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https://paperswithcode.com/paper/boredom-driven-curious-learning-by-homeo
|
1806.01502
| null | null |
Boredom-driven curious learning by Homeo-Heterostatic Value Gradients
|
This paper presents the Homeo-Heterostatic Value Gradients (HHVG) algorithm
as a formal account on the constructive interplay between boredom and curiosity
which gives rise to effective exploration and superior forward model learning.
We envisaged actions as instrumental in agent's own epistemic disclosure. This
motivated two central algorithmic ingredients: devaluation and devaluation
progress, both underpin agent's cognition concerning intrinsically generated
rewards. The two serve as an instantiation of homeostatic and heterostatic
intrinsic motivation. A key insight from our algorithm is that the two
seemingly opposite motivations can be reconciled---without which exploration
and information-gathering cannot be effectively carried out. We supported this
claim with empirical evidence, showing that boredom-enabled agents consistently
outperformed other curious or explorative agent variants in model building
benchmarks based on self-assisted experience accumulation.
|
This paper presents the Homeo-Heterostatic Value Gradients (HHVG) algorithm as a formal account on the constructive interplay between boredom and curiosity which gives rise to effective exploration and superior forward model learning.
|
http://arxiv.org/abs/1806.01502v1
|
http://arxiv.org/pdf/1806.01502v1.pdf
| null |
[
"Yen Yu",
"Acer Y. C. Chang",
"Ryota Kanai"
] |
[] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/information-aggregation-via-dynamic-routing
|
1806.01501
| null | null |
Information Aggregation via Dynamic Routing for Sequence Encoding
|
While much progress has been made in how to encode a text sequence into a
sequence of vectors, less attention has been paid to how to aggregate these
preceding vectors (outputs of RNN/CNN) into fixed-size encoding vector.
Usually, a simple max or average pooling is used, which is a bottom-up and
passive way of aggregation and lack of guidance by task information. In this
paper, we propose an aggregation mechanism to obtain a fixed-size encoding with
a dynamic routing policy. The dynamic routing policy is dynamically deciding
that what and how much information need be transferred from each word to the
final encoding of the text sequence. Following the work of Capsule Network, we
design two dynamic routing policies to aggregate the outputs of RNN/CNN
encoding layer into a final encoding vector. Compared to the other aggregation
methods, dynamic routing can refine the messages according to the state of
final encoding vector. Experimental results on five text classification tasks
show that our method outperforms other aggregating models by a significant
margin. Related source code is released on our github page.
|
The dynamic routing policy is dynamically deciding that what and how much information need be transferred from each word to the final encoding of the text sequence.
|
http://arxiv.org/abs/1806.01501v1
|
http://arxiv.org/pdf/1806.01501v1.pdf
|
COLING 2018 8
|
[
"Jingjing Gong",
"Xipeng Qiu",
"Shaojing Wang",
"Xuanjing Huang"
] |
[
"Sentiment Analysis",
"text-classification",
"Text Classification"
] | 2018-06-05T00:00:00 |
https://aclanthology.org/C18-1232
|
https://aclanthology.org/C18-1232.pdf
|
information-aggregation-via-dynamic-routing-2
| null |
[
{
"code_snippet_url": null,
"description": "A capsule is an activation vector that basically executes on its inputs some complex internal\r\ncomputations. Length of these activation vectors signifies the\r\nprobability of availability of a feature. Furthermore, the condition\r\nof the recognized element is encoded as the direction in which\r\nthe vector is pointing. In traditional, CNN uses Max pooling for\r\ninvariance activities of neurons, which is nothing except a minor\r\nchange in input and the neurons of output signal will remains\r\nsame.",
"full_name": "Capsule Network",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Neural Architecture Search** methods are search methods that seek to learn architectures for machine learning tasks, including the underlying build blocks. Below you can find a continuously updating list of neural architecture search algorithms. ",
"name": "Neural Architecture Search",
"parent": null
},
"name": "Capsule Network",
"source_title": "Dynamic Routing Between Capsules",
"source_url": "http://arxiv.org/abs/1710.09829v2"
},
{
"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
}
] |
https://paperswithcode.com/paper/fusion-graph-convolutional-networks
|
1805.12528
| null | null |
Fusion Graph Convolutional Networks
|
Semi-supervised node classification in attributed graphs, i.e., graphs with
node features, involves learning to classify unlabeled nodes given a partially
labeled graph. Label predictions are made by jointly modeling the node and its'
neighborhood features. State-of-the-art models for node classification on such
attributed graphs use differentiable recursive functions that enable
aggregation and filtering of neighborhood information from multiple hops. In
this work, we analyze the representation capacity of these models to regulate
information from multiple hops independently. From our analysis, we conclude
that these models despite being powerful, have limited representation capacity
to capture multi-hop neighborhood information effectively. Further, we also
propose a mathematically motivated, yet simple extension to existing graph
convolutional networks (GCNs) which has improved representation capacity. We
extensively evaluate the proposed model, F-GCN on eight popular datasets from
different domains. F-GCN outperforms the state-of-the-art models for
semi-supervised learning on six datasets while being extremely competitive on
the other two.
