An almost-perfect chess playing agent has been a long standing challenge in
the field of Artificial Intelligence. Some of the recent advances demonstrate
we are approaching that goal. In this project, we provide methods for faster
training of self-play style algorithms, mathematical details of the algorithm
used, various potential future directions, and discuss most of the relevant
work in the area of computer chess. Deep Pepper uses embedded knowledge to
accelerate the training of the chess engine over a "tabula rasa" system such as
Alpha Zero. We also release our code to promote further research.
| null |
http://arxiv.org/abs/1806.00683v2
|
http://arxiv.org/pdf/1806.00683v2.pdf
| null |
[
"Sai Krishna G. V.",
"Kyle Goyette",
"Ahmad Chamseddine",
"Breandan Considine"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-hierarchical-fuzzy-system-for-an-advanced
|
1806.04611
| null | null |
A Hierarchical Fuzzy System for an Advanced Driving Assistance System
|
In this study, we present a hierarchical fuzzy system by evaluating the risk
state for a Driver Assistance System in order to contribute in reducing the
road accident's number. A key component of this system is its ability to
continually detect and test the inside and outside risks in real time: The
outside car risks by detecting various road moving objects; this proposed
system stands on computer vision approaches. The inside risks by presenting an
automatic system for drowsy driving identification or detection by evaluating
EEG signals of the driver; this developed system is based on computer vision
techniques and biometrics factors (electroencephalogram EEG). This proposed
system is then composed of three main modules. The first module is responsible
for identifying the driver drowsiness state through his eye movements (physical
drowsiness). The second one is responsible for detecting and analysing his
physiological signals to also identify his drowsiness state (moral drowsiness).
The third module is responsible to evaluate the road driving risks by detecting
of the road different moving objects in a real time. The final decision will be
obtained by merging of the three detection systems through the use of fuzzy
decision rules. Finally, the proposed approach has been improved on ten samples
from a proposed dataset.
| null |
http://arxiv.org/abs/1806.04611v1
|
http://arxiv.org/pdf/1806.04611v1.pdf
| null |
[
"Mejdi Ben Dkhil",
"Ali Wali",
"Adel M. ALIMI"
] |
[
"EEG",
"Electroencephalogram (EEG)"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/nonlocal-neural-networks-nonlocal-diffusion
|
1806.00681
| null | null |
Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling
|
Nonlocal neural networks have been proposed and shown to be effective in
several computer vision tasks, where the nonlocal operations can directly
capture long-range dependencies in the feature space. In this paper, we study
the nature of diffusion and damping effect of nonlocal networks by doing
spectrum analysis on the weight matrices of the well-trained networks, and then
propose a new formulation of the nonlocal block. The new block not only learns
the nonlocal interactions but also has stable dynamics, thus allowing deeper
nonlocal structures. Moreover, we interpret our formulation from the general
nonlocal modeling perspective, where we make connections between the proposed
nonlocal network and other nonlocal models, such as nonlocal diffusion process
and Markov jump process.
| null |
http://arxiv.org/abs/1806.00681v4
|
http://arxiv.org/pdf/1806.00681v4.pdf
|
NeurIPS 2018 12
|
[
"Yunzhe Tao",
"Qi Sun",
"Qiang Du",
"Wei Liu"
] |
[] | 2018-06-02T00:00:00 |
http://papers.nips.cc/paper/7331-nonlocal-neural-networks-nonlocal-diffusion-and-nonlocal-modeling
|
http://papers.nips.cc/paper/7331-nonlocal-neural-networks-nonlocal-diffusion-and-nonlocal-modeling.pdf
|
nonlocal-neural-networks-nonlocal-diffusion-1
| null |
[] |
https://paperswithcode.com/paper/a-geometric-approach-for-real-time-monitoring
|
1806.00676
| null | null |
A Geometric Approach for Real-time Monitoring of Dynamic Large Scale Graphs: AS-level graphs illustrated
|
The monitoring of large dynamic networks is a major chal- lenge for a wide
range of application. The complexity stems from properties of the underlying
graphs, in which slight local changes can lead to sizable variations of global
prop- erties, e.g., under certain conditions, a single link cut that may be
overlooked during monitoring can result in splitting the graph into two
disconnected components. Moreover, it is often difficult to determine whether a
change will propagate globally or remain local. Traditional graph theory
measure such as the centrality or the assortativity of the graph are not
satisfying to characterize global properties of the graph. In this paper, we
tackle the problem of real-time monitoring of dynamic large scale graphs by
developing a geometric approach that leverages notions of geometric curvature
and recent development in graph embeddings using Ollivier-Ricci curvature [47].
We illustrate the use of our method by consid- ering the practical case of
monitoring dynamic variations of global Internet using topology changes
information provided by combining several BGP feeds. In particular, we use our
method to detect major events and changes via the geometry of the embedding of
the graph.
| null |
http://arxiv.org/abs/1806.00676v1
|
http://arxiv.org/pdf/1806.00676v1.pdf
| null |
[
"Loqman Salamatian",
"Dali Kaafar",
"Kavé Salamatian"
] |
[] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/beyond-the-one-step-greedy-approach-in-1
|
1802.03654
| null | null |
Beyond the One Step Greedy Approach in Reinforcement Learning
|
The famous Policy Iteration algorithm alternates between policy improvement
and policy evaluation. Implementations of this algorithm with several variants
of the latter evaluation stage, e.g, $n$-step and trace-based returns, have
been analyzed in previous works. However, the case of multiple-step lookahead
policy improvement, despite the recent increase in empirical evidence of its
strength, has to our knowledge not been carefully analyzed yet. In this work,
we introduce the first such analysis. Namely, we formulate variants of
multiple-step policy improvement, derive new algorithms using these definitions
and prove their convergence. Moreover, we show that recent prominent
Reinforcement Learning algorithms are, in fact, instances of our framework. We
thus shed light on their empirical success and give a recipe for deriving new
algorithms for future study.
| null |
http://arxiv.org/abs/1802.03654v3
|
http://arxiv.org/pdf/1802.03654v3.pdf
| null |
[
"Yonathan Efroni",
"Gal Dalal",
"Bruno Scherrer",
"Shie Mannor"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-02-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/emotion-detection-in-text-a-review
|
1806.00674
| null | null |
Emotion Detection in Text: a Review
|
In recent years, emotion detection in text has become more popular due to its
vast potential applications in marketing, political science, psychology,
human-computer interaction, artificial intelligence, etc. Access to a huge
amount of textual data, especially opinionated and self-expression text also
played a special role to bring attention to this field. In this paper, we
review the work that has been done in identifying emotion expressions in text
and argue that although many techniques, methodologies, and models have been
created to detect emotion in text, there are various reasons that make these
methods insufficient. Although, there is an essential need to improve the
design and architecture of current systems, factors such as the complexity of
human emotions, and the use of implicit and metaphorical language in expressing
it, lead us to think that just re-purposing standard methodologies will not be
enough to capture these complexities, and it is important to pay attention to
the linguistic intricacies of emotion expression.
| null |
http://arxiv.org/abs/1806.00674v1
|
http://arxiv.org/pdf/1806.00674v1.pdf
| null |
[
"Armin Seyeditabari",
"Narges Tabari",
"Wlodek Zadrozny"
] |
[
"Marketing"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/optimal-clustering-under-uncertainty
|
1806.00672
| null | null |
Optimal Clustering under Uncertainty
|
Classical clustering algorithms typically either lack an underlying
probability framework to make them predictive or focus on parameter estimation
rather than defining and minimizing a notion of error. Recent work addresses
these issues by developing a probabilistic framework based on the theory of
random labeled point processes and characterizing a Bayes clusterer that
minimizes the number of misclustered points. The Bayes clusterer is analogous
to the Bayes classifier. Whereas determining a Bayes classifier requires full
knowledge of the feature-label distribution, deriving a Bayes clusterer
requires full knowledge of the point process. When uncertain of the point
process, one would like to find a robust clusterer that is optimal over the
uncertainty, just as one may find optimal robust classifiers with uncertain
feature-label distributions. Herein, we derive an optimal robust clusterer by
first finding an effective random point process that incorporates all
randomness within its own probabilistic structure and from which a Bayes
clusterer can be derived that provides an optimal robust clusterer relative to
the uncertainty. This is analogous to the use of effective class-conditional
distributions in robust classification. After evaluating the performance of
robust clusterers in synthetic mixtures of Gaussians models, we apply the
framework to granular imaging, where we make use of the asymptotic
granulometric moment theory for granular images to relate robust clustering
theory to the application.
| null |
http://arxiv.org/abs/1806.00672v1
|
http://arxiv.org/pdf/1806.00672v1.pdf
| null |
[
"Lori A. Dalton",
"Marco E. Benalcázar",
"Edward R. Dougherty"
] |
[
"Clustering",
"parameter estimation",
"Point Processes",
"Robust classification"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/locally-interpretable-models-and-effects
|
1806.00663
| null | null |
Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)
|
Supervised Machine Learning (SML) algorithms such as Gradient Boosting,
Random Forest, and Neural Networks have become popular in recent years due to
their increased predictive performance over traditional statistical methods.
This is especially true with large data sets (millions or more observations and
hundreds to thousands of predictors). However, the complexity of the SML models
makes them opaque and hard to interpret without additional tools. There has
been a lot of interest recently in developing global and local diagnostics for
interpreting and explaining SML models. In this paper, we propose locally
interpretable models and effects based on supervised partitioning (trees)
referred to as LIME-SUP. This is in contrast with the KLIME approach that is
based on clustering the predictor space. We describe LIME-SUP based on fitting
trees to the fitted response (LIM-SUP-R) as well as the derivatives of the
fitted response (LIME-SUP-D). We compare the results with KLIME and describe
its advantages using simulation and real data.
| null |
http://arxiv.org/abs/1806.00663v1
|
http://arxiv.org/pdf/1806.00663v1.pdf
| null |
[
"Linwei Hu",
"Jie Chen",
"Vijayan N. Nair",
"Agus Sudjianto"
] |
[
"Clustering"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-active-learning-of-sparse
|
1805.02350
| null | null |
Efficient active learning of sparse halfspaces
|
We study the problem of efficient PAC active learning of homogeneous linear
classifiers (halfspaces) in $\mathbb{R}^d$, where the goal is to learn a
halfspace with low error using as few label queries as possible. Under the
extra assumption that there is a $t$-sparse halfspace that performs well on the
data ($t \ll d$), we would like our active learning algorithm to be {\em
attribute efficient}, i.e. to have label requirements sublinear in $d$. In this
paper, we provide a computationally efficient algorithm that achieves this
goal. Under certain distributional assumptions on the data, our algorithm
achieves a label complexity of $O(t \cdot \mathrm{polylog}(d, \frac 1
\epsilon))$. In contrast, existing algorithms in this setting are either
computationally inefficient, or subject to label requirements polynomial in $d$
or $\frac 1 \epsilon$.
| null |
http://arxiv.org/abs/1805.02350v2
|
http://arxiv.org/pdf/1805.02350v2.pdf
| null |
[
"Chicheng Zhang"
] |
[
"Active Learning",
"Attribute"
] | 2018-05-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/online-learning-sensing-matrix-and
|
1701.01000
| null | null |
Online Learning Sensing Matrix and Sparsifying Dictionary Simultaneously for Compressive Sensing
|
This paper considers the problem of simultaneously learning the Sensing
Matrix and Sparsifying Dictionary (SMSD) on a large training dataset. To
address the formulated joint learning problem, we propose an online algorithm
that consists of a closed-form solution for optimizing the sensing matrix with
a fixed sparsifying dictionary and a stochastic method for learning the
sparsifying dictionary on a large dataset when the sensing matrix is given.
Benefiting from training on a large dataset, the obtained compressive sensing
(CS) system by the proposed algorithm yields a much better performance in terms
of signal recovery accuracy than the existing ones. The simulation results on
natural images demonstrate the effectiveness of the suggested online algorithm
compared with the existing methods.
|
The simulation results on natural images demonstrate the effectiveness of the suggested online algorithm compared with the existing methods.
|
http://arxiv.org/abs/1701.01000v4
|
http://arxiv.org/pdf/1701.01000v4.pdf
| null |
[
"Tao Hong",
"Zhihui Zhu"
] |
[
"Compressive Sensing"
] | 2017-01-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/signal-and-noise-statistics-oblivious
|
1806.00650
| null | null |
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
|
Orthogonal matching pursuit (OMP) is a widely used algorithm for recovering
sparse high dimensional vectors in linear regression models. The optimal
performance of OMP requires \textit{a priori} knowledge of either the sparsity
of regression vector or noise statistics. Both these statistics are rarely
known \textit{a priori} and are very difficult to estimate. In this paper, we
present a novel technique called residual ratio thresholding (RRT) to operate
OMP without any \textit{a priori} knowledge of sparsity and noise statistics
and establish finite sample and large sample support recovery guarantees for
the same. Both analytical results and numerical simulations in real and
synthetic data sets indicate that RRT has a performance comparable to OMP with
\textit{a priori} knowledge of sparsity and noise statistics.
| null |
http://arxiv.org/abs/1806.00650v1
|
http://arxiv.org/pdf/1806.00650v1.pdf
|
ICML 2018 7
|
[
"Sreejith Kallummil",
"Sheetal Kalyani"
] |
[
"regression"
] | 2018-06-02T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2315
|
http://proceedings.mlr.press/v80/kallummil18a/kallummil18a.pdf
|
signal-and-noise-statistics-oblivious-1
| null |
[
{
"code_snippet_url": null,
"description": "**Linear Regression** is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most common is *least squares*, where we minimize the mean square error between the predicted values $\\hat{y} = \\textbf{X}\\hat{\\beta}$ and actual values $y$: $\\left(y-\\textbf{X}\\beta\\right)^{2}$.\r\n\r\nWe can also define the problem in probabilistic terms as a generalized linear model (GLM) where the pdf is a Gaussian distribution, and then perform maximum likelihood estimation to estimate $\\hat{\\beta}$.\r\n\r\nImage Source: [Wikipedia](https://en.wikipedia.org/wiki/Linear_regression)",
"full_name": "Linear Regression",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Generalized Linear Models (GLMs)** are a class of models that generalize upon linear regression by allowing many more distributions to be modeled for the response variable via a link function. Below you can find a continuously updating list of GLMs.",
"name": "Generalized Linear Models",
"parent": null
},
"name": "Linear Regression",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/a-reinforced-topic-aware-convolutional
|
1805.03616
| null | null |
A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization
|
In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization. Through jointly attending to topics and word-level alignment, our approach can improve coherence, diversity, and informativeness of generated summaries via a biased probability generation mechanism. On the other hand, reinforcement training, like SCST, directly optimizes the proposed model with respect to the non-differentiable metric ROUGE, which also avoids the exposure bias during inference. We carry out the experimental evaluation with state-of-the-art methods over the Gigaword, DUC-2004, and LCSTS datasets. The empirical results demonstrate the superiority of our proposed method in the abstractive summarization.
| null |
https://arxiv.org/abs/1805.03616v3
|
https://arxiv.org/pdf/1805.03616v3.pdf
| null |
[
"Li Wang",
"Junlin Yao",
"Yunzhe Tao",
"Li Zhong",
"Wei Liu",
"Qiang Du"
] |
[
"Abstractive Text Summarization",
"Diversity",
"Informativeness",
"Text Summarization"
] | 2018-05-09T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Self-critical Sequence Training",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Reinforcement Learning Frameworks",
"parent": null
},
"name": "SCST",
"source_title": "Self-critical Sequence Training for Image Captioning",
"source_url": "http://arxiv.org/abs/1612.00563v2"
}
] |
https://paperswithcode.com/paper/binary-classification-with-karmic-threshold
|
1806.00640
| null | null |
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
|
Complex performance measures, beyond the popular measure of accuracy, are
increasingly being used in the context of binary classification. These complex
performance measures are typically not even decomposable, that is, the loss
evaluated on a batch of samples cannot typically be expressed as a sum or
average of losses evaluated at individual samples, which in turn requires new
theoretical and methodological developments beyond standard treatments of
supervised learning. In this paper, we advance this understanding of binary
classification for complex performance measures by identifying two key
properties: a so-called Karmic property, and a more technical
threshold-quasi-concavity property, which we show is milder than existing
structural assumptions imposed on performance measures. Under these properties,
we show that the Bayes optimal classifier is a threshold function of the
conditional probability of positive class. We then leverage this result to come
up with a computationally practical plug-in classifier, via a novel threshold
estimator, and further, provide a novel statistical analysis of classification
error with respect to complex performance measures.
| null |
http://arxiv.org/abs/1806.00640v1
|
http://arxiv.org/pdf/1806.00640v1.pdf
|
ICML 2018 7
|
[
"Bowei Yan",
"Oluwasanmi Koyejo",
"Kai Zhong",
"Pradeep Ravikumar"
] |
[
"Binary Classification",
"Classification",
"General Classification"
] | 2018-06-02T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2232
|
http://proceedings.mlr.press/v80/yan18b/yan18b.pdf
|
binary-classification-with-karmic-threshold-1
| null |
[] |
https://paperswithcode.com/paper/squeeze-and-excitation-on-spatial-and
|
1806.00631
| null | null |
Squeeze-and-Excitation on Spatial and Temporal Deep Feature Space for Action Recognition
|
Spatial and temporal features are two key and complementary information for
human action recognition. In order to make full use of the intra-frame spatial
characteristics and inter-frame temporal relationships, we propose the
Squeeze-and-Excitation Long-term Recurrent Convolutional Networks (SE-LRCN) for
human action recognition. The Squeeze and Excitation operations are used to
implement the feature recalibration. In SE-LRCN, Squeeze-and-Excitation
ResNet-34 (SE-ResNet-34) network is adopted to extract spatial features to
enhance the dependencies and importance of feature channels of pixel
granularity. We also propose the Squeeze-and-Excitation Long Short-Term Memory
(SE-LSTM) network to model the temporal relationship, and to enhance the
dependencies and importance of feature channels of frame granularity. We
evaluate the proposed model on two challenging benchmarks, HMDB51 and UCF101,
and the proposed SE-LRCN achieves the competitive results with the
state-of-the-art.
| null |
http://arxiv.org/abs/1806.00631v2
|
http://arxiv.org/pdf/1806.00631v2.pdf
| null |
[
"Gaoyun An",
"Wen Zhou",
"Yuxuan Wu",
"Zhenxing Zheng",
"Yongwen Liu"
] |
[
"Action Recognition",
"Temporal Action Localization"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/daqn-deep-auto-encoder-and-q-network
|
1806.00630
| null | null |
DAQN: Deep Auto-encoder and Q-Network
|
The deep reinforcement learning method usually requires a large number of
training images and executing actions to obtain sufficient results. When it is
extended a real-task in the real environment with an actual robot, the method
will be required more training images due to complexities or noises of the
input images, and executing a lot of actions on the real robot also becomes a
serious problem. Therefore, we propose an extended deep reinforcement learning
method that is applied a generative model to initialize the network for
reducing the number of training trials. In this paper, we used a deep q-network
method as the deep reinforcement learning method and a deep auto-encoder as the
generative model. We conducted experiments on three different tasks: a
cart-pole game, an atari game, and a real-game with an actual robot. The
proposed method trained efficiently on all tasks than the previous method,
especially 2.5 times faster on a task with real environment images.
| null |
http://arxiv.org/abs/1806.00630v1
|
http://arxiv.org/pdf/1806.00630v1.pdf
| null |
[
"Daiki Kimura"
] |
[
"Deep Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-novel-framework-for-recurrent-neural
|
1806.00628
| null | null |
A Novel Framework for Recurrent Neural Networks with Enhancing Information Processing and Transmission between Units
|
This paper proposes a novel framework for recurrent neural networks (RNNs)
inspired by the human memory models in the field of cognitive neuroscience to
enhance information processing and transmission between adjacent RNNs' units.