|
State-of-the-art models for node classification on such attributed graphs use differentiable recursive functions that enable aggregation and filtering of neighborhood information from multiple hops.
|
http://arxiv.org/abs/1805.12528v5
|
http://arxiv.org/pdf/1805.12528v5.pdf
| null |
[
"Priyesh Vijayan",
"Yash Chandak",
"Mitesh M. Khapra",
"Srinivasan Parthasarathy",
"Balaraman Ravindran"
] |
[
"General Classification",
"Node Classification"
] | 2018-05-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-image-compression-via-end-to-end
|
1806.01496
| null | null |
Deep Image Compression via End-to-End Learning
|
We present a lossy image compression method based on deep convolutional
neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and
JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same
bit rate. Currently, most of the CNNs based approaches train the network using
a L2 loss between the reconstructions and the ground-truths in the pixel
domain, which leads to over-smoothing results and visual quality degradation
especially at a very low bit rate. Therefore, we improve the subjective quality
with the combination of a perception loss and an adversarial loss additionally.
To achieve better rate-distortion optimization (RDO), we also introduce an
easy-to-hard transfer learning when adding quantization error and rate
constraint. Finally, we evaluate our method on public Kodak and the Test
Dataset P/M released by the Computer Vision Lab of ETH Zurich, resulting in
averaged 7.81% and 19.1% BD-rate reduction over BPG, respectively.
|
We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate.
|
http://arxiv.org/abs/1806.01496v1
|
http://arxiv.org/pdf/1806.01496v1.pdf
| null |
[
"Haojie Liu",
"Tong Chen",
"Qiu Shen",
"Tao Yue",
"Zhan Ma"
] |
[
"Image Compression",
"MS-SSIM",
"Quantization",
"SSIM",
"Transfer Learning"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/spatially-localized-atlas-network-tiles
|
1806.00546
| null | null |
Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
|
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is
essential in non-invasive investigation for neuroanatomy. Historically,
multi-atlas segmentation (MAS) has been regarded as the de facto standard
method for whole brain segmentation. Recently, deep neural network approaches
have been applied to whole brain segmentation by learning random patches or 2D
slices. Yet, few previous efforts have been made on detailed whole brain
segmentation using 3D networks due to the following challenges: (1) fitting
entire whole brain volume into 3D networks is restricted by the current GPU
memory, and (2) the large number of targeting labels (e.g., > 100 labels) with
limited number of training 3D volumes (e.g., < 50 scans). In this paper, we
propose the spatially localized atlas network tiles (SLANT) method to
distribute multiple independent 3D fully convolutional networks to cover
overlapped sub-spaces in a standard atlas space. This strategy simplifies the
whole brain learning task to localized sub-tasks, which was enabled by combing
canonical registration and label fusion techniques with deep learning. To
address the second challenge, auxiliary labels on 5111 initially unlabeled
scans were created by MAS for pre-training. From empirical validation, the
state-of-the-art MAS method achieved mean Dice value of 0.76, 0.71, and 0.68,
while the proposed method achieved 0.78, 0.73, and 0.71 on three validation
cohorts. Moreover, the computational time reduced from > 30 hours using MAS to
~15 minutes using the proposed method. The source code is available online
https://github.com/MASILab/SLANTbrainSeg
|
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy.
|
http://arxiv.org/abs/1806.00546v2
|
http://arxiv.org/pdf/1806.00546v2.pdf
| null |
[
"Yuankai Huo",
"Zhoubing Xu",
"Katherine Aboud",
"Prasanna Parvathaneni",
"Shunxing Bao",
"Camilo Bermudez",
"Susan M. Resnick",
"Laurie E. Cutting",
"Bennett A. Landman"
] |
[
"Brain Segmentation",
"GPU",
"Segmentation"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/segen-sample-ensemble-genetic-evolutional
|
1803.08631
| null | null |
SEGEN: Sample-Ensemble Genetic Evolutional Network Model
|
Deep learning, a rebranding of deep neural network research works, has
achieved a remarkable success in recent years. With multiple hidden layers,
deep learning models aim at computing the hierarchical feature representations
of the observational data. Meanwhile, due to its severe disadvantages in data
consumption, computational resources, parameter tuning costs and the lack of
result explainability, deep learning has also suffered from lots of criticism.
In this paper, we will introduce a new representation learning model, namely
"Sample-Ensemble Genetic Evolutionary Network" (SEGEN), which can serve as an
alternative approach to deep learning models. Instead of building one single
deep model, based on a set of sampled sub-instances, SEGEN adopts a
genetic-evolutionary learning strategy to build a group of unit models
generations by generations. The unit models incorporated in SEGEN can be either
traditional machine learning models or the recent deep learning models with a
much "narrower" and "shallower" architecture. The learning results of each
instance at the final generation will be effectively combined from each unit
model via diffusive propagation and ensemble learning strategies. From the
computational perspective, SEGEN requires far less data, fewer computational
resources and parameter tuning efforts, but has sound theoretic
interpretability of the learning process and results. Extensive experiments
have been done on several different real-world benchmark datasets, and the
experimental results obtained by SEGEN have demonstrated its advantages over
the state-of-the-art representation learning models.
|
Deep learning, a rebranding of deep neural network research works, has achieved a remarkable success in recent years.
|
http://arxiv.org/abs/1803.08631v2
|
http://arxiv.org/pdf/1803.08631v2.pdf
| null |
[
"Jiawei Zhang",
"Limeng Cui",
"Fisher B. Gouza"
] |
[
"Deep Learning",
"Ensemble Learning",
"model",
"Representation Learning"
] | 2018-03-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/super-resolution-estimation-of-cyclic-arrival
|
1610.09600
| null | null |
Super-resolution estimation of cyclic arrival rates
|
Exploiting the fact that most arrival processes exhibit cyclic behaviour, we
propose a simple procedure for estimating the intensity of a nonhomogeneous
Poisson process. The estimator is the super-resolution analogue to Shao 2010
and Shao & Lii 2011, which is a sum of $p$ sinusoids where $p$ and the
frequency, amplitude, and phase of each wave are not known and need to be
estimated. This results in an interpretable yet flexible specification that is
suitable for use in modelling as well as in high resolution simulations.
Our estimation procedure sits in between classic periodogram methods and
atomic/total variation norm thresholding. Through a novel use of window
functions in the point process domain, our approach attains super-resolution
without semidefinite programming. Under suitable conditions, finite sample
guarantees can be derived for our procedure. These resolve some open questions
and expand existing results in spectral estimation literature.
| null |
http://arxiv.org/abs/1610.09600v7
|
http://arxiv.org/pdf/1610.09600v7.pdf
| null |
[
"Ningyuan Chen",
"Donald K. K. Lee",
"Sahand Negahban"
] |
[
"Super-Resolution"
] | 2016-10-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/onsets-and-frames-dual-objective-piano
|
1710.11153
| null | null |
Onsets and Frames: Dual-Objective Piano Transcription
|
We advance the state of the art in polyphonic piano music transcription by
using a deep convolutional and recurrent neural network which is trained to
jointly predict onsets and frames. Our model predicts pitch onset events and
then uses those predictions to condition framewise pitch predictions. During
inference, we restrict the predictions from the framewise detector by not
allowing a new note to start unless the onset detector also agrees that an
onset for that pitch is present in the frame. We focus on improving onsets and
offsets together instead of either in isolation as we believe this correlates
better with human musical perception. Our approach results in over a 100%
relative improvement in note F1 score (with offsets) on the MAPS dataset.