The proposed framework for RNNs consists of three stages that is working
memory, forget, and long-term store. The first stage includes taking input data
into sensory memory and transferring it to working memory for preliminary
treatment. And the second stage mainly focuses on proactively forgetting the
secondary information rather than the primary in the working memory. And
finally, we get the long-term store normally using some kind of RNN's unit. Our
framework, which is generalized and simple, is evaluated on 6 datasets which
fall into 3 different tasks, corresponding to text classification, image
classification and language modelling. Experiments reveal that our framework
can obviously improve the performance of traditional recurrent neural networks.
And exploratory task shows the ability of our framework of correctly forgetting
the secondary information.
| null |
http://arxiv.org/abs/1806.00628v1
|
http://arxiv.org/pdf/1806.00628v1.pdf
| null |
[
"Xi Chen",
"Zhi-Hong Deng",
"Gehui Shen",
"Ting Huang"
] |
[
"General Classification",
"image-classification",
"Image Classification",
"Language Modelling",
"text-classification",
"Text Classification"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/recurrent-neural-network-based-semantic
|
1802.03238
| null | null |
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning
|
Sequence-to-sequence (Seq2seq) models have played an important role in the
recent success of various natural language processing methods, such as machine
translation, text summarization, and speech recognition. However, current
Seq2seq models have trouble preserving global latent information from a long
sequence of words. Variational autoencoder (VAE) alleviates this problem by
learning a continuous semantic space of the input sentence. However, it does
not solve the problem completely. In this paper, we propose a new recurrent
neural network (RNN)-based Seq2seq model, RNN semantic variational autoencoder
(RNN--SVAE), to better capture the global latent information of a sequence of
words. To reflect the meaning of words in a sentence properly, without regard
to its position within the sentence, we construct a document information vector
using the attention information between the final state of the encoder and
every prior hidden state. Then, the mean and standard deviation of the
continuous semantic space are learned by using this vector to take advantage of
the variational method. By using the document information vector to find the
semantic space of the sentence, it becomes possible to better capture the
global latent feature of the sentence. Experimental results of three natural
language tasks (i.e., language modeling, missing word imputation, paraphrase
identification) confirm that the proposed RNN--SVAE yields higher performance
than two benchmark models.
|
In this paper, we propose a new recurrent neural network (RNN)-based Seq2seq model, RNN semantic variational autoencoder (RNN--SVAE), to better capture the global latent information of a sequence of words.
|
http://arxiv.org/abs/1802.03238v2
|
http://arxiv.org/pdf/1802.03238v2.pdf
| null |
[
"Myeongjun Jang",
"Seungwan Seo",
"Pilsung Kang"
] |
[
"Imputation",
"Language Modeling",
"Language Modelling",
"Machine Translation",
"Paraphrase Identification",
"Sentence",
"speech-recognition",
"Speech Recognition",
"Text Summarization"
] | 2018-02-09T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Solana transaction not confirmed, your Solana wallet not showing balance, or you're trying to recover a lost Solana wallet, knowing where to get help is essential. That’s why the Solana customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Solana Customer Support Number +1-833-534-1729\r\nSolana operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Solana Transaction Not Confirmed\r\nOne of the most common concerns is when a Solana transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Solana Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Solana wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Solana Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Solana wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Solana Deposit Not Received\r\nIf someone has sent you Solana but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Solana deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Solana Transaction Stuck or Pending\r\nSometimes your Solana transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Solana Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Solana wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Solana Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Solana tech.\r\n\r\n24/7 Availability: Solana doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Solana Support and Wallet Issues\r\nQ1: Can Solana support help me recover stolen BTC?\r\nA: While Solana transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Solana transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Solana’s official number (Solana is decentralized), it connects you to trained professionals experienced in resolving all major Solana issues.\r\n\r\nFinal Thoughts\r\nSolana is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Solana transaction not confirmed, your Solana wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Solana customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Solana Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Solana Customer Service Number +1-833-534-1729",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Seq2Seq**, or **Sequence To Sequence**, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one [LSTM](https://paperswithcode.com/method/lstm), the *encoder*, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a context vector), and then to use another LSTM, the *decoder*, to extract the output sequence\r\nfrom that vector. The second LSTM is essentially a recurrent neural network language model except that it is conditioned on the input sequence.\r\n\r\n(Note that this page refers to the original seq2seq not general sequence-to-sequence models)",
"full_name": "Sequence to Sequence",
"introduced_year": 2000,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Sequence To Sequence Models",
"parent": null
},
"name": "Seq2Seq",
"source_title": "Sequence to Sequence Learning with Neural Networks",
"source_url": "http://arxiv.org/abs/1409.3215v3"
}
] |
https://paperswithcode.com/paper/beyond-trade-off-accelerate-fcn-based-face
|
1804.05197
| null | null |
Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy
|
Fully convolutional neural network (FCN) has been dominating the game of face
detection task for a few years with its congenital capability of
sliding-window-searching with shared kernels, which boiled down all the
redundant calculation, and most recent state-of-the-art methods such as
Faster-RCNN, SSD, YOLO and FPN use FCN as their backbone. So here comes one
question: Can we find a universal strategy to further accelerate FCN with
higher accuracy, so could accelerate all the recent FCN-based methods? To
analyze this, we decompose the face searching space into two orthogonal
directions, `scale' and `spatial'. Only a few coordinates in the space expanded
by the two base vectors indicate foreground. So if FCN could ignore most of the
other points, the searching space and false alarm should be significantly
boiled down. Based on this philosophy, a novel method named scale estimation
and spatial attention proposal ($S^2AP$) is proposed to pay attention to some
specific scales and valid locations in the image pyramid. Furthermore, we adopt
a masked-convolution operation based on the attention result to accelerate FCN
calculation. Experiments show that FCN-based method RPN can be accelerated by
about $4\times$ with the help of $S^2AP$ and masked-FCN and at the same time it
can also achieve the state-of-the-art on FDDB, AFW and MALF face detection
benchmarks as well.
| null |
http://arxiv.org/abs/1804.05197v2
|
http://arxiv.org/pdf/1804.05197v2.pdf
|
CVPR 2018 6
|
[
"Guanglu Song",
"Yu Liu",
"Ming Jiang",
"Yujie Wang",
"Junjie Yan",
"Biao Leng"
] |
[
"Face Detection",
"Philosophy",
"valid"
] | 2018-04-14T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Song_Beyond_Trade-Off_Accelerate_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Song_Beyond_Trade-Off_Accelerate_CVPR_2018_paper.pdf
|
beyond-trade-off-accelerate-fcn-based-face-1
| null |
[
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Non Maximum Suppression** is a computer vision method that selects a single entity out of many overlapping entities (for example bounding boxes in object detection). The criteria is usually discarding entities that are below a given probability bound. With remaining entities we repeatedly pick the entity with the highest probability, output that as the prediction, and discard any remaining box where a $\\text{IoU} \\geq 0.5$ with the box output in the previous step.\r\n\r\nImage Credit: [Martin Kersner](https://github.com/martinkersner/non-maximum-suppression-cpp)",
"full_name": "Non Maximum Suppression",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Proposal Filtering",
"parent": null
},
"name": "Non Maximum Suppression",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/amdegroot/ssd.pytorch/blob/5b0b77faa955c1917b0c710d770739ba8fbff9b7/ssd.py#L10",
"description": "**SSD** is a single-stage object detection method that discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. \r\n\r\nThe fundamental improvement in speed comes from eliminating bounding box proposals and the subsequent pixel or feature resampling stage. Improvements over competing single-stage methods include using a small convolutional filter to predict object categories and offsets in bounding box locations, using separate predictors (filters) for different aspect ratio detections, and applying these filters to multiple feature maps from the later stages of a network in order to perform detection at multiple scales.",
"full_name": "SSD",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Object Detection Models** are architectures used to perform the task of object detection. Below you can find a continuously updating list of object detection models.",
"name": "Object Detection Models",
"parent": null
},
"name": "SSD",
"source_title": "SSD: Single Shot MultiBox Detector",
"source_url": "http://arxiv.org/abs/1512.02325v5"
},
{
"code_snippet_url": "",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "A **Region Proposal Network**, or **RPN**, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals. RPN and algorithms like [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) can be merged into a single network by sharing their convolutional features - using the recently popular terminology of neural networks with attention mechanisms, the RPN component tells the unified network where to look.\r\n\r\nRPNs are designed to efficiently predict region proposals with a wide range of scales and aspect ratios. RPNs use anchor boxes that serve as references at multiple scales and aspect ratios. The scheme can be thought of as a pyramid of regression references, which avoids enumerating images or filters of multiple scales or aspect ratios.",
"full_name": "Region Proposal Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Region Proposal",
"parent": null
},
"name": "RPN",
"source_title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
"source_url": "http://arxiv.org/abs/1506.01497v3"
},
{
"code_snippet_url": "https://github.com/facebookresearch/Detectron/blob/8170b25b425967f8f1c7d715bea3c5b8d9536cd8/detectron/modeling/FPN.py#L117",
"description": "A **Feature Pyramid Network**, or **FPN**, is a feature extractor that takes a single-scale image of an arbitrary size as input, and outputs proportionally sized feature maps at multiple levels, in a fully convolutional fashion. This process is independent of the backbone convolutional architectures. It therefore acts as a generic solution for building feature pyramids inside deep convolutional networks to be used in tasks like object detection.\r\n\r\nThe construction of the pyramid involves a bottom-up pathway and a top-down pathway.\r\n\r\nThe bottom-up pathway is the feedforward computation of the backbone ConvNet, which computes a feature hierarchy consisting of feature maps at several scales with a scaling step of 2. For the feature\r\npyramid, one pyramid level is defined for each stage. The output of the last layer of each stage is used as a reference set of feature maps. For [ResNets](https://paperswithcode.com/method/resnet) we use the feature activations output by each stage’s last [residual block](https://paperswithcode.com/method/residual-block). \r\n\r\nThe top-down pathway hallucinates higher resolution features by upsampling spatially coarser, but semantically stronger, feature maps from higher pyramid levels. These features are then enhanced with features from the bottom-up pathway via lateral connections. Each lateral connection merges feature maps of the same spatial size from the bottom-up pathway and the top-down pathway. The bottom-up feature map is of lower-level semantics, but its activations are more accurately localized as it was subsampled fewer times.",
"full_name": "Feature Pyramid Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Feature Extractors** for object detection are modules used to construct features that can be used for detecting objects. They address issues such as the need to detect multiple-sized objects in an image (and the need to have representations that are suitable for the different scales).",
"name": "Feature Extractors",
"parent": null
},
"name": "FPN",
"source_title": "Feature Pyramid Networks for Object Detection",
"source_url": "http://arxiv.org/abs/1612.03144v2"
},
{
"code_snippet_url": "https://github.com/Jackey9797/FCN",
"description": "**Fully Convolutional Networks**, or **FCNs**, are an architecture used mainly for semantic segmentation. They employ solely locally connected layers, such as [convolution](https://paperswithcode.com/method/convolution), pooling and upsampling. Avoiding the use of dense layers means less parameters (making the networks faster to train). It also means an FCN can work for variable image sizes given all connections are local.\r\n\r\nThe network consists of a downsampling path, used to extract and interpret the context, and an upsampling path, which allows for localization. \r\n\r\nFCNs also employ skip connections to recover the fine-grained spatial information lost in the downsampling path.",
"full_name": "Fully Convolutional Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Semantic Segmentation Models** are a class of methods that address the task of semantically segmenting an image into different object classes. Below you can find a continuously updating list of semantic segmentation models. ",
"name": "Semantic Segmentation Models",
"parent": null
},
"name": "FCN",
"source_title": "Fully Convolutional Networks for Semantic Segmentation",
"source_url": "http://arxiv.org/abs/1605.06211v1"
}
] |
https://paperswithcode.com/paper/ap18-olr-challenge-three-tasks-and-their
|
1806.00616
| null | null |
AP18-OLR Challenge: Three Tasks and Their Baselines
|
The third oriental language recognition (OLR) challenge AP18-OLR is
introduced in this paper, including the data profile, the tasks and the
evaluation principles. Following the events in the last two years, namely
AP16-OLR and AP17-OLR, the challenge this year focuses on more challenging
tasks, including (1) short-duration utterances, (2) confusing languages, and
(3) open-set recognition. The same as the previous events, the data of AP18-OLR
is also provided by SpeechOcean and the NSFC M2ASR project. Baselines based on
both the i-vector model and neural networks are constructed for the
participants' reference. We report the baseline results on the three tasks and
demonstrate that the three tasks are truly challenging. All the data is free
for participants, and the Kaldi recipes for the baselines have been published
online.
|
The third oriental language recognition (OLR) challenge AP18-OLR is introduced in this paper, including the data profile, the tasks and the evaluation principles.
|
http://arxiv.org/abs/1806.00616v1
|
http://arxiv.org/pdf/1806.00616v1.pdf
| null |
[
"Zhiyuan Tang",
"Dong Wang",
"Qing Chen"
] |
[
"Open Set Learning"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multiplex-communities-and-the-emergence-of
|
1806.00615
| null | null |
Multiplex Communities and the Emergence of International Conflict
|
Advances in community detection reveal new insights into multiplex and multilayer networks. Less work, however, investigates the relationship between these communities and outcomes in social systems. We leverage these advances to shed light on the relationship between the cooperative mesostructure of the international system and the onset of interstate conflict. We detect communities based upon weaker signals of affinity expressed in United Nations votes and speeches, as well as stronger signals observed across multiple layers of bilateral cooperation. Communities of diplomatic affinity display an expected negative relationship with conflict onset. Ties in communities based upon observed cooperation, however, display no effect under a standard model specification and a positive relationship with conflict under an alternative specification. These results align with some extant hypotheses but also point to a paucity in our understanding of the relationship between community structure and behavioral outcomes in networks.
| null |
https://arxiv.org/abs/1806.00615v2
|
https://arxiv.org/pdf/1806.00615v2.pdf
| null |
[
"Caleb Pomeroy",
"Niheer Dasandi",
"Slava Jankin Mikhaylov"
] |
[
"Community Detection"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/accounting-for-the-neglected-dimensions-of-ai
|
1806.00610
| null | null |
Between Progress and Potential Impact of AI: the Neglected Dimensions
|
We reframe the analysis of progress in AI by incorporating into an overall framework both the task performance of a system, and the time and resource costs incurred in the development and deployment of the system. These costs include: data, expert knowledge, human oversight, software resources, computing cycles, hardware and network facilities, and (what kind of) time. These costs are distributed over the life cycle of the system, and may place differing demands on different developers and users. The multidimensional performance and cost space we present can be collapsed to a single utility metric that measures the value of the system for different stakeholders. Even without a single utility function, AI advances can be generically assessed by whether they expand the Pareto surface. We label these types of costs as neglected dimensions of AI progress, and explore them using four case studies: Alpha* (Go, Chess, and other board games), ALE (Atari games), ImageNet (Image classification) and Virtual Personal Assistants (Siri, Alexa, Cortana, and Google Assistant). This broader model of progress in AI will lead to novel ways of estimating the potential societal use and impact of an AI system, and the establishment of milestones for future progress.
| null |
https://arxiv.org/abs/1806.00610v2
|
https://arxiv.org/pdf/1806.00610v2.pdf
| null |
[
"Fernando Martínez-Plumed",
"Shahar Avin",
"Miles Brundage",
"Allan Dafoe",
"Sean Ó hÉigeartaigh",
"José Hernández-Orallo"
] |
[
"Atari Games",
"Board Games",
"image-classification",
"Image Classification"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/gamepad-a-learning-environment-for-theorem
|
1806.00608
| null |
r1xwKoR9Y7
|
GamePad: A Learning Environment for Theorem Proving
|
In this paper, we introduce a system called GamePad that can be used to
explore the application of machine learning methods to theorem proving in the
Coq proof assistant. Interactive theorem provers such as Coq enable users to
construct machine-checkable proofs in a step-by-step manner. Hence, they
provide an opportunity to explore theorem proving with human supervision. We
use GamePad to synthesize proofs for a simple algebraic rewrite problem and
train baseline models for a formalization of the Feit-Thompson theorem. We
address position evaluation (i.e., predict the number of proof steps left) and
tactic prediction (i.e., predict the next proof step) tasks, which arise
naturally in tactic-based theorem proving.
|
In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant.
|
http://arxiv.org/abs/1806.00608v2
|
http://arxiv.org/pdf/1806.00608v2.pdf
|
ICLR 2019 5
|
[
"Daniel Huang",
"Prafulla Dhariwal",
"Dawn Song",
"Ilya Sutskever"
] |
[
"Automated Theorem Proving",
"Position"
] | 2018-06-02T00:00:00 |
https://openreview.net/forum?id=r1xwKoR9Y7
|
https://openreview.net/pdf?id=r1xwKoR9Y7
|
gamepad-a-learning-environment-for-theorem-1
| null |
[] |
https://paperswithcode.com/paper/maximum-principle-based-algorithms-for-deep
|
1710.09513
| null | null |
Maximum Principle Based Algorithms for Deep Learning
|
The continuous dynamical system approach to deep learning is explored in
order to devise alternative frameworks for training algorithms. Training is
recast as a control problem and this allows us to formulate necessary
optimality conditions in continuous time using the Pontryagin's maximum
principle (PMP). A modification of the method of successive approximations is
then used to solve the PMP, giving rise to an alternative training algorithm
for deep learning. This approach has the advantage that rigorous error
estimates and convergence results can be established. We also show that it may
avoid some pitfalls of gradient-based methods, such as slow convergence on flat
landscapes near saddle points. Furthermore, we demonstrate that it obtains
favorable initial convergence rate per-iteration, provided Hamiltonian
maximization can be efficiently carried out - a step which is still in need of
improvement. Overall, the approach opens up new avenues to attack problems
associated with deep learning, such as trapping in slow manifolds and
inapplicability of gradient-based methods for discrete trainable variables.
|
The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms.
|
http://arxiv.org/abs/1710.09513v4
|
http://arxiv.org/pdf/1710.09513v4.pdf
| null |
[
"Qianxiao Li",
"Long Chen",
"Cheng Tai",
"Weinan E"
] |
[
"Deep Learning"
] | 2017-10-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/an-optimal-control-approach-to-deep-learning
|
1803.01299
| null | null |
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
|
Deep learning is formulated as a discrete-time optimal control problem. This
allows one to characterize necessary conditions for optimality and develop
training algorithms that do not rely on gradients with respect to the trainable
parameters. In particular, we introduce the discrete-time method of successive
approximations (MSA), which is based on the Pontryagin's maximum principle, for
training neural networks. A rigorous error estimate for the discrete MSA is
obtained, which sheds light on its dynamics and the means to stabilize the
algorithm. The developed methods are applied to train, in a rather principled
way, neural networks with weights that are constrained to take values in a
discrete set. We obtain competitive performance and interestingly, very sparse
weights in the case of ternary networks, which may be useful in model
deployment in low-memory devices.
|
Deep learning is formulated as a discrete-time optimal control problem.
|
http://arxiv.org/abs/1803.01299v2
|
http://arxiv.org/pdf/1803.01299v2.pdf
|
ICML 2018 7
|
[
"Qianxiao Li",
"Shuji Hao"
] |
[] | 2018-03-04T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1992
|
http://proceedings.mlr.press/v80/li18b/li18b.pdf
|
an-optimal-control-approach-to-deep-learning-1
| null |
[] |
https://paperswithcode.com/paper/estimating-activity-cycles-with-probabilistic-1
|
1712.08240
| null | null |
Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data
|
Debate over the existence of branches in the stellar activity-rotation
diagrams continues. Application of modern time series analysis tools to study
the mean cycle periods in chromospheric activity index is lacking. We develop
such models, based on Gaussian processes, for one-dimensional time series and
apply it to the extended Mount Wilson Ca H&K sample. Our main aim is to study
how the previously commonly used assumption of strict harmonicity of the
stellar cycles as well as handling of the linear trends affects the results. We
introduce three methods of different complexity, starting with the simple
Bayesian harmonic model and followed by Gaussian Process models with periodic
and quasi-periodic covariance functions. We confirm the existence of two
populations in the activity-period diagram. We find only one significant trend
in the inactive population, namely that the cycle periods get shorter with
increasing rotation. This is in contrast with earlier studies, that postulate
the existence of trends in both of the populations. In terms of rotation to
cycle period ratio, our data is consistent with only two activity branches such
that the active branch merges together with the transitional one. The retrieved
stellar cycles are uniformly distributed over the R'HK activity index,
indicating that the operation of stellar large-scale dynamos carries smoothly
over the Vaughan-Preston gap. At around the solar activity index, however,
indications of a disruption in the cyclic dynamo action are seen. Our study
shows that stellar cycle estimates depend significantly on the model applied.