Furthermore, we extend the model to predict relative velocities of normalized
audio which results in more natural-sounding transcriptions.
|
We advance the state of the art in polyphonic piano music transcription by using a deep convolutional and recurrent neural network which is trained to jointly predict onsets and frames.
|
http://arxiv.org/abs/1710.11153v2
|
http://arxiv.org/pdf/1710.11153v2.pdf
| null |
[
"Curtis Hawthorne",
"Erich Elsen",
"Jialin Song",
"Adam Roberts",
"Ian Simon",
"Colin Raffel",
"Jesse Engel",
"Sageev Oore",
"Douglas Eck"
] |
[
"Music Transcription"
] | 2017-10-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/gesf-a-universal-discriminative-mapping
|
1805.11182
| null | null |
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning
|
Graph embedding is a central problem in social network analysis and many
other applications, aiming to learn the vector representation for each node.
While most existing approaches need to specify the neighborhood and the
dependence form to the neighborhood, which may significantly degrades the
flexibility of representation, we propose a novel graph node embedding method
(namely GESF) via the set function technique. Our method can 1) learn an
arbitrary form of representation function from neighborhood, 2) automatically
decide the significance of neighbors at different distances, and 3) be applied
to heterogeneous graph embedding, which may contain multiple types of nodes.
Theoretical guarantee for the representation capability of our method has been
proved for general homogeneous and heterogeneous graphs and evaluation results
on benchmark data sets show that the proposed GESF outperforms the
state-of-the-art approaches on producing node vectors for classification tasks.
| null |
http://arxiv.org/abs/1805.11182v3
|
http://arxiv.org/pdf/1805.11182v3.pdf
| null |
[
"Shupeng Gui",
"Xiangliang Zhang",
"Shuang Qiu",
"Mingrui Wu",
"Jieping Ye",
"Ji Liu"
] |
[
"Graph Embedding",
"Graph Representation Learning",
"Representation Learning"
] | 2018-05-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-primer-on-causal-analysis
|
1806.01488
| null | null |
A Primer on Causal Analysis
|
We provide a conceptual map to navigate causal analysis problems. Focusing on
the case of discrete random variables, we consider the case of causal effect
estimation from observational data. The presented approaches apply also to
continuous variables, but the issue of estimation becomes more complex. We then
introduce the four schools of thought for causal analysis
| null |
http://arxiv.org/abs/1806.01488v1
|
http://arxiv.org/pdf/1806.01488v1.pdf
| null |
[
"Finnian Lattimore",
"Cheng Soon Ong"
] |
[
"Navigate"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/forecasting-crime-with-deep-learning
|
1806.01486
| null | null |
Forecasting Crime with Deep Learning
|
The objective of this work is to take advantage of deep neural networks in
order to make next day crime count predictions in a fine-grain city partition.
We make predictions using Chicago and Portland crime data, which is augmented
with additional datasets covering weather, census data, and public
transportation. The crime counts are broken into 10 bins and our model predicts
the most likely bin for a each spatial region at a daily level. We train this
data using increasingly complex neural network structures, including variations
that are suited to the spatial and temporal aspects of the crime prediction
problem. With our best model we are able to predict the correct bin for overall
crime count with 75.6% and 65.3% accuracy for Chicago and Portland,
respectively. The results show the efficacy of neural networks for the
prediction problem and the value of using external datasets in addition to
standard crime data.
| null |
http://arxiv.org/abs/1806.01486v1
|
http://arxiv.org/pdf/1806.01486v1.pdf
| null |
[
"Alexander Stec",
"Diego Klabjan"
] |
[
"Crime Prediction",
"Deep Learning"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/long-time-predictive-modeling-of-nonlinear
|
1805.12547
| null | null |
Long-time predictive modeling of nonlinear dynamical systems using neural networks
|
We study the use of feedforward neural networks (FNN) to develop models of
nonlinear dynamical systems from data. Emphasis is placed on predictions at
long times, with limited data availability. Inspired by global stability
analysis, and the observation of the strong correlation between the local error
and the maximum singular value of the Jacobian of the ANN, we introduce
Jacobian regularization in the loss function. This regularization suppresses
the sensitivity of the prediction to the local error and is shown to improve
accuracy and robustness. Comparison between the proposed approach and sparse
polynomial regression is presented in numerical examples ranging from simple
ODE systems to nonlinear PDE systems including vortex shedding behind a
cylinder, and instability-driven buoyant mixing flow. Furthermore, limitations
of feedforward neural networks are highlighted, especially when the training
data does not include a low dimensional attractor. Strategies of data
augmentation are presented as remedies to address these issues to a certain
extent.
| null |
http://arxiv.org/abs/1805.12547v5
|
http://arxiv.org/pdf/1805.12547v5.pdf
| null |
[
"Shaowu Pan",
"Karthik Duraisamy"
] |
[
"Data Augmentation"
] | 2018-05-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/r-instance-learning-for-missing-people-tweets
|
1805.10856
| null | null |
r-Instance Learning for Missing People Tweets Identification
|
The number of missing people (i.e., people who get lost) greatly increases in
recent years. It is a serious worldwide problem, and finding the missing people
consumes a large amount of social resources. In tracking and finding these
missing people, timely data gathering and analysis actually play an important
role. With the development of social media, information about missing people
can get propagated through the web very quickly, which provides a promising way
to solve the problem. The information in online social media is usually of
heterogeneous categories, involving both complex social interactions and
textual data of diverse structures. Effective fusion of these different types
of information for addressing the missing people identification problem can be
a great challenge. Motivated by the multi-instance learning problem and
existing social science theory of "homophily", in this paper, we propose a
novel r-instance (RI) learning model.
| null |
http://arxiv.org/abs/1805.10856v2
|
http://arxiv.org/pdf/1805.10856v2.pdf
| null |
[
"Yang Yang",
"Haoyan Liu",
"Xia Hu",
"Jiawei Zhang",
"Xiao-Ming Zhang",
"Zhoujun Li",
"Philip S. Yu"
] |
[] | 2018-05-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/3d-human-pose-estimation-with-2d-marginal
|
1806.01484
| null | null |
3D Human Pose Estimation with 2D Marginal Heatmaps
|
Automatically determining three-dimensional human pose from monocular RGB
image data is a challenging problem. The two-dimensional nature of the input
results in intrinsic ambiguities which make inferring depth particularly
difficult. Recently, researchers have demonstrated that the flexible
statistical modelling capabilities of deep neural networks are sufficient to
make such inferences with reasonable accuracy. However, many of these models
use coordinate output techniques which are memory-intensive, not
differentiable, and/or do not spatially generalise well. We propose
improvements to 3D coordinate prediction which avoid the aforementioned
undesirable traits by predicting 2D marginal heatmaps under an augmented
soft-argmax scheme. Our resulting model, MargiPose, produces visually coherent
heatmaps whilst maintaining differentiability. We are also able to achieve
state-of-the-art accuracy on publicly available 3D human pose estimation data.
|
Automatically determining three-dimensional human pose from monocular RGB image data is a challenging problem.
|
http://arxiv.org/abs/1806.01484v2
|
http://arxiv.org/pdf/1806.01484v2.pdf
| null |
[
"Aiden Nibali",
"Zhen He",
"Stuart Morgan",
"Luke Prendergast"
] |
[
"3D Human Pose Estimation",
"Pose Estimation"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/jtav-jointly-learning-social-media-content
|
1806.01483
| null | null |
JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features
|
Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others. In contrast with previous works that focus mainly on single modal or bi-modal learning, we propose to learn social media content by fusing jointly textual, acoustic, and visual information (JTAV). Effective strategies are proposed to extract fine-grained features of each modality, that is, attBiGRU and DCRNN. We also introduce cross-modal fusion and attentive pooling techniques to integrate multi-modal information comprehensively. Extensive experimental evaluation conducted on real-world datasets demonstrates our proposed model outperforms the state-of-the-art approaches by a large margin.
|
Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others.