Such model-dependent aspects include the improper treatment of linear trends,
while the assumption of strict harmonicity can result in the appearance of
double cyclicities that seem more likely to be explained by the
quasi-periodicity of the cycles.
| null |
http://arxiv.org/abs/1712.08240v4
|
http://arxiv.org/pdf/1712.08240v4.pdf
| null |
[
"N. Olspert",
"J. Lehtinen",
"M. J. Käpylä",
"J. Pelt",
"A. Grigorievskiy"
] |
[
"Gaussian Processes",
"Time Series",
"Time Series Analysis"
] | 2017-12-21T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.\r\n\r\nImage Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams",
"full_name": "Gaussian Process",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "Gaussian Process",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/internal-model-from-observations-for-reward
|
1806.01267
| null | null |
Internal Model from Observations for Reward Shaping
|
Reinforcement learning methods require careful design involving a reward
function to obtain the desired action policy for a given task. In the absence
of hand-crafted reward functions, prior work on the topic has proposed several
methods for reward estimation by using expert state trajectories and action
pairs. However, there are cases where complete or good action information
cannot be obtained from expert demonstrations. We propose a novel reinforcement
learning method in which the agent learns an internal model of observation on
the basis of expert-demonstrated state trajectories to estimate rewards without
completely learning the dynamics of the external environment from state-action
pairs. The internal model is obtained in the form of a predictive model for the
given expert state distribution. During reinforcement learning, the agent
predicts the reward as a function of the difference between the actual state
and the state predicted by the internal model. We conducted multiple
experiments in environments of varying complexity, including the Super Mario
Bros and Flappy Bird games. We show our method successfully trains good
policies directly from expert game-play videos.
| null |
http://arxiv.org/abs/1806.01267v4
|
http://arxiv.org/pdf/1806.01267v4.pdf
| null |
[
"Daiki Kimura",
"Subhajit Chaudhury",
"Ryuki Tachibana",
"Sakyasingha Dasgupta"
] |
[
"model",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/boxnet-deep-learning-based-biomedical-image
|
1806.00593
| null | null |
BoxNet: Deep Learning Based Biomedical Image Segmentation Using Boxes Only Annotation
|
In recent years, deep learning (DL) methods have become powerful tools for
biomedical image segmentation. However, high annotation efforts and costs are
commonly needed to acquire sufficient biomedical training data for DL models.
To alleviate the burden of manual annotation, in this paper, we propose a new
weakly supervised DL approach for biomedical image segmentation using boxes
only annotation. First, we develop a method to combine graph search (GS) and DL
to generate fine object masks from box annotation, in which DL uses box
annotation to compute a rough segmentation for GS and then GS is applied to
locate the optimal object boundaries. During the mask generation process, we
carefully utilize information from box annotation to filter out potential
errors, and then use the generated masks to train an accurate DL segmentation
network. Extensive experiments on gland segmentation in histology images, lymph
node segmentation in ultrasound images, and fungus segmentation in electron
microscopy images show that our approach attains superior performance over the
best known state-of-the-art weakly supervised DL method and is able to achieve
(1) nearly the same accuracy compared to fully supervised DL methods with far
less annotation effort, (2) significantly better results with similar
annotation time, and (3) robust performance in various applications.
| null |
http://arxiv.org/abs/1806.00593v1
|
http://arxiv.org/pdf/1806.00593v1.pdf
| null |
[
"Lin Yang",
"Yizhe Zhang",
"Zhuo Zhao",
"Hao Zheng",
"Peixian Liang",
"Michael T. C. Ying",
"Anil T. Ahuja",
"Danny Z. Chen"
] |
[
"Image Segmentation",
"Segmentation",
"Semantic Segmentation"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/does-the-brain-represent-words-an-evaluation
|
1806.00591
| null | null |
Does the brain represent words? An evaluation of brain decoding studies of language understanding
|
Language decoding studies have identified word representations which can be
used to predict brain activity in response to novel words and sentences
(Anderson et al., 2016; Pereira et al., 2018). The unspoken assumption of these
studies is that, during processing, linguistic information is transformed into
some shared semantic space, and those semantic representations are then used
for a variety of linguistic and non-linguistic tasks. We claim that current
studies vastly underdetermine the content of these representations, the
algorithms which the brain deploys to produce and consume them, and the
computational tasks which they are designed to solve. We illustrate this
indeterminacy with an extension of the sentence-decoding experiment of Pereira
et al. (2018), showing how standard evaluations fail to distinguish between
language processing models which deploy different mechanisms and which are
optimized to solve very different tasks. We conclude by suggesting changes to
the brain decoding paradigm which can support stronger claims of neural
representation.
|
Language decoding studies have identified word representations which can be used to predict brain activity in response to novel words and sentences (Anderson et al., 2016; Pereira et al., 2018).
|
http://arxiv.org/abs/1806.00591v1
|
http://arxiv.org/pdf/1806.00591v1.pdf
| null |
[
"Jon Gauthier",
"Anna Ivanova"
] |
[
"Brain Decoding",
"Sentence"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-entropy-for-policy-gradient-with
|
1806.00589
| null | null |
Efficient Entropy for Policy Gradient with Multidimensional Action Space
|
In recent years, deep reinforcement learning has been shown to be adept at
solving sequential decision processes with high-dimensional state spaces such
as in the Atari games. Many reinforcement learning problems, however, involve
high-dimensional discrete action spaces as well as high-dimensional state
spaces. This paper considers entropy bonus, which is used to encourage
exploration in policy gradient. In the case of high-dimensional action spaces,
calculating the entropy and its gradient requires enumerating all the actions
in the action space and running forward and backpropagation for each action,
which may be computationally infeasible. We develop several novel unbiased
estimators for the entropy bonus and its gradient. We apply these estimators to
several models for the parameterized policies, including Independent Sampling,
CommNet, Autoregressive with Modified MDP, and Autoregressive with LSTM.
Finally, we test our algorithms on two environments: a multi-hunter
multi-rabbit grid game and a multi-agent multi-arm bandit problem. The results
show that our entropy estimators substantially improve performance with
marginal additional computational cost.
| null |
http://arxiv.org/abs/1806.00589v1
|
http://arxiv.org/pdf/1806.00589v1.pdf
| null |
[
"Yiming Zhang",
"Quan Ho Vuong",
"Kenny Song",
"Xiao-Yue Gong",
"Keith W. Ross"
] |
[
"Atari Games",
"Deep Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-02T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/fast-locality-sensitive-hashing-for-beam
|
1806.00588
| null | null |
Fast Locality Sensitive Hashing for Beam Search on GPU
|
We present a GPU-based Locality Sensitive Hashing (LSH) algorithm to speed up
beam search for sequence models. We utilize the winner-take-all (WTA) hash,
which is based on relative ranking order of hidden dimensions and thus
resilient to perturbations in numerical values. Our algorithm is designed by
fully considering the underling architecture of CUDA-enabled GPUs
(Algorithm/Architecture Co-design): 1) A parallel Cuckoo hash table is applied
for LSH code lookup (guaranteed O(1) lookup time); 2) Candidate lists are
shared across beams to maximize the parallelism; 3) Top frequent words are
merged into candidate lists to improve performance. Experiments on 4
large-scale neural machine translation models demonstrate that our algorithm
can achieve up to 4x speedup on softmax module, and 2x overall speedup without
hurting BLEU on GPU.
| null |
http://arxiv.org/abs/1806.00588v1
|
http://arxiv.org/pdf/1806.00588v1.pdf
| null |
[
"Xing Shi",
"Shizhen Xu",
"Kevin Knight"
] |
[
"GPU",
"Machine Translation",
"Translation"
] | 2018-06-02T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/cytonmt-an-efficient-neural-machine
|
1802.07170
| null | null |
CytonMT: an Efficient Neural Machine Translation Open-source Toolkit Implemented in C++
|
This paper presents an open-source neural machine translation toolkit named
CytonMT (https://github.com/arthurxlw/cytonMt). The toolkit is built from
scratch only using C++ and NVIDIA's GPU-accelerated libraries. The toolkit
features training efficiency, code simplicity and translation quality.
Benchmarks show that CytonMT accelerates the training speed by 64.5% to 110.8%
on neural networks of various sizes, and achieves competitive translation
quality.
|
This paper presents an open-source neural machine translation toolkit named CytonMT (https://github. com/arthurxlw/cytonMt).
|
http://arxiv.org/abs/1802.07170v2
|
http://arxiv.org/pdf/1802.07170v2.pdf
|
EMNLP 2018 11
|
[
"Xiaolin Wang",
"Masao Utiyama",
"Eiichiro Sumita"
] |
[
"GPU",
"Machine Translation",
"Translation"
] | 2018-02-17T00:00:00 |
https://aclanthology.org/D18-2023
|
https://aclanthology.org/D18-2023.pdf
|
cytonmt-an-efficient-neural-machine-1
| null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/monocular-depth-estimation-with-augmented
|
1806.00585
| null | null |
Monocular Depth Estimation with Augmented Ordinal Depth Relationships
|
Most existing algorithms for depth estimation from single monocular images need large quantities of metric groundtruth depths for supervised learning. We show that relative depth can be an informative cue for metric depth estimation and can be easily obtained from vast stereo videos. Acquiring metric depths from stereo videos is sometimes impracticable due to the absence of camera parameters. In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm. We introduce a new "Relative Depth in Stereo" (RDIS) dataset densely labelled with relative depths. We first pretrain a ResNet model on our RDIS dataset. Then we finetune the model on RGB-D datasets with metric ground-truth depths. During our finetuning, we formulate depth estimation as a classification task. This re-formulation scheme enables us to obtain the confidence of a depth prediction in the form of probability distribution. With this confidence, we propose an information gain loss to make use of the predictions that are close to ground-truth. We evaluate our approach on both indoor and outdoor benchmark RGB-D datasets and achieve state-of-the-art performance.
| null |
https://arxiv.org/abs/1806.00585v2
|
https://arxiv.org/pdf/1806.00585v2.pdf
| null |
[
"Yuanzhouhan Cao",
"Tianqi Zhao",
"Ke Xian",
"Chunhua Shen",
"Zhiguo Cao",
"Shugong Xu"
] |
[
"Depth Estimation",
"Depth Prediction",
"Monocular Depth Estimation",
"Stereo Matching",
"Stereo Matching Hand"
] | 2018-06-02T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**Average Pooling** is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs. It extracts features more smoothly than [Max Pooling](https://paperswithcode.com/method/max-pooling), whereas max pooling extracts more pronounced features like edges.\r\n\r\nImage Source: [here](https://www.researchgate.net/figure/Illustration-of-Max-Pooling-and-Average-Pooling-Figure-2-above-shows-an-example-of-max_fig2_333593451)",
"full_name": "Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Average Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L75",
"description": "A **Bottleneck Residual Block** is a variant of the [residual block](https://paperswithcode.com/method/residual-block) that utilises 1x1 convolutions to create a bottleneck. The use of a bottleneck reduces the number of parameters and matrix multiplications. The idea is to make residual blocks as thin as possible to increase depth and have less parameters. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture, and are used as part of deeper ResNets such as ResNet-50 and ResNet-101.",
"full_name": "Bottleneck Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Bottleneck Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/baa592b215804927e28638f6a7f3318cbc411d49/torchvision/models/resnet.py#L157",
"description": "**Global Average Pooling** is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding category of the classification task in the last mlpconv layer. Instead of adding fully connected layers on top of the feature maps, we take the average of each feature map, and the resulting vector is fed directly into the [softmax](https://paperswithcode.com/method/softmax) layer. \r\n\r\nOne advantage of global [average pooling](https://paperswithcode.com/method/average-pooling) over the fully connected layers is that it is more native to the [convolution](https://paperswithcode.com/method/convolution) structure by enforcing correspondences between feature maps and categories. Thus the feature maps can be easily interpreted as categories confidence maps. Another advantage is that there is no parameter to optimize in the global average pooling thus overfitting is avoided at this layer. Furthermore, global average pooling sums out the spatial information, thus it is more robust to spatial translations of the input.",
"full_name": "Global Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Global Average Pooling",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L35",
"description": "**Residual Blocks** are skip-connection blocks that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture.\r\n \r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$. The $\\mathcal{F}({x})$ acts like a residual, hence the name 'residual block'.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers. Having skip connections allows the network to more easily learn identity-like mappings.\r\n\r\nNote that in practice, [Bottleneck Residual Blocks](https://paperswithcode.com/method/bottleneck-residual-block) are used for deeper ResNets, such as ResNet-50 and ResNet-101, as these bottleneck blocks are less computationally intensive.",
"full_name": "Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/0adb5843766092fba584791af76383125fd0d01c/torch/nn/init.py#L389",
"description": "**Kaiming Initialization**, or **He Initialization**, is an initialization method for neural networks that takes into account the non-linearity of activation functions, such as [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nA proper initialization method should avoid reducing or magnifying the magnitudes of input signals exponentially. Using a derivation they work out that the condition to stop this happening is:\r\n\r\n$$\\frac{1}{2}n\\_{l}\\text{Var}\\left[w\\_{l}\\right] = 1 $$\r\n\r\nThis implies an initialization scheme of:\r\n\r\n$$ w\\_{l} \\sim \\mathcal{N}\\left(0, 2/n\\_{l}\\right)$$\r\n\r\nThat is, a zero-centered Gaussian with standard deviation of $\\sqrt{2/{n}\\_{l}}$ (variance shown in equation above). Biases are initialized at $0$.",
"full_name": "Kaiming Initialization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Initialization** methods are used to initialize the weights in a neural network. Below can you find a continuously updating list of initialization methods.",
"name": "Initialization",
"parent": null
},
"name": "Kaiming Initialization",
"source_title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
"source_url": "http://arxiv.org/abs/1502.01852v1"
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Bitcoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Bitcoin transaction not confirmed, your Bitcoin wallet not showing balance, or you're trying to recover a lost Bitcoin wallet, knowing where to get help is essential. That’s why the Bitcoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Bitcoin Customer Support Number +1-833-534-1729\r\nBitcoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Bitcoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Bitcoin transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Bitcoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Bitcoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Bitcoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Bitcoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Bitcoin Deposit Not Received\r\nIf someone has sent you Bitcoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Bitcoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Bitcoin Transaction Stuck or Pending\r\nSometimes your Bitcoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Bitcoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Bitcoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Bitcoin Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Bitcoin tech.\r\n\r\n24/7 Availability: Bitcoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Bitcoin Support and Wallet Issues\r\nQ1: Can Bitcoin support help me recover stolen BTC?\r\nA: While Bitcoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Bitcoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Bitcoin’s official number (Bitcoin is decentralized), it connects you to trained professionals experienced in resolving all major Bitcoin issues.\r\n\r\nFinal Thoughts\r\nBitcoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Bitcoin transaction not confirmed, your Bitcoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Bitcoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Bitcoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "Bitcoin Customer Service Number +1-833-534-1729",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
}
] |
https://paperswithcode.com/paper/improving-sparse-representation-based
|
1607.01059
| null | null |
Improving Sparse Representation-Based Classification Using Local Principal Component Analysis
|
Sparse representation-based classification (SRC), proposed by Wright et al.,
seeks the sparsest decomposition of a test sample over the dictionary of
training samples, with classification to the most-contributing class. Because
it assumes test samples can be written as linear combinations of their
same-class training samples, the success of SRC depends on the size and
representativeness of the training set. Our proposed classification algorithm
enlarges the training set by using local principal component analysis to
approximate the basis vectors of the tangent hyperplane of the class manifold
at each training sample. The dictionary in SRC is replaced by a local
dictionary that adapts to the test sample and includes training samples and
their corresponding tangent basis vectors. We use a synthetic data set and
three face databases to demonstrate that this method can achieve higher
classification accuracy than SRC in cases of sparse sampling, nonlinear class
manifolds, and stringent dimension reduction.
| null |
http://arxiv.org/abs/1607.01059v6
|
http://arxiv.org/pdf/1607.01059v6.pdf
| null |
[
"Chelsea Weaver",
"Naoki Saito"
] |
[
"Classification",
"Dimensionality Reduction",
"General Classification",
"Sparse Representation-based Classification"
] | 2016-07-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/federated-learning-with-non-iid-data
|
1806.00582
| null | null |
Federated Learning with Non-IID Data
|
Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits. In this work, we focus on the statistical challenge of federated learning when local data is non-IID. We first show that the accuracy of federated learning reduces significantly, by up to 55% for neural networks trained for highly skewed non-IID data, where each client device trains only on a single class of data. We further show that this accuracy reduction can be explained by the weight divergence, which can be quantified by the earth mover's distance (EMD) between the distribution over classes on each device and the population distribution. As a solution, we propose a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices. Experiments show that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.
|
Experiments show that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.
|
https://arxiv.org/abs/1806.00582v2
|
https://arxiv.org/pdf/1806.00582v2.pdf
| null |
[
"Yue Zhao",
"Meng Li",
"Liangzhen Lai",
"Naveen Suda",
"Damon Civin",
"Vikas Chandra"
] |
[
"Federated Learning"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/detecting-adversarial-examples-via-key-based
|
1806.00580
| null | null |
Detecting Adversarial Examples via Key-based Network
|
Though deep neural networks have achieved state-of-the-art performance in
visual classification, recent studies have shown that they are all vulnerable
to the attack of adversarial examples. Small and often imperceptible
perturbations to the input images are sufficient to fool the most powerful deep
neural networks. Various defense methods have been proposed to address this
issue. However, they either require knowledge on the process of generating
adversarial examples, or are not robust against new attacks specifically
designed to penetrate the existing defense. In this work, we introduce
key-based network, a new detection-based defense mechanism to distinguish
adversarial examples from normal ones based on error correcting output codes,
using the binary code vectors produced by multiple binary classifiers applied
to randomly chosen label-sets as signatures to match normal images and reject
adversarial examples. In contrast to existing defense methods, the proposed
method does not require knowledge of the process for generating adversarial
examples and can be applied to defend against different types of attacks. For
the practical black-box and gray-box scenarios, where the attacker does not
know the encoding scheme, we show empirically that key-based network can
effectively detect adversarial examples generated by several state-of-the-art
attacks.