|
https://arxiv.org/abs/1806.01483v2
|
https://arxiv.org/pdf/1806.01483v2.pdf
|
COLING 2018 8
|
[
"Hongru Liang",
"Haozheng Wang",
"Jun Wang",
"ShaoDi You",
"Zhe Sun",
"Jin-Mao Wei",
"Zhenglu Yang"
] |
[
"Information Retrieval",
"Recommendation Systems",
"Retrieval"
] | 2018-06-05T00:00:00 |
https://aclanthology.org/C18-1108
|
https://aclanthology.org/C18-1108.pdf
|
jtav-jointly-learning-social-media-content-1
| null |
[] |
https://paperswithcode.com/paper/sophie-an-attentive-gan-for-predicting-paths
|
1806.01482
| null | null |
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints
|
This paper addresses the problem of path prediction for multiple interacting
agents in a scene, which is a crucial step for many autonomous platforms such
as self-driving cars and social robots. We present \textit{SoPhie}; an
interpretable framework based on Generative Adversarial Network (GAN), which
leverages two sources of information, the path history of all the agents in a
scene, and the scene context information, using images of the scene. To predict
a future path for an agent, both physical and social information must be
leveraged. Previous work has not been successful to jointly model physical and
social interactions. Our approach blends a social attention mechanism with a
physical attention that helps the model to learn where to look in a large scene
and extract the most salient parts of the image relevant to the path. Whereas,
the social attention component aggregates information across the different
agent interactions and extracts the most important trajectory information from
the surrounding neighbors. SoPhie also takes advantage of GAN to generates more
realistic samples and to capture the uncertain nature of the future paths by
modeling its distribution. All these mechanisms enable our approach to predict
socially and physically plausible paths for the agents and to achieve
state-of-the-art performance on several different trajectory forecasting
benchmarks.
|
Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors.
|
http://arxiv.org/abs/1806.01482v2
|
http://arxiv.org/pdf/1806.01482v2.pdf
|
CVPR 2019 6
|
[
"Amir Sadeghian",
"Vineet Kosaraju",
"Ali Sadeghian",
"Noriaki Hirose",
"S. Hamid Rezatofighi",
"Silvio Savarese"
] |
[
"Generative Adversarial Network",
"Self-Driving Cars",
"Trajectory Forecasting",
"Trajectory Prediction"
] | 2018-06-05T00:00:00 |
http://openaccess.thecvf.com/content_CVPR_2019/html/Sadeghian_SoPhie_An_Attentive_GAN_for_Predicting_Paths_Compliant_to_Social_CVPR_2019_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2019/papers/Sadeghian_SoPhie_An_Attentive_GAN_for_Predicting_Paths_Compliant_to_Social_CVPR_2019_paper.pdf
|
sophie-an-attentive-gan-for-predicting-paths-1
| 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": "",
"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/deep-learning-for-iot-big-data-and-streaming
|
1712.04301
| null | null |
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
|
In the era of the Internet of Things (IoT), an enormous amount of sensing
devices collect and/or generate various sensory data over time for a wide range
of fields and applications. Based on the nature of the application, these
devices will result in big or fast/real-time data streams. Applying analytics
over such data streams to discover new information, predict future insights,
and make control decisions is a crucial process that makes IoT a worthy
paradigm for businesses and a quality-of-life improving technology. In this
paper, we provide a thorough overview on using a class of advanced machine
learning techniques, namely Deep Learning (DL), to facilitate the analytics and
learning in the IoT domain. We start by articulating IoT data characteristics
and identifying two major treatments for IoT data from a machine learning
perspective, namely IoT big data analytics and IoT streaming data analytics. We
also discuss why DL is a promising approach to achieve the desired analytics in
these types of data and applications. The potential of using emerging DL
techniques for IoT data analytics are then discussed, and its promises and
challenges are introduced. We present a comprehensive background on different
DL architectures and algorithms. We also analyze and summarize major reported
research attempts that leveraged DL in the IoT domain. The smart IoT devices
that have incorporated DL in their intelligence background are also discussed.
DL implementation approaches on the fog and cloud centers in support of IoT
applications are also surveyed. Finally, we shed light on some challenges and
potential directions for future research. At the end of each section, we
highlight the lessons learned based on our experiments and review of the recent
literature.
|
The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced.
|
http://arxiv.org/abs/1712.04301v2
|
http://arxiv.org/pdf/1712.04301v2.pdf
| null |
[
"Mehdi Mohammadi",
"Ala Al-Fuqaha",
"Sameh Sorour",
"Mohsen Guizani"
] |
[] | 2017-12-09T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/neural-adversarial-training-for-semi
|
1806.00971
| null | null |
Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis
|
Japanese predicate-argument structure (PAS) analysis involves zero anaphora
resolution, which is notoriously difficult. To improve the performance of
Japanese PAS analysis, it is straightforward to increase the size of corpora
annotated with PAS. However, since it is prohibitively expensive, it is
promising to take advantage of a large amount of raw corpora. In this paper, we
propose a novel Japanese PAS analysis model based on semi-supervised
adversarial training with a raw corpus. In our experiments, our model
outperforms existing state-of-the-art models for Japanese PAS analysis.
| null |
http://arxiv.org/abs/1806.00971v2
|
http://arxiv.org/pdf/1806.00971v2.pdf
|
ACL 2018 7
|
[
"Shuhei Kurita",
"Daisuke Kawahara",
"Sadao Kurohashi"
] |
[] | 2018-06-04T00:00:00 |
https://aclanthology.org/P18-1044
|
https://aclanthology.org/P18-1044.pdf
|
neural-adversarial-training-for-semi-1
| null |
[] |
https://paperswithcode.com/paper/semantic-aware-generative-adversarial-nets
|
1806.00600
| null | null |
Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation
|
In spite of the compelling achievements that deep neural networks (DNNs) have
made in medical image computing, these deep models often suffer from degraded
performance when being applied to new test datasets with domain shift. In this
paper, we present a novel unsupervised domain adaptation approach for
segmentation tasks by designing semantic-aware generative adversarial networks
(GANs). Specifically, we transform the test image into the appearance of source
domain, with the semantic structural information being well preserved, which is
achieved by imposing a nested adversarial learning in semantic label space. In
this way, the segmentation DNN learned from the source domain is able to be
directly generalized to the transformed test image, eliminating the need of
training a new model for every new target dataset. Our domain adaptation
procedure is unsupervised, without using any target domain labels. The
adversarial learning of our network is guided by a GAN loss for mapping data
distributions, a cycle-consistency loss for retaining pixel-level content, and
a semantic-aware loss for enhancing structural information. We validated our
method on two different chest X-ray public datasets for left/right lung
segmentation. Experimental results show that the segmentation performance of
our unsupervised approach is highly competitive with the upper bound of
supervised transfer learning.
| null |
http://arxiv.org/abs/1806.00600v2
|
http://arxiv.org/pdf/1806.00600v2.pdf
| null |
[
"Cheng Chen",
"Qi Dou",
"Hao Chen",
"Pheng-Ann Heng"
] |
[
"Domain Adaptation",
"Segmentation",
"Transfer Learning",
"Unsupervised Domain Adaptation"
] | 2018-06-02T00:00:00 | null | null | null | 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": "",
"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/understanding-regularized-spectral-clustering
|
1806.01468
| null | null |
Understanding Regularized Spectral Clustering via Graph Conductance
|
This paper uses the relationship between graph conductance and spectral
clustering to study (i) the failures of spectral clustering and (ii) the
benefits of regularization. The explanation is simple. Sparse and stochastic
graphs create a lot of small trees that are connected to the core of the graph
by only one edge. Graph conductance is sensitive to these noisy `dangling
sets'. Spectral clustering inherits this sensitivity. The second part of the
paper starts from a previously proposed form of regularized spectral clustering
and shows that it is related to the graph conductance on a `regularized graph'.
We call the conductance on the regularized graph CoreCut. Based upon previous
arguments that relate graph conductance to spectral clustering (e.g. Cheeger
inequality), minimizing CoreCut relaxes to regularized spectral clustering.
Simple inspection of CoreCut reveals why it is less sensitive to small cuts in
the graph. Together, these results show that unbalanced partitions from
spectral clustering can be understood as overfitting to noise in the periphery
of a sparse and stochastic graph. Regularization fixes this overfitting. In
addition to this statistical benefit, these results also demonstrate how
regularization can improve the computational speed of spectral clustering. We
provide simulations and data examples to illustrate these results.
|
The second part of the paper starts from a previously proposed form of regularized spectral clustering and shows that it is related to the graph conductance on a `regularized graph'.