| null |
http://arxiv.org/abs/1806.00580v1
|
http://arxiv.org/pdf/1806.00580v1.pdf
| null |
[
"Pinlong Zhao",
"Zhouyu Fu",
"Ou wu",
"QinGhua Hu",
"Jun Wang"
] |
[] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/scan-sliding-convolutional-attention-network
|
1806.00578
| null | null |
SCAN: Sliding Convolutional Attention Network for Scene Text Recognition
|
Scene text recognition has drawn great attentions in the community of
computer vision and artificial intelligence due to its challenges and wide
applications. State-of-the-art recurrent neural networks (RNN) based models map
an input sequence to a variable length output sequence, but are usually applied
in a black box manner and lack of transparency for further improvement, and the
maintaining of the entire past hidden states prevents parallel computation in a
sequence. In this paper, we investigate the intrinsic characteristics of text
recognition, and inspired by human cognition mechanisms in reading texts, we
propose a scene text recognition method with sliding convolutional attention
network (SCAN). Similar to the eye movement during reading, the process of SCAN
can be viewed as an alternation between saccades and visual fixations. Compared
to the previous recurrent models, computations over all elements of SCAN can be
fully parallelized during training. Experimental results on several challenging
benchmarks, including the IIIT5k, SVT and ICDAR 2003/2013 datasets, demonstrate
the superiority of SCAN over state-of-the-art methods in terms of both the
model interpretability and performance.
| null |
http://arxiv.org/abs/1806.00578v1
|
http://arxiv.org/pdf/1806.00578v1.pdf
| null |
[
"Yi-Chao Wu",
"Fei Yin",
"Xu-Yao Zhang",
"Li Liu",
"Cheng-Lin Liu"
] |
[
"Scene Text Recognition"
] | 2018-06-02T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "Interpretability",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images for downstream tasks such as classification and object detection. The most popular subcategory are convolutional neural networks. Below you can find a continuously updated list of image models.",
"name": "Image Models",
"parent": null
},
"name": "Interpretability",
"source_title": "CAM: Causal additive models, high-dimensional order search and penalized regression",
"source_url": "http://arxiv.org/abs/1310.1533v2"
}
] |
https://paperswithcode.com/paper/coarse-to-fine-salient-object-detection-with
|
1805.07936
| null | null |
Coarse-to-Fine Salient Object Detection with Low-Rank Matrix Recovery
|
Low-Rank Matrix Recovery (LRMR) has recently been applied to saliency detection by decomposing image features into a low-rank component associated with background and a sparse component associated with visual salient regions. Despite its great potential, existing LRMR-based saliency detection methods seldom consider the inter-relationship among elements within these two components, thus are prone to generating scattered or incomplete saliency maps. In this paper, we introduce a novel and efficient LRMR-based saliency detection model under a coarse-to-fine framework to circumvent this limitation. First, we roughly measure the saliency of image regions with a baseline LRMR model that integrates a $\ell_1$-norm sparsity constraint and a Laplacian regularization smooth term. Given samples from the coarse saliency map, we then learn a projection that maps image features to refined saliency values, to significantly sharpen the object boundaries and to preserve the object entirety. We evaluate our framework against existing LRMR-based methods on three benchmark datasets. Experimental results validate the superiority of our method as well as the effectiveness of our suggested coarse-to-fine framework, especially for images containing multiple objects.
| null |
https://arxiv.org/abs/1805.07936v4
|
https://arxiv.org/pdf/1805.07936v4.pdf
| null |
[
"Qi Zheng",
"Shujian Yu",
"Xinge You",
"Qinmu Peng"
] |
[
"object-detection",
"Object Detection",
"RGB Salient Object Detection",
"Saliency Detection",
"Salient Object Detection"
] | 2018-05-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/autoencoders-learn-generative-linear-models
|
1806.00572
| null | null |
Autoencoders Learn Generative Linear Models
|
We provide a series of results for unsupervised learning with autoencoders.
Specifically, we study shallow two-layer autoencoder architectures with shared
weights. We focus on three generative models for data that are common in
statistical machine learning: (i) the mixture-of-gaussians model, (ii) the
sparse coding model, and (iii) the sparsity model with non-negative
coefficients. For each of these models, we prove that under suitable choices of
hyperparameters, architectures, and initialization, autoencoders learned by
gradient descent can successfully recover the parameters of the corresponding
model. To our knowledge, this is the first result that rigorously studies the
dynamics of gradient descent for weight-sharing autoencoders. Our analysis can
be viewed as theoretical evidence that shallow autoencoder modules indeed can
be used as feature learning mechanisms for a variety of data models, and may
shed insight on how to train larger stacked architectures with autoencoders as
basic building blocks.
| null |
http://arxiv.org/abs/1806.00572v3
|
http://arxiv.org/pdf/1806.00572v3.pdf
| null |
[
"Thanh V. Nguyen",
"Raymond K. W. Wong",
"Chinmay Hegde"
] |
[] | 2018-06-02T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Solana transaction not confirmed, your Solana wallet not showing balance, or you're trying to recover a lost Solana wallet, knowing where to get help is essential. That’s why the Solana customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Solana Customer Support Number +1-833-534-1729\r\nSolana operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Solana Transaction Not Confirmed\r\nOne of the most common concerns is when a Solana transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Solana Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Solana wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Solana Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Solana wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Solana Deposit Not Received\r\nIf someone has sent you Solana but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Solana deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Solana Transaction Stuck or Pending\r\nSometimes your Solana transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Solana Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Solana wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Solana Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Solana tech.\r\n\r\n24/7 Availability: Solana doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Solana Support and Wallet Issues\r\nQ1: Can Solana support help me recover stolen BTC?\r\nA: While Solana transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Solana transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Solana’s official number (Solana is decentralized), it connects you to trained professionals experienced in resolving all major Solana issues.\r\n\r\nFinal Thoughts\r\nSolana is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Solana transaction not confirmed, your Solana wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Solana customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Solana Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Solana Customer Service Number +1-833-534-1729",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
}
] |
https://paperswithcode.com/paper/variable-selection-for-nonparametric-learning
|
1806.00569
| null | null |
Variable Selection for Nonparametric Learning with Power Series Kernels
|
In this paper, we propose a variable selection method for general
nonparametric kernel-based estimation. The proposed method consists of
two-stage estimation: (1) construct a consistent estimator of the target
function, (2) approximate the estimator using a few variables by l1-type
penalized estimation. We see that the proposed method can be applied to various
kernel nonparametric estimation such as kernel ridge regression, kernel-based
density and density-ratio estimation. We prove that the proposed method has the
property of the variable selection consistency when the power series kernel is
used. This result is regarded as an extension of the variable selection
consistency for the non-negative garrote to the kernel-based estimators.
Several experiments including simulation studies and real data applications
show the effectiveness of the proposed method.
| null |
http://arxiv.org/abs/1806.00569v2
|
http://arxiv.org/pdf/1806.00569v2.pdf
| null |
[
"Kota Matsui",
"Wataru Kumagai",
"Kenta Kanamori",
"Mitsuaki Nishikimi",
"Takafumi Kanamori"
] |
[
"Density Ratio Estimation",
"regression",
"Variable Selection"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/semtk-an-ontology-first-open-source-semantic
|
1710.11531
| null | null |
SemTK: An Ontology-first, Open Source Semantic Toolkit for Managing and Querying Knowledge Graphs
|
The relatively recent adoption of Knowledge Graphs as an enabling technology
in multiple high-profile artificial intelligence and cognitive applications has
led to growing interest in the Semantic Web technology stack. Many
semantics-related tools, however, are focused on serving experts with a deep
understanding of semantic technologies. For example, triplification of
relational data is available but there is no open source tool that allows a
user unfamiliar with OWL/RDF to import data into a semantic triple store in an
intuitive manner. Further, many tools require users to have a working
understanding of SPARQL to query data. Casual users interested in benefiting
from the power of Knowledge Graphs have few tools available for exploring,
querying, and managing semantic data. We present SemTK, the Semantics Toolkit,
a user-friendly suite of tools that allow both expert and non-expert semantics
users convenient ingestion of relational data, simplified query generation, and
more. The exploration of ontologies and instance data is performed through
SPARQLgraph, an intuitive web-based user interface in SemTK understandable and
navigable by a lay user. The open source version of SemTK is available at
http://semtk.research.ge.com
|
For example, triplification of relational data is available but there is no open source tool that allows a user unfamiliar with OWL/RDF to import data into a semantic triple store in an intuitive manner.
|
http://arxiv.org/abs/1710.11531v2
|
http://arxiv.org/pdf/1710.11531v2.pdf
| null |
[
"Paul Cuddihy",
"Justin McHugh",
"Jenny Weisenberg Williams",
"Varish Mulwad",
"Kareem S. Aggour"
] |
[
"Knowledge Graphs"
] | 2017-10-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/generating-realistic-geology-conditioned-on
|
1802.03065
| null | null |
Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks
|
An important problem in geostatistics is to build models of the subsurface of
the Earth given physical measurements at sparse spatial locations. Typically,
this is done using spatial interpolation methods or by reproducing patterns
from a reference image. However, these algorithms fail to produce realistic
patterns and do not exhibit the wide range of uncertainty inherent in the
prediction of geology. In this paper, we show how semantic inpainting with
Generative Adversarial Networks can be used to generate varied realizations of
geology which honor physical measurements while matching the expected
geological patterns. In contrast to other algorithms, our method scales well
with the number of data points and mimics a distribution of patterns as opposed
to a single pattern or image. The generated conditional samples are state of
the art.
|
An important problem in geostatistics is to build models of the subsurface of the Earth given physical measurements at sparse spatial locations.
|
http://arxiv.org/abs/1802.03065v3
|
http://arxiv.org/pdf/1802.03065v3.pdf
| null |
[
"Emilien Dupont",
"Tuanfeng Zhang",
"Peter Tilke",
"Lin Liang",
"William Bailey"
] |
[
"Spatial Interpolation"
] | 2018-02-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/improved-learning-of-one-hidden-layer
|
1805.07798
| null |
rkMnHjC5YQ
|
Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps
|
We propose a new algorithm to learn a one-hidden-layer convolutional neural
network where both the convolutional weights and the outputs weights are
parameters to be learned. Our algorithm works for a general class of
(potentially overlapping) patches, including commonly used structures for
computer vision tasks. Our algorithm draws ideas from (1) isotonic regression
for learning neural networks and (2) landscape analysis of non-convex matrix
factorization problems. We believe these findings may inspire further
development in designing provable algorithms for learning neural networks and
other complex models.
| null |
http://arxiv.org/abs/1805.07798v2
|
http://arxiv.org/pdf/1805.07798v2.pdf
|
ICLR 2019 5
|
[
"Simon S. Du",
"Surbhi Goel"
] |
[
"regression"
] | 2018-05-20T00:00:00 |
https://openreview.net/forum?id=rkMnHjC5YQ
|
https://openreview.net/pdf?id=rkMnHjC5YQ
|
improved-learning-of-one-hidden-layer-1
| null |
[] |
https://paperswithcode.com/paper/cubeslam-monocular-3d-object-detection-and
|
1806.00557
| null | null |
CubeSLAM: Monocular 3D Object SLAM
|
We present a method for single image 3D cuboid object detection and
multi-view object SLAM in both static and dynamic environments, and demonstrate
that the two parts can improve each other. Firstly for single image object
detection, we generate high-quality cuboid proposals from 2D bounding boxes and
vanishing points sampling. The proposals are further scored and selected based
on the alignment with image edges. Secondly, multi-view bundle adjustment with
new object measurements is proposed to jointly optimize poses of cameras,
objects and points. Objects can provide long-range geometric and scale
constraints to improve camera pose estimation and reduce monocular drift.
Instead of treating dynamic regions as outliers, we utilize object
representation and motion model constraints to improve the camera pose
estimation. The 3D detection experiments on SUN RGBD and KITTI show better
accuracy and robustness over existing approaches. On the public TUM, KITTI
odometry and our own collected datasets, our SLAM method achieves the
state-of-the-art monocular camera pose estimation and at the same time,
improves the 3D object detection accuracy.
|
Objects can provide long-range geometric and scale constraints to improve camera pose estimation and reduce monocular drift.
|
http://arxiv.org/abs/1806.00557v2
|
http://arxiv.org/pdf/1806.00557v2.pdf
| null |
[
"Shichao Yang",
"Sebastian Scherer"
] |
[
"3D Object Detection",
"Camera Pose Estimation",
"Object",
"object-detection",
"Object Detection",
"Object SLAM",
"Pose Estimation"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/intrinsic-isometric-manifold-learning-with
|
1806.00556
| null | null |
Intrinsic Isometric Manifold Learning with Application to Localization
|
Data living on manifolds commonly appear in many applications. Often this
results from an inherently latent low-dimensional system being observed through
higher dimensional measurements. We show that under certain conditions, it is
possible to construct an intrinsic and isometric data representation, which
respects an underlying latent intrinsic geometry. Namely, we view the observed
data only as a proxy and learn the structure of a latent unobserved intrinsic
manifold, whereas common practice is to learn the manifold of the observed
data. For this purpose, we build a new metric and propose a method for its
robust estimation by assuming mild statistical priors and by using artificial
neural networks as a mechanism for metric regularization and parametrization.
We show successful application to unsupervised indoor localization in ad-hoc
sensor networks. Specifically, we show that our proposed method facilitates
accurate localization of a moving agent from imaging data it collects.
Importantly, our method is applied in the same way to two different imaging
modalities, thereby demonstrating its intrinsic and modality-invariant
capabilities.
| null |
http://arxiv.org/abs/1806.00556v2
|
http://arxiv.org/pdf/1806.00556v2.pdf
| null |
[
"Ariel Schwartz",
"Ronen Talmon"
] |
[
"Indoor Localization"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-curiosity-search-intra-life-exploration
|
1806.00553
| null |
BkeDEoCctQ
|
Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems
|
Traditional exploration methods in RL require agents to perform random
actions to find rewards. But these approaches struggle on sparse-reward domains
like Montezuma's Revenge where the probability that any random action sequence
leads to reward is extremely low. Recent algorithms have performed well on such
tasks by encouraging agents to visit new states or perform new actions in
relation to all prior training episodes (which we call across-training
novelty). But such algorithms do not consider whether an agent exhibits
intra-life novelty: doing something new within the current episode, regardless
of whether those behaviors have been performed in previous episodes. We
hypothesize that across-training novelty might discourage agents from
revisiting initially non-rewarding states that could become important stepping
stones later in training. We introduce Deep Curiosity Search (DeepCS), which
encourages intra-life exploration by rewarding agents for visiting as many
different states as possible within each episode, and show that DeepCS matches
the performance of current state-of-the-art methods on Montezuma's Revenge. We
further show that DeepCS improves exploration on Amidar, Freeway, Gravitar, and
Tutankham (many of which are hard exploration games). Surprisingly, DeepCS
doubles A2C performance on Seaquest, a game we would not have expected to
benefit from intra-life exploration because the arena is small and already
easily navigated by naive exploration techniques. In one run, DeepCS achieves a
maximum training score of 80,000 points on Seaquest, higher than any methods
other than Ape-X. The strong performance of DeepCS on these sparse- and
dense-reward tasks suggests that encouraging intra-life novelty is an
interesting, new approach for improving performance in Deep RL and motivates
further research into hybridizing across-training and intra-life exploration
methods.
| null |
http://arxiv.org/abs/1806.00553v3
|
http://arxiv.org/pdf/1806.00553v3.pdf
| null |
[
"Christopher Stanton",
"Jeff Clune"
] |
[
"Deep Reinforcement Learning",
"Montezuma's Revenge",
"Reinforcement Learning"
] | 2018-06-01T00:00:00 |
https://openreview.net/forum?id=BkeDEoCctQ
|
https://openreview.net/pdf?id=BkeDEoCctQ
| null | null |
[
{
"code_snippet_url": "",
"description": "**Prioritized Experience Replay** is a type of [experience replay](https://paperswithcode.com/method/experience-replay) in reinforcement learning where we more frequently replay transitions with high expected learning progress, as measured by the magnitude of their temporal-difference (TD) error. This prioritization can lead to a loss of diversity, which is alleviated with stochastic prioritization, and introduce bias, which can be corrected with importance sampling.\r\n\r\nThe stochastic sampling method interpolates between pure greedy prioritization and uniform random sampling. The probability of being sampled is ensured to be monotonic in a transition's priority, while guaranteeing a non-zero probability even for the lowest-priority transition. Concretely, define the probability of sampling transition $i$ as\r\n\r\n$$P(i) = \\frac{p_i^{\\alpha}}{\\sum_k p_k^{\\alpha}}$$\r\n\r\nwhere $p_i > 0$ is the priority of transition $i$. The exponent $\\alpha$ determines how much prioritization is used, with $\\alpha=0$ corresponding to the uniform case.\r\n\r\nPrioritized replay introduces bias because it changes this distribution in an uncontrolled fashion, and therefore changes the solution that the estimates will converge to. We can correct this bias by using\r\nimportance-sampling (IS) weights:\r\n\r\n$$ w\\_{i} = \\left(\\frac{1}{N}\\cdot\\frac{1}{P\\left(i\\right)}\\right)^{\\beta} $$\r\n\r\nthat fully compensates for the non-uniform probabilities $P\\left(i\\right)$ if $\\beta = 1$. These weights can be folded into the [Q-learning](https://paperswithcode.com/method/q-learning) update by using $w\\_{i}\\delta\\_{i}$ instead of $\\delta\\_{i}$ - weighted IS rather than ordinary IS. For stability reasons, we always normalize weights by $1/\\max\\_{i}w\\_{i}$ so\r\nthat they only scale the update downwards.\r\n\r\nThe two types of prioritization are proportional based, where $p\\_{i} = |\\delta\\_{i}| + \\epsilon$ and rank-based, where $p\\_{i} = \\frac{1}{\\text{rank}\\left(i\\right)}$, the latter where $\\text{rank}\\left(i\\right)$ is the rank of transition $i$ when the replay memory is sorted according to |$\\delta\\_{i}$|, For proportional based, hyperparameters used were $\\alpha = 0.7$, $\\beta\\_{0} = 0.5$. For the rank-based variant, hyperparameters used were $\\alpha = 0.6$, $\\beta\\_{0} = 0.4$.",
"full_name": "Prioritized Experience Replay",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Replay Memory",
"parent": null
},
"name": "Prioritized Experience Replay",
"source_title": "Prioritized Experience Replay",
"source_url": "http://arxiv.org/abs/1511.05952v4"
},
{
"code_snippet_url": null,
"description": "**Ape-X** is a distributed architecture for deep reinforcement learning. The algorithm decouples acting from learning: the actors interact with their own instances of the environment by selecting actions according to a shared neural network, and accumulate the resulting experience in a shared [experience replay](https://paperswithcode.com/method/experience-replay) memory; the learner replays samples of experience and updates the neural network. The architecture relies on [prioritized experience replay](https://paperswithcode.com/method/prioritized-experience-replay) to focus only on the most significant data generated by the actors.\r\n\r\nIn contrast to Gorila, Ape-X uses a shared, centralized replay memory, and instead of sampling\r\nuniformly, it prioritizes, to sample the most useful data more often. All communications are batched with the centralized replay, increasing the efficiency and throughput at the cost of some latency. \r\nAnd by learning off-policy, Ape-X has the ability to combine data from many distributed actors, by giving the different actors different exploration policies, broadening the diversity of the experience they jointly encounter.",
"full_name": "Ape-X",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Distributed Reinforcement Learning",
"parent": null
},
"name": "Ape-X",
"source_title": "Distributed Prioritized Experience Replay",
"source_url": "http://arxiv.org/abs/1803.00933v1"
},
{
"code_snippet_url": null,
"description": "**A2C**, or **Advantage Actor Critic**, is a synchronous version of the [A3C](https://paperswithcode.com/method/a3c) policy gradient method. As an alternative to the asynchronous implementation of A3C, A2C is a synchronous, deterministic implementation that waits for each actor to finish its segment of experience before updating, averaging over all of the actors. This more effectively uses GPUs due to larger batch sizes.\r\n\r\nImage Credit: [OpenAI Baselines](https://openai.com/blog/baselines-acktr-a2c/)",
"full_name": "A2C",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "**Policy Gradient Methods** try to optimize the policy function directly in reinforcement learning. This contrasts with, for example, Q-Learning, where the policy manifests itself as maximizing a value function. Below you can find a continuously updating catalog of policy gradient methods.",
"name": "Policy Gradient Methods",
"parent": null
},
"name": "A2C",
"source_title": "Asynchronous Methods for Deep Reinforcement Learning",
"source_url": "http://arxiv.org/abs/1602.01783v2"
}
] |
https://paperswithcode.com/paper/bayesian-approach-to-model-based
|
1806.00552
| null | null |
Bayesian approach to model-based extrapolation of nuclear observables
|
The mass, or binding energy, is the basis property of the atomic nucleus. It
determines its stability, and reaction and decay rates. Quantifying the nuclear
binding is important for understanding the origin of elements in the universe.
The astrophysical processes responsible for the nucleosynthesis in stars often
take place far from the valley of stability, where experimental masses are not
known. In such cases, missing nuclear information must be provided by
theoretical predictions using extreme extrapolations. Bayesian machine learning
techniques can be applied to improve predictions by taking full advantage of
the information contained in the deviations between experimental and calculated
masses. We consider 10 global models based on nuclear Density Functional Theory
as well as two more phenomenological mass models. The emulators of S2n
residuals and credibility intervals defining theoretical error bars are
constructed using Bayesian Gaussian processes and Bayesian neural networks. We
consider a large training dataset pertaining to nuclei whose masses were
measured before 2003. For the testing datasets, we considered those exotic
nuclei whose masses have been determined after 2003. We then carried out
extrapolations towards the 2n dripline. While both Gaussian processes and
Bayesian neural networks reduce the rms deviation from experiment
significantly, GP offers a better and much more stable performance. The
increase in the predictive power is quite astonishing: the resulting rms
deviations from experiment on the testing dataset are similar to those of more
phenomenological models. The empirical coverage probability curves we obtain
match very well the reference values which is highly desirable to ensure
honesty of uncertainty quantification, and the estimated credibility intervals
on predictions make it possible to evaluate predictive power of individual
models.
| null |
http://arxiv.org/abs/1806.00552v3
|
http://arxiv.org/pdf/1806.00552v3.pdf
| null |
[
"Léo Neufcourt",
"Yuchen Cao",
"Witold Nazarewicz",
"Frederi Viens"
] |
[
"Gaussian Processes",
"Uncertainty Quantification"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/equivalence-between-wasserstein-and-value
|
1806.01265
| null | null |
Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning
|
Learning a generative model is a key component of model-based reinforcement
learning. Though learning a good model in the tabular setting is a simple task,
learning a useful model in the approximate setting is challenging. In this
context, an important question is the loss function used for model learning as
varying the loss function can have a remarkable impact on effectiveness of
planning. Recently Farahmand et al. (2017) proposed a value-aware model
learning (VAML) objective that captures the structure of value function during
model learning. Using tools from Asadi et al. (2018), we show that minimizing
the VAML objective is in fact equivalent to minimizing the Wasserstein metric.
This equivalence improves our understanding of value-aware models, and also
creates a theoretical foundation for applications of Wasserstein in model-based
reinforcement~learning.
| null |
http://arxiv.org/abs/1806.01265v2
|
http://arxiv.org/pdf/1806.01265v2.pdf
| null |
[
"Kavosh Asadi",
"Evan Cater",
"Dipendra Misra",
"Michael L. Littman"
] |
[
"Model-based Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/neural-proximal-gradient-descent-for
|
1806.03963
| null | null |
Neural Proximal Gradient Descent for Compressive Imaging
|
Recovering high-resolution images from limited sensory data typically leads
to a serious ill-posed inverse problem, demanding inversion algorithms that
effectively capture the prior information. Learning a good inverse mapping from
training data faces severe challenges, including: (i) scarcity of training
data; (ii) need for plausible reconstructions that are physically feasible;
(iii) need for fast reconstruction, especially in real-time applications. We
develop a successful system solving all these challenges, using as basic
architecture the recurrent application of proximal gradient algorithm. We learn
a proximal map that works well with real images based on residual networks.
Contraction of the resulting map is analyzed, and incoherence conditions are
investigated that drive the convergence of the iterates. Extensive experiments
are carried out under different settings: (a) reconstructing abdominal MRI of
pediatric patients from highly undersampled Fourier-space data and (b)
superresolving natural face images. Our key findings include: 1. a recurrent
ResNet with a single residual block unrolled from an iterative algorithm yields
an effective proximal which accurately reveals MR image details. 2. Our
architecture significantly outperforms conventional non-recurrent deep ResNets
by 2dB SNR; it is also trained much more rapidly. 3. It outperforms
state-of-the-art compressed-sensing Wavelet-based methods by 4dB SNR, with 100x
speedups in reconstruction time.
|
Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information.
|
http://arxiv.org/abs/1806.03963v1
|
http://arxiv.org/pdf/1806.03963v1.pdf
|
NeurIPS 2018 12
|
[
"Morteza Mardani",
"Qingyun Sun",
"Shreyas Vasawanala",
"Vardan Papyan",
"Hatef Monajemi",
"John Pauly",
"David Donoho"
] |
[
"compressed sensing"
] | 2018-06-01T00:00:00 |
http://papers.nips.cc/paper/8166-neural-proximal-gradient-descent-for-compressive-imaging
|
http://papers.nips.cc/paper/8166-neural-proximal-gradient-descent-for-compressive-imaging.pdf
|
neural-proximal-gradient-descent-for-1
| null |
[
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L35",
"description": "**Residual Blocks** are skip-connection blocks that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture.\r\n \r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$. The $\\mathcal{F}({x})$ acts like a residual, hence the name 'residual block'.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers. Having skip connections allows the network to more easily learn identity-like mappings.\r\n\r\nNote that in practice, [Bottleneck Residual Blocks](https://paperswithcode.com/method/bottleneck-residual-block) are used for deeper ResNets, such as ResNet-50 and ResNet-101, as these bottleneck blocks are less computationally intensive.",
"full_name": "Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
}
] |
https://paperswithcode.com/paper/dependent-gated-reading-for-cloze-style
|
1805.10528
| null | null |
Dependent Gated Reading for Cloze-Style Question Answering
|
We present a novel deep learning architecture to address the cloze-style
question answering task. Existing approaches employ reading mechanisms that do
not fully exploit the interdependency between the document and the query. In
this paper, we propose a novel \emph{dependent gated reading} bidirectional GRU
network (DGR) to efficiently model the relationship between the document and
the query during encoding and decision making. Our evaluation shows that DGR
obtains highly competitive performance on well-known machine comprehension
benchmarks such as the Children's Book Test (CBT-NE and CBT-CN) and Who DiD
What (WDW, Strict and Relaxed). Finally, we extensively analyze and validate
our model by ablation and attention studies.
| null |
http://arxiv.org/abs/1805.10528v2
|
http://arxiv.org/pdf/1805.10528v2.pdf
|
COLING 2018 8
|
[
"Reza Ghaeini",
"Xiaoli Z. Fern",
"Hamed Shahbazi",
"Prasad Tadepalli"
] |
[
"Decision Making",
"Question Answering",
"Reading Comprehension"
] | 2018-05-26T00:00:00 |
https://aclanthology.org/C18-1282
|
https://aclanthology.org/C18-1282.pdf
|
dependent-gated-reading-for-cloze-style-2
| null |
[] |
https://paperswithcode.com/paper/network-enhancement-a-general-method-to
|
1805.03327
| null | null |
Network Enhancement: a general method to denoise weighted biological networks
|
Networks are ubiquitous in biology where they encode connectivity patterns at
all scales of organization, from molecular to the biome. However, biological
networks are noisy due to the limitations of measurement technology and
inherent natural variation, which can hamper discovery of network patterns and
dynamics. We propose Network Enhancement (NE), a method for improving the
signal-to-noise ratio of undirected, weighted networks. NE uses a doubly
stochastic matrix operator that induces sparsity and provides a closed-form
solution that increases spectral eigengap of the input network. As a result, NE
removes weak edges, enhances real connections, and leads to better downstream
performance. Experiments show that NE improves gene function prediction by
denoising tissue-specific interaction networks, alleviates interpretation of
noisy Hi-C contact maps from the human genome, and boosts fine-grained
identification accuracy of species. Our results indicate that NE is widely
applicable for denoising biological networks.
| null |
http://arxiv.org/abs/1805.03327v2
|
http://arxiv.org/pdf/1805.03327v2.pdf
| null |
[
"Bo Wang",
"Armin Pourshafeie",
"Marinka Zitnik",
"Junjie Zhu",
"Carlos D. Bustamante",
"Serafim Batzoglou",
"Jure Leskovec"
] |
[
"Denoising"
] | 2018-05-09T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-externalities-of-exploration-and-how-data
|
1806.00543
| null | null |
The Externalities of Exploration and How Data Diversity Helps Exploitation
|
Online learning algorithms, widely used to power search and content
optimization on the web, must balance exploration and exploitation, potentially
sacrificing the experience of current users for information that will lead to
better decisions in the future. Recently, concerns have been raised about
whether the process of exploration could be viewed as unfair, placing too much
burden on certain individuals or groups. Motivated by these concerns, we
initiate the study of the externalities of exploration - the undesirable side
effects that the presence of one party may impose on another - under the linear
contextual bandits model. We introduce the notion of a group externality,
measuring the extent to which the presence of one population of users impacts
the rewards of another. We show that this impact can in some cases be negative,
and that, in a certain sense, no algorithm can avoid it. We then study
externalities at the individual level, interpreting the act of exploration as
an externality imposed on the current user of a system by future users. This
drives us to ask under what conditions inherent diversity in the data makes
explicit exploration unnecessary. We build on a recent line of work on the
smoothed analysis of the greedy algorithm that always chooses the action that
currently looks optimal, improving on prior results to show that a greedy
approach almost matches the best possible Bayesian regret rate of any other
algorithm on the same problem instance whenever the diversity conditions hold,
and that this regret is at most $\tilde{O}(T^{1/3})$. Returning to group-level
effects, we show that under the same conditions, negative group externalities
essentially vanish under the greedy algorithm. Together, our results uncover a
sharp contrast between the high externalities that exist in the worst case, and
the ability to remove all externalities if the data is sufficiently diverse.
| null |
http://arxiv.org/abs/1806.00543v2
|
http://arxiv.org/pdf/1806.00543v2.pdf
| null |
[
"Manish Raghavan",
"Aleksandrs Slivkins",
"Jennifer Wortman Vaughan",
"Zhiwei Steven Wu"
] |
[
"Diversity",
"Multi-Armed Bandits"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/integrating-episodic-memory-into-a
|
1806.00540
| null |
ByJDAIe0b
|
Integrating Episodic Memory into a Reinforcement Learning Agent using Reservoir Sampling
|
Episodic memory is a psychology term which refers to the ability to recall
specific events from the past. We suggest one advantage of this particular type
of memory is the ability to easily assign credit to a specific state when
remembered information is found to be useful. Inspired by this idea, and the
increasing popularity of external memory mechanisms to handle long-term
dependencies in deep learning systems, we propose a novel algorithm which uses
a reservoir sampling procedure to maintain an external memory consisting of a
fixed number of past states. The algorithm allows a deep reinforcement learning
agent to learn online to preferentially remember those states which are found
to be useful to recall later on. Critically this method allows for efficient
online computation of gradient estimates with respect to the write process of
the external memory. Thus unlike most prior mechanisms for external memory it
is feasible to use in an online reinforcement learning setting.
| null |
http://arxiv.org/abs/1806.00540v1
|
http://arxiv.org/pdf/1806.00540v1.pdf
|
ICLR 2018 1
|
[
"Kenny J. Young",
"Richard S. Sutton",
"Shuo Yang"
] |
[
"Deep Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-01T00:00:00 |
https://openreview.net/forum?id=ByJDAIe0b
|
https://openreview.net/pdf?id=ByJDAIe0b
|
integrating-episodic-memory-into-a-1
| null |
[] |
https://paperswithcode.com/paper/run-procrustes-run-on-the-convergence-of
|
1806.00534
| null | null |
Provably convergent acceleration in factored gradient descent with applications in matrix sensing
|
We present theoretical results on the convergence of \emph{non-convex} accelerated gradient descent in matrix factorization models with $\ell_2$-norm loss. The purpose of this work is to study the effects of acceleration in non-convex settings, where provable convergence with acceleration should not be considered a \emph{de facto} property. The technique is applied to matrix sensing problems, for the estimation of a rank $r$ optimal solution $X^\star \in \mathbb{R}^{n \times n}$. Our contributions can be summarized as follows. $i)$ We show that acceleration in factored gradient descent converges at a linear rate; this fact is novel for non-convex matrix factorization settings, under common assumptions. $ii)$ Our proof technique requires the acceleration parameter to be carefully selected, based on the properties of the problem, such as the condition number of $X^\star$ and the condition number of objective function. $iii)$ Currently, our proof leads to the same dependence on the condition number(s) in the contraction parameter, similar to recent results on non-accelerated algorithms. $iv)$ Acceleration is observed in practice, both in synthetic examples and in two real applications: neuronal multi-unit activities recovery from single electrode recordings, and quantum state tomography on quantum computing simulators.
| null |
https://arxiv.org/abs/1806.00534v5
|
https://arxiv.org/pdf/1806.00534v5.pdf
| null |
[
"Tayo Ajayi",
"David Mildebrath",
"Anastasios Kyrillidis",
"Shashanka Ubaru",
"Georgios Kollias",
"Kristofer Bouchard"
] |
[
"Quantum State Tomography"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/audio-visual-scene-aware-dialog-avsd
|
1806.00525
| null | null |
Audio Visual Scene-Aware Dialog (AVSD) Challenge at DSTC7
|
Scene-aware dialog systems will be able to have conversations with users
about the objects and events around them. Progress on such systems can be made
by integrating state-of-the-art technologies from multiple research areas
including end-to-end dialog systems visual dialog, and video description. We
introduce the Audio Visual Scene Aware Dialog (AVSD) challenge and dataset. In
this challenge, which is one track of the 7th Dialog System Technology
Challenges (DSTC7) workshop1, the task is to build a system that generates
responses in a dialog about an input video
|
Scene-aware dialog systems will be able to have conversations with users about the objects and events around them.
|
http://arxiv.org/abs/1806.00525v1
|
http://arxiv.org/pdf/1806.00525v1.pdf
| null |
[
"Huda Alamri",
"Vincent Cartillier",
"Raphael Gontijo Lopes",
"Abhishek Das",
"Jue Wang",
"Irfan Essa",
"Dhruv Batra",
"Devi Parikh",
"Anoop Cherian",
"Tim K. Marks",
"Chiori Hori"
] |
[
"Video Description",
"Visual Dialog"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/targeted-kernel-networks-faster-convolutions
|
1806.00523
| null | null |
Targeted Kernel Networks: Faster Convolutions with Attentive Regularization
|
We propose Attentive Regularization (AR), a method to constrain the
activation maps of kernels in Convolutional Neural Networks (CNNs) to specific
regions of interest (ROIs). Each kernel learns a location of specialization
along with its weights through standard backpropagation. A differentiable
attention mechanism requiring no additional supervision is used to optimize the
ROIs. Traditional CNNs of different types and structures can be modified with
this idea into equivalent Targeted Kernel Networks (TKNs), while keeping the
network size nearly identical. By restricting kernel ROIs, we reduce the number
of sliding convolutional operations performed throughout the network in its
forward pass, speeding up both training and inference. We evaluate our proposed
architecture on both synthetic and natural tasks across multiple domains. TKNs
obtain significant improvements over baselines, requiring less computation
(around an order of magnitude) while achieving superior performance.
| null |
http://arxiv.org/abs/1806.00523v2
|
http://arxiv.org/pdf/1806.00523v2.pdf
| null |
[
"Kashyap Chitta"
] |
[] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/opentag-open-attribute-value-extraction-from
|
1806.01264
| null | null |
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]
|
Extraction of missing attribute values is to find values describing an
attribute of interest from a free text input. Most past related work on
extraction of missing attribute values work with a closed world assumption with
the possible set of values known beforehand, or use dictionaries of values and
hand-crafted features. How can we discover new attribute values that we have
never seen before? Can we do this with limited human annotation or supervision?
We study this problem in the context of product catalogs that often have
missing values for many attributes of interest.
In this work, we leverage product profile information such as titles and
descriptions to discover missing values of product attributes. We develop a
novel deep tagging model OpenTag for this extraction problem with the following
contributions: (1) we formalize the problem as a sequence tagging task, and
propose a joint model exploiting recurrent neural networks (specifically,
bidirectional LSTM) to capture context and semantics, and Conditional Random
Fields (CRF) to enforce tagging consistency, (2) we develop a novel attention
mechanism to provide interpretable explanation for our model's decisions, (3)
we propose a novel sampling strategy exploring active learning to reduce the
burden of human annotation. OpenTag does not use any dictionary or hand-crafted
features as in prior works. Extensive experiments in real-life datasets in
different domains show that OpenTag with our active learning strategy discovers
new attribute values from as few as 150 annotated samples (reduction in 3.3x
amount of annotation effort) with a high F-score of 83%, outperforming
state-of-the-art models.
|
We study this problem in the context of product catalogs that often have missing values for many attributes of interest.
|
http://arxiv.org/abs/1806.01264v2
|
http://arxiv.org/pdf/1806.01264v2.pdf
| null |
[
"Guineng Zheng",
"Subhabrata Mukherjee",
"Xin Luna Dong",
"Fei-Fei Li"
] |
[
"Active Learning",
"Attribute",
"Attribute Value Extraction",
"Missing Values",
"named-entity-recognition",
"Named Entity Recognition",
"Named Entity Recognition (NER)"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/structurally-sparsified-backward-propagation
|
1806.00512
| null | null |
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
|
Exploiting sparsity enables hardware systems to run neural networks faster
and more energy-efficiently. However, most prior sparsity-centric optimization
techniques only accelerate the forward pass of neural networks and usually
require an even longer training process with iterative pruning and retraining.
We observe that artificially inducing sparsity in the gradients of the gates in
an LSTM cell has little impact on the training quality. Further, we can enforce
structured sparsity in the gate gradients to make the LSTM backward pass up to
45% faster than the state-of-the-art dense approach and 168% faster than the
state-of-the-art sparsifying method on modern GPUs. Though the structured
sparsifying method can impact the accuracy of a model, this performance gap can
be eliminated by mixing our sparse training method and the standard dense
training method. Experimental results show that the mixed method can achieve
comparable results in a shorter time span than using purely dense training.
| null |
http://arxiv.org/abs/1806.00512v1
|
http://arxiv.org/pdf/1806.00512v1.pdf
| null |
[
"Maohua Zhu",
"Jason Clemons",
"Jeff Pool",
"Minsoo Rhu",
"Stephen W. Keckler",
"Yuan Xie"
] |
[] | 2018-06-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Pruning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Model Compression",
"parent": null
},
"name": "Pruning",
"source_title": "Pruning Filters for Efficient ConvNets",
"source_url": "http://arxiv.org/abs/1608.08710v3"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/semi-recurrent-cnn-based-vae-gan-for
|
1806.00509
| null | null |
Semi-Recurrent CNN-based VAE-GAN for Sequential Data Generation
|
A semi-recurrent hybrid VAE-GAN model for generating sequential data is
introduced. In order to consider the spatial correlation of the data in each
frame of the generated sequence, CNNs are utilized in the encoder, generator,
and discriminator. The subsequent frames are sampled from the latent
distributions obtained by encoding the previous frames. As a result, the
dependencies between the frames are maintained. Two testing frameworks for
synthesizing a sequence with any number of frames are also proposed. The
promising experimental results on piano music generation indicates the
potential of the proposed framework in modeling other sequential data such as
video.
|
A semi-recurrent hybrid VAE-GAN model for generating sequential data is introduced.
|
http://arxiv.org/abs/1806.00509v1
|
http://arxiv.org/pdf/1806.00509v1.pdf
| null |
[
"Mohammad Akbari",
"Jie Liang"
] |
[
"Music Generation"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/agil-learning-attention-from-human-for
|
1806.03960
| null | null |
AGIL: Learning Attention from Human for Visuomotor Tasks
|
When intelligent agents learn visuomotor behaviors from human demonstrations,
they may benefit from knowing where the human is allocating visual attention,
which can be inferred from their gaze. A wealth of information regarding
intelligent decision making is conveyed by human gaze allocation; hence,
exploiting such information has the potential to improve the agents'
performance. With this motivation, we propose the AGIL (Attention Guided
Imitation Learning) framework. We collect high-quality human action and gaze
data while playing Atari games in a carefully controlled experimental setting.
Using these data, we first train a deep neural network that can predict human
gaze positions and visual attention with high accuracy (the gaze network) and
then train another network to predict human actions (the policy network).
Incorporating the learned attention model from the gaze network into the policy
network significantly improves the action prediction accuracy and task
performance.
| null |
http://arxiv.org/abs/1806.03960v1
|
http://arxiv.org/pdf/1806.03960v1.pdf
|
ECCV 2018 9
|
[
"Ruohan Zhang",
"Zhuode Liu",
"Luxin Zhang",
"Jake A. Whritner",
"Karl S. Muller",
"Mary M. Hayhoe",
"Dana H. Ballard"
] |
[
"Atari Games",
"Decision Making",
"Imitation Learning"
] | 2018-06-01T00:00:00 |
http://openaccess.thecvf.com/content_ECCV_2018/html/Ruohan_Zhang_AGIL_Learning_Attention_ECCV_2018_paper.html
|
http://openaccess.thecvf.com/content_ECCV_2018/papers/Ruohan_Zhang_AGIL_Learning_Attention_ECCV_2018_paper.pdf
|
agil-learning-attention-from-human-for-1
| null |
[] |
https://paperswithcode.com/paper/backpropagation-for-implicit-spectral
|
1806.00499
| null | null |
Backpropagation for Implicit Spectral Densities
|
Most successful machine intelligence systems rely on gradient-based learning,
which is made possible by backpropagation. Some systems are designed to aid us
in interpreting data when explicit goals cannot be provided. These unsupervised
systems are commonly trained by backpropagating through a likelihood function.
We introduce a tool that allows us to do this even when the likelihood is not
explicitly set, by instead using the implicit likelihood of the model.
Explicitly defining the likelihood often entails making heavy-handed
assumptions that impede our ability to solve challenging tasks. On the other
hand, the implicit likelihood of the model is accessible without the need for
such assumptions. Our tool, which we call spectral backpropagation, allows us
to optimize it in much greater generality than what has been attempted before.
GANs can also be viewed as a technique for optimizing implicit likelihoods. We
study them using spectral backpropagation in order to demonstrate robustness
for high-dimensional problems, and identify two novel properties of the
generator G: (1) there exist aberrant, nonsensical outputs to which G assigns
very high likelihood, and (2) the eigenvectors of the metric induced by G over
latent space correspond to quasi-disentangled explanatory factors.
|
We introduce a tool that allows us to do this even when the likelihood is not explicitly set, by instead using the implicit likelihood of the model.
|
http://arxiv.org/abs/1806.00499v1
|
http://arxiv.org/pdf/1806.00499v1.pdf
| null |
[
"Aditya Ramesh",
"Yann Lecun"
] |
[] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/implicit-bias-of-gradient-descent-on-linear
|
1806.00468
| null | null |
Implicit Bias of Gradient Descent on Linear Convolutional Networks
|
We show that gradient descent on full-width linear convolutional networks of
depth $L$ converges to a linear predictor related to the $\ell_{2/L}$ bridge
penalty in the frequency domain. This is in contrast to linearly fully
connected networks, where gradient descent converges to the hard margin linear
support vector machine solution, regardless of depth.
| null |
http://arxiv.org/abs/1806.00468v2
|
http://arxiv.org/pdf/1806.00468v2.pdf
|
NeurIPS 2018 12
|
[
"Suriya Gunasekar",
"Jason Lee",
"Daniel Soudry",
"Nathan Srebro"
] |
[] | 2018-06-01T00:00:00 |
http://papers.nips.cc/paper/8156-implicit-bias-of-gradient-descent-on-linear-convolutional-networks
|
http://papers.nips.cc/paper/8156-implicit-bias-of-gradient-descent-on-linear-convolutional-networks.pdf
|
implicit-bias-of-gradient-descent-on-linear-1
| null |
[] |
https://paperswithcode.com/paper/surgical-activity-recognition-in-robot
|
1806.00466
| null | null |
Surgical Activity Recognition in Robot-Assisted Radical Prostatectomy using Deep Learning
|
Adverse surgical outcomes are costly to patients and hospitals. Approaches to
benchmark surgical care are often limited to gross measures across the entire
procedure despite the performance of particular tasks being largely responsible
for undesirable outcomes. In order to produce metrics from tasks as opposed to
the whole procedure, methods to recognize automatically individual surgical
tasks are needed. In this paper, we propose several approaches to recognize
surgical activities in robot-assisted minimally invasive surgery using deep
learning. We collected a clinical dataset of 100 robot-assisted radical
prostatectomies (RARP) with 12 tasks each and propose `RP-Net', a modified
version of InceptionV3 model, for image based surgical activity recognition. We
achieve an average precision of 80.9% and average recall of 76.7% across all
tasks using RP-Net which out-performs all other RNN and CNN based models
explored in this paper. Our results suggest that automatic surgical activity
recognition during RARP is feasible and can be the foundation for advanced
analytics.
| null |
http://arxiv.org/abs/1806.00466v1
|
http://arxiv.org/pdf/1806.00466v1.pdf
| null |
[
"Aneeq Zia",
"Andrew Hung",
"Irfan Essa",
"Anthony Jarc"
] |
[
"Activity Recognition"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/improved-image-captioning-with-adversarial
|
1805.00063
| null | null |
Adversarial Semantic Alignment for Improved Image Captions
|
In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions. We empirically focus on the viability of two training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST, even without accessing discriminator gradients directly. We also address the problem of automatic evaluation for captioning models and introduce a new semantic score, and show its correlation to human judgement. As an evaluation paradigm, we argue that an important criterion for a captioner is the ability to generalize to compositions of objects that do not usually co-occur together. To this end, we introduce a small captioned Out of Context (OOC) test set. The OOC set, combined with our semantic score, are the proposed new diagnosis tools for the captioning community. When evaluated on OOC and MS-COCO benchmarks, we show that SCST-based training has a strong performance in both semantic score and human evaluation, promising to be a valuable new approach for efficient discrete GAN training.
| null |
https://arxiv.org/abs/1805.00063v3
|
https://arxiv.org/pdf/1805.00063v3.pdf
| null |
[
"Pierre L. Dognin",
"Igor Melnyk",
"Youssef Mroueh",
"Jarret Ross",
"Tom Sercu"
] |
[
"Image Captioning"
] | 2018-04-30T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Self-critical Sequence Training",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Reinforcement Learning Frameworks",
"parent": null
},
"name": "SCST",
"source_title": "Self-critical Sequence Training for Image Captioning",
"source_url": "http://arxiv.org/abs/1612.00563v2"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Dogecoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're trying to recover a lost Dogecoin wallet, knowing where to get help is essential. That’s why the Dogecoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Dogecoin Customer Support Number +1-833-534-1729\r\nDogecoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Dogecoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Dogecoin transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Dogecoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Dogecoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Dogecoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Dogecoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Dogecoin Deposit Not Received\r\nIf someone has sent you Dogecoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Dogecoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Dogecoin Transaction Stuck or Pending\r\nSometimes your Dogecoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Dogecoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Dogecoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Dogecoin Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Dogecoin tech.\r\n\r\n24/7 Availability: Dogecoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Dogecoin Support and Wallet Issues\r\nQ1: Can Dogecoin support help me recover stolen BTC?\r\nA: While Dogecoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Dogecoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Dogecoin’s official number (Dogecoin is decentralized), it connects you to trained professionals experienced in resolving all major Dogecoin issues.\r\n\r\nFinal Thoughts\r\nDogecoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Dogecoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Dogecoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Dogecoin Customer Service Number +1-833-534-1729",
"source_title": "Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1406.2661v1"
}
] |
https://paperswithcode.com/paper/adversarial-quantum-circuit-learning-for-pure
|
1806.00463
| null | null |
Adversarial quantum circuit learning for pure state approximation
|
Adversarial learning is one of the most successful approaches to modelling
high-dimensional probability distributions from data. The quantum computing
community has recently begun to generalize this idea and to look for potential
applications. In this work, we derive an adversarial algorithm for the problem
of approximating an unknown quantum pure state. Although this could be done on
universal quantum computers, the adversarial formulation enables us to execute
the algorithm on near-term quantum computers. Two parametrized circuits are
optimized in tandem: One tries to approximate the target state, the other tries
to distinguish between target and approximated state. Supported by numerical
simulations, we show that resilient backpropagation algorithms perform
remarkably well in optimizing the two circuits. We use the bipartite
entanglement entropy to design an efficient heuristic for the stopping
criterion. Our approach may find application in quantum state tomography.
| null |
http://arxiv.org/abs/1806.00463v3
|
http://arxiv.org/pdf/1806.00463v3.pdf
| null |
[
"Marcello Benedetti",
"Edward Grant",
"Leonard Wossnig",
"Simone Severini"
] |
[
"Quantum State Tomography"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multi-objective-contextual-multi-armed-bandit
|
1708.05655
| null | null |
Multi-objective Contextual Multi-armed Bandit with a Dominant Objective
|
In this paper, we propose a new multi-objective contextual multi-armed bandit
(MAB) problem with two objectives, where one of the objectives dominates the
other objective. Unlike single-objective MAB problems in which the learner
obtains a random scalar reward for each arm it selects, in the proposed
problem, the learner obtains a random reward vector, where each component of
the reward vector corresponds to one of the objectives and the distribution of
the reward depends on the context that is provided to the learner at the
beginning of each round. We call this problem contextual multi-armed bandit
with a dominant objective (CMAB-DO). In CMAB-DO, the goal of the learner is to
maximize its total reward in the non-dominant objective while ensuring that it
maximizes its total reward in the dominant objective. In this case, the optimal
arm given a context is the one that maximizes the expected reward in the
non-dominant objective among all arms that maximize the expected reward in the
dominant objective. First, we show that the optimal arm lies in the Pareto
front. Then, we propose the multi-objective contextual multi-armed bandit
algorithm (MOC-MAB), and define two performance measures: the 2-dimensional
(2D) regret and the Pareto regret. We show that both the 2D regret and the
Pareto regret of MOC-MAB are sublinear in the number of rounds. We also compare
the performance of the proposed algorithm with other state-of-the-art methods
in synthetic and real-world datasets. The proposed model and the algorithm have
a wide range of real-world applications that involve multiple and possibly
conflicting objectives ranging from wireless communication to medical diagnosis
and recommender systems.
| null |
http://arxiv.org/abs/1708.05655v3
|
http://arxiv.org/pdf/1708.05655v3.pdf
| null |
[
"Cem Tekin",
"Eralp Turgay"
] |
[
"Medical Diagnosis",
"Recommendation Systems"
] | 2017-08-18T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/do-cifar-10-classifiers-generalize-to-cifar
|
1806.00451
| null | null |
Do CIFAR-10 Classifiers Generalize to CIFAR-10?
|
Machine learning is currently dominated by largely experimental work focused
on improvements in a few key tasks. However, the impressive accuracy numbers of
the best performing models are questionable because the same test sets have
been used to select these models for multiple years now. To understand the
danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by
creating a new test set of truly unseen images. Although we ensure that the new
test set is as close to the original data distribution as possible, we find a
large drop in accuracy (4% to 10%) for a broad range of deep learning models.
Yet more recent models with higher original accuracy show a smaller drop and
better overall performance, indicating that this drop is likely not due to
overfitting based on adaptivity. Instead, we view our results as evidence that
current accuracy numbers are brittle and susceptible to even minute natural
variations in the data distribution.
|
Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models.
|
http://arxiv.org/abs/1806.00451v1
|
http://arxiv.org/pdf/1806.00451v1.pdf
| null |
[
"Benjamin Recht",
"Rebecca Roelofs",
"Ludwig Schmidt",
"Vaishaal Shankar"
] |
[] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-to-bid-without-knowing-your-value
|
1711.01333
| null | null |
Learning to Bid Without Knowing your Value
|
We address online learning in complex auction settings, such as sponsored
search auctions, where the value of the bidder is unknown to her, evolving in
an arbitrary manner and observed only if the bidder wins an allocation. We
leverage the structure of the utility of the bidder and the partial feedback
that bidders typically receive in auctions, in order to provide algorithms with
regret rates against the best fixed bid in hindsight, that are exponentially
faster in convergence in terms of dependence on the action space, than what
would have been derived by applying a generic bandit algorithm and almost
equivalent to what would have been achieved in the full information setting.
Our results are enabled by analyzing a new online learning setting with
outcome-based feedback, which generalizes learning with feedback graphs. We
provide an online learning algorithm for this setting, of independent interest,
with regret that grows only logarithmically with the number of actions and
linearly only in the number of potential outcomes (the latter being very small
in most auction settings). Last but not least, we show that our algorithm
outperforms the bandit approach experimentally and that this performance is
robust to dropping some of our theoretical assumptions or introducing noise in
the feedback that the bidder receives.
|
We address online learning in complex auction settings, such as sponsored search auctions, where the value of the bidder is unknown to her, evolving in an arbitrary manner and observed only if the bidder wins an allocation.
|
http://arxiv.org/abs/1711.01333v5
|
http://arxiv.org/pdf/1711.01333v5.pdf
| null |
[
"Zhe Feng",
"Chara Podimata",
"Vasilis Syrgkanis"
] |
[] | 2017-11-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-guide-to-constraining-effective-field
|
1805.00020
| null | null |
A Guide to Constraining Effective Field Theories with Machine Learning
|
We develop, discuss, and compare several inference techniques to constrain
theory parameters in collider experiments. By harnessing the latent-space
structure of particle physics processes, we extract extra information from the
simulator. This augmented data can be used to train neural networks that
precisely estimate the likelihood ratio. The new methods scale well to many
observables and high-dimensional parameter spaces, do not require any
approximations of the parton shower and detector response, and can be evaluated
in microseconds. Using weak-boson-fusion Higgs production as an example
process, we compare the performance of several techniques. The best results are
found for likelihood ratio estimators trained with extra information about the
score, the gradient of the log likelihood function with respect to the theory
parameters. The score also provides sufficient statistics that contain all the
information needed for inference in the neighborhood of the Standard Model.
These methods enable us to put significantly stronger bounds on effective
dimension-six operators than the traditional approach based on histograms. They
also outperform generic machine learning methods that do not make use of the
particle physics structure, demonstrating their potential to substantially
improve the new physics reach of the LHC legacy results.
|
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments.
|
http://arxiv.org/abs/1805.00020v4
|
http://arxiv.org/pdf/1805.00020v4.pdf
| null |
[
"Johann Brehmer",
"Kyle Cranmer",
"Gilles Louppe",
"Juan Pavez"
] |
[
"BIG-bench Machine Learning"
] | 2018-04-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/large-margin-classification-in-hyperbolic
|
1806.00437
| null | null |
Large-Margin Classification in Hyperbolic Space
|
Representing data in hyperbolic space can effectively capture latent
hierarchical relationships. With the goal of enabling accurate classification
of points in hyperbolic space while respecting their hyperbolic geometry, we
introduce hyperbolic SVM, a hyperbolic formulation of support vector machine
classifiers, and elucidate through new theoretical work its connection to the
Euclidean counterpart. We demonstrate the performance improvement of hyperbolic
SVM for multi-class prediction tasks on real-world complex networks as well as
simulated datasets. Our work allows analytic pipelines that take the inherent
hyperbolic geometry of the data into account in an end-to-end fashion without
resorting to ill-fitting tools developed for Euclidean space.
|
Representing data in hyperbolic space can effectively capture latent hierarchical relationships.
|
http://arxiv.org/abs/1806.00437v1
|
http://arxiv.org/pdf/1806.00437v1.pdf
| null |
[
"Hyunghoon Cho",
"Benjamin DeMeo",
"Jian Peng",
"Bonnie Berger"
] |
[
"Classification",
"General Classification"
] | 2018-06-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **Support Vector Machine**, or **SVM**, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs to high-dimensional feature spaces. SVMs construct a hyper-plane or set of hyper-planes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyper-plane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. The figure to the right shows the decision function for a linearly separable problem, with three samples on the margin boundaries, called “support vectors”. \r\n\r\nSource: [scikit-learn](https://scikit-learn.org/stable/modules/svm.html)",
"full_name": "Support Vector Machine",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "SVM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/constraining-effective-field-theories-with
|
1805.00013
| null | null |
Constraining Effective Field Theories with Machine Learning
|
We present powerful new analysis techniques to constrain effective field
theories at the LHC. By leveraging the structure of particle physics processes,
we extract extra information from Monte-Carlo simulations, which can be used to
train neural network models that estimate the likelihood ratio. These methods
scale well to processes with many observables and theory parameters, do not
require any approximations of the parton shower or detector response, and can
be evaluated in microseconds. We show that they allow us to put significantly
stronger bounds on dimension-six operators than existing methods, demonstrating
their potential to improve the precision of the LHC legacy constraints.
|
We present powerful new analysis techniques to constrain effective field theories at the LHC.
|
http://arxiv.org/abs/1805.00013v4
|
http://arxiv.org/pdf/1805.00013v4.pdf
| null |
[
"Johann Brehmer",
"Kyle Cranmer",
"Gilles Louppe",
"Juan Pavez"
] |
[
"BIG-bench Machine Learning"
] | 2018-04-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-classification-approach-towards
|
1806.00428
| null | null |
A Classification approach towards Unsupervised Learning of Visual Representations
|
In this paper, we present a technique for unsupervised learning of visual
representations. Specifically, we train a model for foreground and background
classification task, in the process of which it learns visual representations.
Foreground and background patches for training come af- ter mining for such
patches from hundreds and thousands of unlabelled videos available on the web
which we ex- tract using a proposed patch extraction algorithm. With- out using
any supervision, with just using 150, 000 unla- belled videos and the PASCAL
VOC 2007 dataset, we train a object recognition model that achieves 45.3 mAP
which is close to the best performing unsupervised feature learn- ing technique
whereas better than many other proposed al- gorithms. The code for patch
extraction is implemented in Matlab and available open source at the following
link .
|
In this paper, we present a technique for unsupervised learning of visual representations.
|
http://arxiv.org/abs/1806.00428v1
|
http://arxiv.org/pdf/1806.00428v1.pdf
| null |
[
"Aditya Vora"
] |
[
"Classification",
"General Classification",
"Object Recognition"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/solving-stochastic-differential-equations-and
|
1806.00421
| null | null |
Solving the Kolmogorov PDE by means of deep learning
|
Stochastic differential equations (SDEs) and the Kolmogorov partial differential equations (PDEs) associated to them have been widely used in models from engineering, finance, and the natural sciences. In particular, SDEs and Kolmogorov PDEs, respectively, are highly employed in models for the approximative pricing of financial derivatives. Kolmogorov PDEs and SDEs, respectively, can typically not be solved explicitly and it has been and still is an active topic of research to design and analyze numerical methods which are able to approximately solve Kolmogorov PDEs and SDEs, respectively. Nearly all approximation methods for Kolmogorov PDEs in the literature suffer under the curse of dimensionality or only provide approximations of the solution of the PDE at a single fixed space-time point. In this paper we derive and propose a numerical approximation method which aims to overcome both of the above mentioned drawbacks and intends to deliver a numerical approximation of the Kolmogorov PDE on an entire region $[a,b]^d$ without suffering from the curse of dimensionality. Numerical results on examples including the heat equation, the Black-Scholes model, the stochastic Lorenz equation, and the Heston model suggest that the proposed approximation algorithm is quite effective in high dimensions in terms of both accuracy and speed.
| null |
https://arxiv.org/abs/1806.00421v2
|
https://arxiv.org/pdf/1806.00421v2.pdf
| null |
[
"Christian Beck",
"Sebastian Becker",
"Philipp Grohs",
"Nor Jaafari",
"Arnulf Jentzen"
] |
[
"Deep Learning"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/whitening-and-coloring-batch-transform-for
|
1806.00420
| null | null |
Whitening and Coloring batch transform for GANs
|
Batch Normalization (BN) is a common technique used to speed-up and stabilize
training. On the other hand, the learnable parameters of BN are commonly used
in conditional Generative Adversarial Networks (cGANs) for representing
class-specific information using conditional Batch Normalization (cBN). In this
paper we propose to generalize both BN and cBN using a Whitening and Coloring
based batch normalization. We show that our conditional Coloring can represent
categorical conditioning information which largely helps the cGAN qualitative
results. Moreover, we show that full-feature whitening is important in a
general GAN scenario in which the training process is known to be highly
unstable. We test our approach on different datasets and using different GAN
networks and training protocols, showing a consistent improvement in all the
tested frameworks. Our CIFAR-10 conditioned results are higher than all
previous works on this dataset.
|
In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization.
|
http://arxiv.org/abs/1806.00420v2
|
http://arxiv.org/pdf/1806.00420v2.pdf
|
ICLR 2019 5
|
[
"Aliaksandr Siarohin",
"Enver Sangineto",
"Nicu Sebe"
] |
[
"Image Generation"
] | 2018-06-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "A **Feedforward Network**, or a **Multilayer Perceptron (MLP)**, is a neural network with solely densely connected layers. This is the classic neural network architecture of the literature. It consists of inputs $x$ passed through units $h$ (of which there can be many layers) to predict a target $y$. Activation functions are generally chosen to be non-linear to allow for flexible functional approximation.\r\n\r\nImage Source: Deep Learning, Goodfellow et al",
"full_name": "Feedforward Network",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Feedforward Network",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/ap229997/Conditional-Batch-Norm/blob/6e237ed5794246e1bbbe95bbda9acf81d0cdeace/model/cbn.py#L9",
"description": "**Conditional Batch Normalization (CBN)** is a class-conditional variant of [batch normalization](https://paperswithcode.com/method/batch-normalization). The key idea is to predict the $\\gamma$ and $\\beta$ of the batch normalization from an embedding - e.g. a language embedding in VQA. CBN enables the linguistic embedding to manipulate entire feature maps by scaling them up or down, negating them, or shutting them off. CBN has also been used in [GANs](https://paperswithcode.com/methods/category/generative-adversarial-networks) to allow class information to affect the batch normalization parameters.\r\n\r\nConsider a single convolutional layer with batch normalization module $\\text{BN}\\left(F\\_{i,c,h,w}|\\gamma\\_{c}, \\beta\\_{c}\\right)$ for which pretrained scalars $\\gamma\\_{c}$ and $\\beta\\_{c}$ are available. We would like to directly predict these affine scaling parameters from, e.g., a language embedding $\\mathbf{e\\_{q}}$. When starting the training procedure, these parameters must be close to the pretrained values to recover the original [ResNet](https://paperswithcode.com/method/resnet) model as a poor initialization could significantly deteriorate performance. Unfortunately, it is difficult to initialize a network to output the pretrained $\\gamma$ and $\\beta$. For these reasons, the authors propose to predict a change $\\delta\\beta\\_{c}$ and $\\delta\\gamma\\_{c}$ on the frozen original scalars, for which it is straightforward to initialize a neural network to produce an output with zero-mean and small variance.\r\n\r\nThe authors use a one-hidden-layer MLP to predict these deltas from a question embedding $\\mathbf{e\\_{q}}$ for all feature maps within the layer:\r\n\r\n$$\\Delta\\beta = \\text{MLP}\\left(\\mathbf{e\\_{q}}\\right)$$\r\n\r\n$$\\Delta\\gamma = \\text{MLP}\\left(\\mathbf{e\\_{q}}\\right)$$\r\n\r\nSo, given a feature map with $C$ channels, these MLPs output a vector of size $C$. We then add these predictions to the $\\beta$ and $\\gamma$ parameters:\r\n\r\n$$ \\hat{\\beta}\\_{c} = \\beta\\_{c} + \\Delta\\beta\\_{c} $$\r\n\r\n$$ \\hat{\\gamma}\\_{c} = \\gamma\\_{c} + \\Delta\\gamma\\_{c} $$\r\n\r\nFinally, these updated $\\hat{β}$ and $\\hat{\\gamma}$ are used as parameters for the batch normalization: $\\text{BN}\\left(F\\_{i,c,h,w}|\\hat{\\gamma\\_{c}}, \\hat{\\beta\\_{c}}\\right)$. The authors freeze all ResNet parameters, including $\\gamma$ and $\\beta$, during training. A ResNet consists of\r\nfour stages of computation, each subdivided in several residual blocks. In each block, the authors apply CBN to the three convolutional layers.",
"full_name": "Conditional Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Conditional Batch Normalization",
"source_title": "Modulating early visual processing by language",
"source_url": "http://arxiv.org/abs/1707.00683v3"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Dogecoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're trying to recover a lost Dogecoin wallet, knowing where to get help is essential. That’s why the Dogecoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Dogecoin Customer Support Number +1-833-534-1729\r\nDogecoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. 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A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Dogecoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Dogecoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Dogecoin Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Dogecoin tech.\r\n\r\n24/7 Availability: Dogecoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Dogecoin Support and Wallet Issues\r\nQ1: Can Dogecoin support help me recover stolen BTC?\r\nA: While Dogecoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Dogecoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Dogecoin’s official number (Dogecoin is decentralized), it connects you to trained professionals experienced in resolving all major Dogecoin issues.\r\n\r\nFinal Thoughts\r\nDogecoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Dogecoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Dogecoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Dogecoin Customer Service Number +1-833-534-1729",
"source_title": "Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1406.2661v1"
}
] |
https://paperswithcode.com/paper/end-to-end-active-object-tracking-via
|
1705.10561
| null | null |
End-to-end Active Object Tracking via Reinforcement Learning
|
We study active object tracking, where a tracker takes as input the visual
observation (i.e., frame sequence) and produces the camera control signal
(e.g., move forward, turn left, etc.). Conventional methods tackle the tracking
and the camera control separately, which is challenging to tune jointly. It
also incurs many human efforts for labeling and many expensive trial-and-errors
in realworld. To address these issues, we propose, in this paper, an end-to-end
solution via deep reinforcement learning, where a ConvNet-LSTM function
approximator is adopted for the direct frame-toaction prediction. We further
propose an environment augmentation technique and a customized reward function,
which are crucial for a successful training. The tracker trained in simulators
(ViZDoom, Unreal Engine) shows good generalization in the case of unseen object
moving path, unseen object appearance, unseen background, and distracting
object. It can restore tracking when occasionally losing the target. With the
experiments over the VOT dataset, we also find that the tracking ability,
obtained solely from simulators, can potentially transfer to real-world
scenarios.
| null |
http://arxiv.org/abs/1705.10561v3
|
http://arxiv.org/pdf/1705.10561v3.pdf
|
ICML 2018 7
|
[
"Wenhan Luo",
"Peng Sun",
"Fangwei Zhong",
"Wei Liu",
"Tong Zhang",
"Yizhou Wang"
] |
[
"Deep Reinforcement Learning",
"Object",
"Object Tracking",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2017-05-30T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1889
|
http://proceedings.mlr.press/v80/luo18a/luo18a.pdf
|
end-to-end-active-object-tracking-via-1
| null |
[] |
https://paperswithcode.com/paper/global-linear-convergence-of-newtons-method
|
1806.00413
| null | null |
Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients
|
We show that Newton's method converges globally at a linear rate for
objective functions whose Hessians are stable. This class of problems includes
many functions which are not strongly convex, such as logistic regression. Our
linear convergence result is (i) affine-invariant, and holds even if an (ii)
approximate Hessian is used, and if the subproblems are (iii) only solved
approximately. Thus we theoretically demonstrate the superiority of Newton's
method over first-order methods, which would only achieve a sublinear
$O(1/t^2)$ rate under similar conditions.
| null |
http://arxiv.org/abs/1806.00413v1
|
http://arxiv.org/pdf/1806.00413v1.pdf
| null |
[
"Sai Praneeth Karimireddy",
"Sebastian U. Stich",
"Martin Jaggi"
] |
[
"regression"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/oversegmenting-graphs
|
1806.00411
| null | null |
Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning
|
We propose a novel iterative method to adapt a a graph to d-dimensional image data. The method drives the nodes of the graph towards image features. The adaptation process naturally lends itself to a measure of feature saliency which can then be used to retain meaningful nodes and edges in the graph. From the adapted graph, we also propose the computation of a dual graph, which inherits the saliency measure from the adapted graph, and whose edges run along image features, hence producing an oversegmenting graph. The proposed method is computationally efficient and fully parallelisable. We propose two distance measures to find image saliency along graph edges, and evaluate the performance on synthetic images and on natural images from publicly available databases. In both cases, the most salient nodes of the graph achieve average boundary recall over 90%. We also apply our method to image classification on the MNIST hand-written digit dataset, using a recently proposed Deep Geometric Learning architecture, and achieving state-of-the-art classification accuracy, for a graph-based method, of 97.86%.
| null |
https://arxiv.org/abs/1806.00411v2
|
https://arxiv.org/pdf/1806.00411v2.pdf
| null |
[
"Alberto Gomez",
"Veronika A. Zimmer",
"Bishesh Khanal",
"Nicolas Toussaint",
"Julia A. Schnabel"
] |
[
"Clustering",
"Deep Learning",
"General Classification",
"image-classification",
"Image Classification"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/inverting-supervised-representations-with
|
1806.00400
| null | null |
Inverting Supervised Representations with Autoregressive Neural Density Models
|
We present a method for feature interpretation that makes use of recent
advances in autoregressive density estimation models to invert model
representations. We train generative inversion models to express a distribution
over input features conditioned on intermediate model representations. Insights
into the invariances learned by supervised models can be gained by viewing
samples from these inversion models. In addition, we can use these inversion
models to estimate the mutual information between a model's inputs and its
intermediate representations, thus quantifying the amount of information
preserved by the network at different stages. Using this method we examine the
types of information preserved at different layers of convolutional neural
networks, and explore the invariances induced by different architectural
choices. Finally we show that the mutual information between inputs and network
layers decreases over the course of training, supporting recent work by
Shwartz-Ziv and Tishby (2017) on the information bottleneck theory of deep
learning.
| null |
http://arxiv.org/abs/1806.00400v2
|
http://arxiv.org/pdf/1806.00400v2.pdf
| null |
[
"Charlie Nash",
"Nate Kushman",
"Christopher K. I. Williams"
] |
[
"Density Estimation"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/radio-galaxy-morphology-generation-using-dnn
|
1806.00398
| null | null |
Radio Galaxy Morphology Generation Using DNN Autoencoder and Gaussian Mixture Models
|
The morphology of a radio galaxy is highly affected by its central active
galactic nuclei (AGN), which is studied to reveal the evolution of the super
massive black hole (SMBH). In this work, we propose a morphology generation
framework for two typical radio galaxies namely Fanaroff-Riley type-I (FRI) and
type-II (FRII) with deep neural network based autoencoder (DNNAE) and Gaussian
mixture models (GMMs). The encoder and decoder subnets in the DNNAE are
symmetric aside a fully-connected layer namely code layer hosting the extracted
feature vectors. By randomly generating the feature vectors later with a
three-component Gaussian Mixture models, new FRI or FRII radio galaxy
morphologies are simulated. Experiments were demonstrated on real radio galaxy
images, where we discussed the length of feature vectors, selection of lost
functions, and made comparisons on batch normalization and dropout techniques
for training the network. The results suggest a high efficiency and performance
of our morphology generation framework. Code is available at:
https://github.com/myinxd/dnnae-gmm.
|
In this work, we propose a morphology generation framework for two typical radio galaxies namely Fanaroff-Riley type-I (FRI) and type-II (FRII) with deep neural network based autoencoder (DNNAE) and Gaussian mixture models (GMMs).
|
http://arxiv.org/abs/1806.00398v1
|
http://arxiv.org/pdf/1806.00398v1.pdf
| null |
[
"Zhixian Ma",
"Jie Zhu",
"Weitian Li",
"Haiguang Xu"
] |
[
"Decoder"
] | 2018-06-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Solana transaction not confirmed, your Solana wallet not showing balance, or you're trying to recover a lost Solana wallet, knowing where to get help is essential. That’s why the Solana customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Solana Customer Support Number +1-833-534-1729\r\nSolana operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Solana Transaction Not Confirmed\r\nOne of the most common concerns is when a Solana transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Solana Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Solana wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Solana Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Solana wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Solana Deposit Not Received\r\nIf someone has sent you Solana but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Solana deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Solana Transaction Stuck or Pending\r\nSometimes your Solana transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Solana Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Solana wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Solana Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Solana tech.\r\n\r\n24/7 Availability: Solana doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Solana Support and Wallet Issues\r\nQ1: Can Solana support help me recover stolen BTC?\r\nA: While Solana transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Solana transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Solana’s official number (Solana is decentralized), it connects you to trained professionals experienced in resolving all major Solana issues.\r\n\r\nFinal Thoughts\r\nSolana is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Solana transaction not confirmed, your Solana wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Solana customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Solana Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Solana Customer Service Number +1-833-534-1729",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
}
] |
https://paperswithcode.com/paper/image-to-image-translation-for-cross-domain
|
1805.09730
| null | null |
Image-to-image translation for cross-domain disentanglement
|
Deep image translation methods have recently shown excellent results,
outputting high-quality images covering multiple modes of the data
distribution. There has also been increased interest in disentangling the
internal representations learned by deep methods to further improve their
performance and achieve a finer control. In this paper, we bridge these two
objectives and introduce the concept of cross-domain disentanglement. We aim to
separate the internal representation into three parts. The shared part contains
information for both domains. The exclusive parts, on the other hand, contain
only factors of variation that are particular to each domain. We achieve this
through bidirectional image translation based on Generative Adversarial
Networks and cross-domain autoencoders, a novel network component. Our model
offers multiple advantages. We can output diverse samples covering multiple
modes of the distributions of both domains, perform domain-specific image
transfer and interpolation, and cross-domain retrieval without the need of
labeled data, only paired images. We compare our model to the state-of-the-art
in multi-modal image translation and achieve better results for translation on
challenging datasets as well as for cross-domain retrieval on realistic
datasets.
|
We compare our model to the state-of-the-art in multi-modal image translation and achieve better results for translation on challenging datasets as well as for cross-domain retrieval on realistic datasets.
|
http://arxiv.org/abs/1805.09730v3
|
http://arxiv.org/pdf/1805.09730v3.pdf
|
NeurIPS 2018 12
|
[
"Abel Gonzalez-Garcia",
"Joost Van de Weijer",
"Yoshua Bengio"
] |
[
"Disentanglement",
"Image-to-Image Translation",
"Retrieval",
"Translation"
] | 2018-05-24T00:00:00 |
http://papers.nips.cc/paper/7404-image-to-image-translation-for-cross-domain-disentanglement
|
http://papers.nips.cc/paper/7404-image-to-image-translation-for-cross-domain-disentanglement.pdf
|
image-to-image-translation-for-cross-domain-1
| null |
[] |
https://paperswithcode.com/paper/persistence-paths-and-signature-features-in
|
1806.00381
| null | null |
Persistence paths and signature features in topological data analysis
|
We introduce a new feature map for barcodes that arise in persistent homology
computation. The main idea is to first realize each barcode as a path in a
convenient vector space, and to then compute its path signature which takes
values in the tensor algebra of that vector space. The composition of these two
operations - barcode to path, path to tensor series - results in a feature map
that has several desirable properties for statistical learning, such as
universality and characteristicness, and achieves state-of-the-art results on
common classification benchmarks.
| null |
http://arxiv.org/abs/1806.00381v2
|
http://arxiv.org/pdf/1806.00381v2.pdf
| null |
[
"Ilya Chevyrev",
"Vidit Nanda",
"Harald Oberhauser"
] |
[
"General Classification",
"tensor algebra",
"Topological Data Analysis"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/proportional-volume-sampling-and
|
1802.08318
| null | null |
Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design
|
We study the optimal design problems where the goal is to choose a set of
linear measurements to obtain the most accurate estimate of an unknown vector
in $d$ dimensions. We study the $A$-optimal design variant where the objective
is to minimize the average variance of the error in the maximum likelihood
estimate of the vector being measured. The problem also finds applications in
sensor placement in wireless networks, sparse least squares regression, feature
selection for $k$-means clustering, and matrix approximation. In this paper, we
introduce proportional volume sampling to obtain improved approximation
algorithms for $A$-optimal design. Our main result is to obtain improved
approximation algorithms for the $A$-optimal design problem by introducing the
proportional volume sampling algorithm. Our results nearly optimal bounds in
the asymptotic regime when the number of measurements done, $k$, is
significantly more than the dimension $d$. We also give first approximation
algorithms when $k$ is small including when $k=d$. The proportional
volume-sampling algorithm also gives approximation algorithms for other optimal
design objectives such as $D$-optimal design and generalized ratio objective
matching or improving previous best known results. Interestingly, we show that
a similar guarantee cannot be obtained for the $E$-optimal design problem. We
also show that the $A$-optimal design problem is NP-hard to approximate within
a fixed constant when $k=d$.
| null |
http://arxiv.org/abs/1802.08318v5
|
http://arxiv.org/pdf/1802.08318v5.pdf
| null |
[
"Aleksandar Nikolov",
"Mohit Singh",
"Uthaipon Tao Tantipongpipat"
] |
[
"Clustering",
"feature selection"
] | 2018-02-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/nonlinear-acceleration-of-cnns
|
1806.00370
| null | null |
Nonlinear Acceleration of CNNs
|
The Regularized Nonlinear Acceleration (RNA) algorithm is an acceleration
method capable of improving the rate of convergence of many optimization
schemes such as gradient descend, SAGA or SVRG. Until now, its analysis is
limited to convex problems, but empirical observations shows that RNA may be
extended to wider settings. In this paper, we investigate further the benefits
of RNA when applied to neural networks, in particular for the task of image
recognition on CIFAR10 and ImageNet. With very few modifications of exiting
frameworks, RNA improves slightly the optimization process of CNNs, after
training.
|
The Regularized Nonlinear Acceleration (RNA) algorithm is an acceleration method capable of improving the rate of convergence of many optimization schemes such as gradient descend, SAGA or SVRG.
|
http://arxiv.org/abs/1806.00370v1
|
http://arxiv.org/pdf/1806.00370v1.pdf
| null |
[
"Damien Scieur",
"Edouard Oyallon",
"Alexandre d'Aspremont",
"Francis Bach"
] |
[] | 2018-06-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "SAGA is a method in the spirit of SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient algorithms with fast linear convergence rates. SAGA improves on the theory behind SAG and SVRG, with better theoretical convergence rates, and has support for composite objectives where a proximal operator is used on the regulariser. Unlike SDCA, SAGA supports non-strongly convex problems directly, and is adaptive to any inherent strong convexity of the problem.",
"full_name": "SAGA",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Optimization",
"parent": null
},
"name": "SAGA",
"source_title": "SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives",
"source_url": "http://arxiv.org/abs/1407.0202v3"
}
] |
https://paperswithcode.com/paper/gep-pg-decoupling-exploration-and
|
1802.05054
| null | null |
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
|
In continuous action domains, standard deep reinforcement learning algorithms
like DDPG suffer from inefficient exploration when facing sparse or deceptive
reward problems. Conversely, evolutionary and developmental methods focusing on
exploration like Novelty Search, Quality-Diversity or Goal Exploration
Processes explore more robustly but are less efficient at fine-tuning policies
using gradient descent. In this paper, we present the GEP-PG approach, taking
the best of both worlds by sequentially combining a Goal Exploration Process
and two variants of DDPG. We study the learning performance of these components
and their combination on a low dimensional deceptive reward problem and on the
larger Half-Cheetah benchmark. We show that DDPG fails on the former and that
GEP-PG improves over the best DDPG variant in both environments. Supplementary
videos and discussion can be found at http://frama.link/gep_pg, the code at
http://github.com/flowersteam/geppg.
|
In continuous action domains, standard deep reinforcement learning algorithms like DDPG suffer from inefficient exploration when facing sparse or deceptive reward problems.
|
http://arxiv.org/abs/1802.05054v5
|
http://arxiv.org/pdf/1802.05054v5.pdf
|
ICML 2018 7
|
[
"Cédric Colas",
"Olivier Sigaud",
"Pierre-Yves Oudeyer"
] |
[
"Deep Reinforcement Learning",
"Diversity",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-02-14T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2151
|
http://proceedings.mlr.press/v80/colas18a/colas18a.pdf
|
gep-pg-decoupling-exploration-and-1
| null |
[
{
"code_snippet_url": null,
"description": "**Experience Replay** is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, $e\\_{t} = \\left(s\\_{t}, a\\_{t}, r\\_{t}, s\\_{t+1}\\right)$ in a data-set $D = e\\_{1}, \\cdots, e\\_{N}$ , pooled over many episodes into a replay memory. We then usually sample the memory randomly for a minibatch of experience, and use this to learn off-policy, as with Deep Q-Networks. This tackles the problem of autocorrelation leading to unstable training, by making the problem more like a supervised learning problem.\r\n\r\nImage Credit: [Hands-On Reinforcement Learning with Python, Sudharsan Ravichandiran](https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781788836524)",
"full_name": "Experience Replay",
"introduced_year": 1993,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Replay Memory",
"parent": null
},
"name": "Experience Replay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Weight Decay**, or **$L_{2}$ Regularization**, is a regularization technique applied to the weights of a neural network. We minimize a loss function compromising both the primary loss function and a penalty on the $L\\_{2}$ Norm of the weights:\r\n\r\n$$L\\_{new}\\left(w\\right) = L\\_{original}\\left(w\\right) + \\lambda{w^{T}w}$$\r\n\r\nwhere $\\lambda$ is a value determining the strength of the penalty (encouraging smaller weights). \r\n\r\nWeight decay can be incorporated directly into the weight update rule, rather than just implicitly by defining it through to objective function. Often weight decay refers to the implementation where we specify it directly in the weight update rule (whereas L2 regularization is usually the implementation which is specified in the objective function).\r\n\r\nImage Source: Deep Learning, Goodfellow et al",
"full_name": "Weight Decay",
"introduced_year": 1943,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Weight Decay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": null,
"description": "**DDPG**, or **Deep Deterministic Policy Gradient**, is an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. It combines the actor-critic approach with insights from [DQNs](https://paperswithcode.com/method/dqn): in particular, the insights that 1) the network is trained off-policy with samples from a replay buffer to minimize correlations between samples, and 2) the network is trained with a target Q network to give consistent targets during temporal difference backups. DDPG makes use of the same ideas along with [batch normalization](https://paperswithcode.com/method/batch-normalization).",
"full_name": "Deep Deterministic Policy Gradient",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "**Policy Gradient Methods** try to optimize the policy function directly in reinforcement learning. This contrasts with, for example, Q-Learning, where the policy manifests itself as maximizing a value function. Below you can find a continuously updating catalog of policy gradient methods.",
"name": "Policy Gradient Methods",
"parent": null
},
"name": "DDPG",
"source_title": "Continuous control with deep reinforcement learning",
"source_url": "https://arxiv.org/abs/1509.02971v6"
}
] |
https://paperswithcode.com/paper/accurate-and-efficient-similarity-search-for
|
1806.00365
| null | null |
Accurate and Efficient Similarity Search for Large Scale Face Recognition
|
Face verification is a relatively easy task with the help of discriminative
features from deep neural networks. However, it is still a challenge to
recognize faces on millions of identities while keeping high performance and
efficiency. The challenge 2 of MS-Celeb-1M is a classification task. However,
the number of identities is too large and it is not that elegant to treat the
task as an image classification task. We treat the classification task as
similarity search and do experiments on different similarity search strategies.
Similarity search strategy accelerates the speed of searching and boosts the
accuracy of final results. The model used for extracting features is a single
deep neural network pretrained on CASIA-Webface, which is not trained on the
base set or novel set offered by official. Finally, we rank \textbf{3rd}, while
the speed of searching is 1ms/image.
| null |
http://arxiv.org/abs/1806.00365v1
|
http://arxiv.org/pdf/1806.00365v1.pdf
| null |
[
"Ce Qi",
"Zhi-Zhong Liu",
"Fei Su"
] |
[
"Classification",
"Face Recognition",
"Face Verification",
"General Classification",
"image-classification",
"Image Classification"
] | 2018-06-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/domain-adaptation-for-mri-organ-segmentation
|
1806.00363
| null | null |
Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy
|
The variations in multi-center data in medical imaging studies have brought
the necessity of domain adaptation. Despite the advancement of machine learning
in automatic segmentation, performance often degrades when algorithms are
applied on new data acquired from different scanners or sequences than the
training data. Manual annotation is costly and time consuming if it has to be
carried out for every new target domain. In this work, we investigate automatic
selection of suitable subjects to be annotated for supervised domain adaptation
using the concept of reverse classification accuracy (RCA). RCA predicts the
performance of a trained model on data from the new domain and different
strategies of selecting subjects to be included in the adaptation via transfer
learning are evaluated. We perform experiments on a two-center MR database for
the task of organ segmentation. We show that subject selection via RCA can
reduce the burden of annotation of new data for the target domain.
|
The variations in multi-center data in medical imaging studies have brought the necessity of domain adaptation.
|
http://arxiv.org/abs/1806.00363v1
|
http://arxiv.org/pdf/1806.00363v1.pdf
| null |
[
"Vanya V. Valindria",
"Ioannis Lavdas",
"Wenjia Bai",
"Konstantinos Kamnitsas",
"Eric O. Aboagye",
"Andrea G. Rockall",
"Daniel Rueckert",
"Ben Glocker"
] |
[
"Classification",
"Domain Adaptation",
"General Classification",
"Organ Segmentation",
"Segmentation",
"Transfer Learning"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-systematic-classification-of-knowledge
|
1806.00358
| null | null |
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset
|
The recent work of Clark et al. introduces the AI2 Reasoning Challenge (ARC)
and the associated ARC dataset that partitions open domain, complex science
questions into an Easy Set and a Challenge Set. That paper includes an analysis
of 100 questions with respect to the types of knowledge and reasoning required
to answer them; however, it does not include clear definitions of these types,
nor does it offer information about the quality of the labels. We propose a
comprehensive set of definitions of knowledge and reasoning types necessary for
answering the questions in the ARC dataset. Using ten annotators and a
sophisticated annotation interface, we analyze the distribution of labels
across the Challenge Set and statistics related to them. Additionally, we
demonstrate that although naive information retrieval methods return sentences
that are irrelevant to answering the query, sufficient supporting text is often
present in the (ARC) corpus. Evaluating with human-selected relevant sentences
improves the performance of a neural machine comprehension model by 42 points.
| null |
http://arxiv.org/abs/1806.00358v2
|
http://arxiv.org/pdf/1806.00358v2.pdf
|
WS 2018 7
|
[
"Michael Boratko",
"Harshit Padigela",
"Divyendra Mikkilineni",
"Pritish Yuvraj",
"Rajarshi Das",
"Andrew McCallum",
"Maria Chang",
"Achille Fokoue-Nkoutche",
"Pavan Kapanipathi",
"Nicholas Mattei",
"Ryan Musa",
"Kartik Talamadupula",
"Michael Witbrock"
] |
[
"AI2 Reasoning Challenge",
"ARC",
"General Classification",
"Information Retrieval",
"Reading Comprehension",
"Retrieval"
] | 2018-06-01T00:00:00 |
https://aclanthology.org/W18-2607
|
https://aclanthology.org/W18-2607.pdf
|
a-systematic-classification-of-knowledge-1
| null |
[] |
https://paperswithcode.com/paper/some-of-them-can-be-guessed-exploring-the
|
1806.00354
| null | null |
Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers
|
We study the role of linguistic context in predicting quantifiers (`few',
`all'). We collect crowdsourced data from human participants and test various
models in a local (single-sentence) and a global context (multi-sentence)
condition. Models significantly out-perform humans in the former setting and
are only slightly better in the latter. While human performance improves with
more linguistic context (especially on proportional quantifiers), model
performance suffers. Models are very effective in exploiting lexical and
morpho-syntactic patterns; humans are better at genuinely understanding the
meaning of the (global) context.
|
We study the role of linguistic context in predicting quantifiers (`few', `all').
|
http://arxiv.org/abs/1806.00354v1
|
http://arxiv.org/pdf/1806.00354v1.pdf
|
ACL 2018 7
|
[
"Sandro Pezzelle",
"Shane Steinert-Threlkeld",
"Raffaela Bernardi",
"Jakub Szymanik"
] |
[
"Sentence"
] | 2018-06-01T00:00:00 |
https://aclanthology.org/P18-2019
|
https://aclanthology.org/P18-2019.pdf
|
some-of-them-can-be-guessed-exploring-the-1
| null |
[] |
https://paperswithcode.com/paper/producing-radiologist-quality-reports-for
|
1806.00340
| null | null |
Producing radiologist-quality reports for interpretable artificial intelligence
|
Current approaches to explaining the decisions of deep learning systems for
medical tasks have focused on visualising the elements that have contributed to
each decision. We argue that such approaches are not enough to "open the black
box" of medical decision making systems because they are missing a key
component that has been used as a standard communication tool between doctors
for centuries: language. We propose a model-agnostic interpretability method
that involves training a simple recurrent neural network model to produce
descriptive sentences to clarify the decision of deep learning classifiers.
We test our method on the task of detecting hip fractures from frontal pelvic
x-rays. This process requires minimal additional labelling despite producing
text containing elements that the original deep learning classification model
was not specifically trained to detect.
The experimental results show that: 1) the sentences produced by our method
consistently contain the desired information, 2) the generated sentences are
preferred by doctors compared to current tools that create saliency maps, and
3) the combination of visualisations and generated text is better than either
alone.
| null |
http://arxiv.org/abs/1806.00340v1
|
http://arxiv.org/pdf/1806.00340v1.pdf
| null |
[
"William Gale",
"Luke Oakden-Rayner",
"Gustavo Carneiro",
"Andrew P. Bradley",
"Lyle J. Palmer"
] |
[
"Decision Making",
"Deep Learning",
"Descriptive"
] | 2018-06-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "Interpretability",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images for downstream tasks such as classification and object detection. The most popular subcategory are convolutional neural networks. Below you can find a continuously updated list of image models.",
"name": "Image Models",
"parent": null
},
"name": "Interpretability",
"source_title": "CAM: Causal additive models, high-dimensional order search and penalized regression",
"source_url": "http://arxiv.org/abs/1310.1533v2"
}
] |
https://paperswithcode.com/paper/structured-local-optima-in-sparse-blind
|
1806.00338
| null | null |
Structured Local Optima in Sparse Blind Deconvolution
|
Blind deconvolution is a ubiquitous problem of recovering two unknown signals from their convolution. Unfortunately, this is an ill-posed problem in general. This paper focuses on the {\em short and sparse} blind deconvolution problem, where the one unknown signal is short and the other one is sparsely and randomly supported. This variant captures the structure of the unknown signals in several important applications. We assume the short signal to have unit $\ell^2$ norm and cast the blind deconvolution problem as a nonconvex optimization problem over the sphere. We demonstrate that (i) in a certain region of the sphere, every local optimum is close to some shift truncation of the ground truth, and (ii) for a generic short signal of length $k$, when the sparsity of activation signal $\theta\lesssim k^{-2/3}$ and number of measurements $m\gtrsim poly(k)$, a simple initialization method together with a descent algorithm which escapes strict saddle points recovers a near shift truncation of the ground truth kernel.
| null |
https://arxiv.org/abs/1806.00338v2
|
https://arxiv.org/pdf/1806.00338v2.pdf
| null |
[
"Yuqian Zhang",
"Han-Wen Kuo",
"John Wright"
] |
[] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-reinforcement-learning-approach-to-age-of
|
1806.00336
| null | null |
A Reinforcement Learning Approach to Age of Information in Multi-User Networks
|
Scheduling the transmission of time-sensitive data to multiple users over
error-prone communication channels is studied with the goal of minimizing the
long-term average age of information (AoI) at the users under a constraint on
the average number of transmissions at the source node. After each
transmission, the source receives an instantaneous ACK/NACK feedback from the
intended receiver and decides on what time and to which user to transmit the
next update. The optimal scheduling policy is first studied under different
feedback mechanisms when the channel statistics are known; in particular, the
standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols are
considered. Then a reinforcement learning (RL) approach is introduced, which
does not assume any a priori information on the random processes governing the
channel states. Different RL methods are verified and compared through
numerical simulations.
| null |
http://arxiv.org/abs/1806.00336v1
|
http://arxiv.org/pdf/1806.00336v1.pdf
| null |
[
"Elif Tuğçe Ceran",
"Deniz Gündüz",
"András György"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"Scheduling"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/sockeye-a-toolkit-for-neural-machine
|
1712.05690
| null | null |
Sockeye: A Toolkit for Neural Machine Translation
|
We describe Sockeye (version 1.12), an open-source sequence-to-sequence
toolkit for Neural Machine Translation (NMT). Sockeye is a production-ready
framework for training and applying models as well as an experimental platform
for researchers. Written in Python and built on MXNet, the toolkit offers
scalable training and inference for the three most prominent encoder-decoder
architectures: attentional recurrent neural networks, self-attentional
transformers, and fully convolutional networks. Sockeye also supports a wide
range of optimizers, normalization and regularization techniques, and inference
improvements from current NMT literature. Users can easily run standard
training recipes, explore different model settings, and incorporate new ideas.
In this paper, we highlight Sockeye's features and benchmark it against other
NMT toolkits on two language arcs from the 2017 Conference on Machine
Translation (WMT): English-German and Latvian-English. We report competitive
BLEU scores across all three architectures, including an overall best score for
Sockeye's transformer implementation. To facilitate further comparison, we
release all system outputs and training scripts used in our experiments. The
Sockeye toolkit is free software released under the Apache 2.0 license.
|
Written in Python and built on MXNet, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural networks, self-attentional transformers, and fully convolutional networks.
|
http://arxiv.org/abs/1712.05690v2
|
http://arxiv.org/pdf/1712.05690v2.pdf
| null |
[
"Felix Hieber",
"Tobias Domhan",
"Michael Denkowski",
"David Vilar",
"Artem Sokolov",
"Ann Clifton",
"Matt Post"
] |
[
"Decoder",
"Machine Translation",
"NMT",
"Translation"
] | 2017-12-15T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "A **Linear Layer** is a projection $\\mathbf{XW + b}$.",
"full_name": "Linear Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Linear Layer",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": null,
"description": "**Position-Wise Feed-Forward Layer** is a type of [feedforward layer](https://www.paperswithcode.com/method/category/feedforwad-networks) consisting of two [dense layers](https://www.paperswithcode.com/method/dense-connections) that applies to the last dimension, which means the same dense layers are used for each position item in the sequence, so called position-wise.",
"full_name": "Position-Wise Feed-Forward Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Position-Wise Feed-Forward Layer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9",
"description": "**Multi-head Attention** is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension. Intuitively, multiple attention heads allows for attending to parts of the sequence differently (e.g. longer-term dependencies versus shorter-term dependencies). \r\n\r\n$$ \\text{MultiHead}\\left(\\textbf{Q}, \\textbf{K}, \\textbf{V}\\right) = \\left[\\text{head}\\_{1},\\dots,\\text{head}\\_{h}\\right]\\textbf{W}_{0}$$\r\n\r\n$$\\text{where} \\text{ head}\\_{i} = \\text{Attention} \\left(\\textbf{Q}\\textbf{W}\\_{i}^{Q}, \\textbf{K}\\textbf{W}\\_{i}^{K}, \\textbf{V}\\textbf{W}\\_{i}^{V} \\right) $$\r\n\r\nAbove $\\textbf{W}$ are all learnable parameter matrices.\r\n\r\nNote that [scaled dot-product attention](https://paperswithcode.com/method/scaled) is most commonly used in this module, although in principle it can be swapped out for other types of attention mechanism.\r\n\r\nSource: [Lilian Weng](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mechanisms)",
"full_name": "Multi-Head Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "Multi-Head Attention",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "",
"description": "",
"full_name": "Attention Is All You Need",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "If you're looking to get in touch with American Airlines fast, ☎️+1-801-(855)-(5905)or +1-804-853-9001✅ there are\r\nseveral efficient ways to reach their customer service team. The quickest method is to dial ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. American’s phone service ensures that you can speak with a live\r\nrepresentative promptly to resolve any issues or queries regarding your booking, reservation,\r\nor any changes, such as name corrections or ticket cancellations.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Attention",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
}
] |
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