|
http://arxiv.org/abs/1806.01468v4
|
http://arxiv.org/pdf/1806.01468v4.pdf
|
NeurIPS 2018 12
|
[
"Yilin Zhang",
"Karl Rohe"
] |
[
"Clustering"
] | 2018-06-05T00:00:00 |
http://papers.nips.cc/paper/8262-understanding-regularized-spectral-clustering-via-graph-conductance
|
http://papers.nips.cc/paper/8262-understanding-regularized-spectral-clustering-via-graph-conductance.pdf
|
understanding-regularized-spectral-clustering-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
},
{
"code_snippet_url": "",
"description": "Spectral clustering has attracted increasing attention due to\r\nthe promising ability in dealing with nonlinearly separable datasets [15], [16]. In spectral clustering, the spectrum of the graph Laplacian is used to reveal the cluster structure. The spectral clustering algorithm mainly consists of two steps: 1) constructs the low dimensional embedded representation of the data based on the eigenvectors of the graph Laplacian, 2) applies k-means on the constructed low dimensional data to obtain the clustering result. Thus,",
"full_name": "Spectral Clustering",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Clustering** methods cluster a dataset so that similar datapoints are located in the same group. Below you can find a continuously updating list of clustering methods.",
"name": "Clustering",
"parent": null
},
"name": "Spectral Clustering",
"source_title": "A Tutorial on Spectral Clustering",
"source_url": "http://arxiv.org/abs/0711.0189v1"
}
] |
https://paperswithcode.com/paper/graph-based-regularization-for-regression
|
1803.07658
| null | null |
Graph-based regularization for regression problems with alignment and highly-correlated designs
|
Sparse models for high-dimensional linear regression and machine learning have received substantial attention over the past two decades. Model selection, or determining which features or covariates are the best explanatory variables, is critical to the interpretability of a learned model. Much of the current literature assumes that covariates are only mildly correlated. However, in many modern applications covariates are highly correlated and do not exhibit key properties (such as the restricted eigenvalue condition, restricted isometry property, or other related assumptions). This work considers a high-dimensional regression setting in which a graph governs both correlations among the covariates and the similarity among regression coefficients -- meaning there is \emph{alignment} between the covariates and regression coefficients. Using side information about the strength of correlations among features, we form a graph with edge weights corresponding to pairwise covariances. This graph is used to define a graph total variation regularizer that promotes similar weights for correlated features. This work shows how the proposed graph-based regularization yields mean-squared error guarantees for a broad range of covariance graph structures. These guarantees are optimal for many specific covariance graphs, including block and lattice graphs. Our proposed approach outperforms other methods for highly-correlated design in a variety of experiments on synthetic data and real biochemistry data.
| null |
https://arxiv.org/abs/1803.07658v3
|
https://arxiv.org/pdf/1803.07658v3.pdf
| null |
[
"Yuan Li",
"Benjamin Mark",
"Garvesh Raskutti",
"Rebecca Willett",
"Hyebin Song",
"David Neiman"
] |
[
"Model Selection",
"regression"
] | 2018-03-20T00:00:00 | 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"
},
{
"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/active-feature-acquisition-with-supervised
|
1802.05380
| null | null |
Active Feature Acquisition with Supervised Matrix Completion
|
Feature missing is a serious problem in many applications, which may lead to
low quality of training data and further significantly degrade the learning
performance. While feature acquisition usually involves special devices or
complex process, it is expensive to acquire all feature values for the whole
dataset. On the other hand, features may be correlated with each other, and
some values may be recovered from the others. It is thus important to decide
which features are most informative for recovering the other features as well
as improving the learning performance. In this paper, we try to train an
effective classification model with least acquisition cost by jointly
performing active feature querying and supervised matrix completion. When
completing the feature matrix, a novel target function is proposed to
simultaneously minimize the reconstruction error on observed entries and the
supervised loss on training data. When querying the feature value, the most
uncertain entry is actively selected based on the variance of previous
iterations. In addition, a bi-objective optimization method is presented for
cost-aware active selection when features bear different acquisition costs. The
effectiveness of the proposed approach is well validated by both theoretical
analysis and experimental study.
| null |
http://arxiv.org/abs/1802.05380v2
|
http://arxiv.org/pdf/1802.05380v2.pdf
| null |
[
"Sheng-Jun Huang",
"Miao Xu",
"Ming-Kun Xie",
"Masashi Sugiyama",
"Gang Niu",
"Songcan Chen"
] |
[
"Matrix Completion"
] | 2018-02-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/an-information-theoretic-view-for-deep
|
1804.09060
| null | null |
An Information-Theoretic View for Deep Learning
|
Deep learning has transformed computer vision, natural language processing,
and speech recognition\cite{badrinarayanan2017segnet, dong2016image,
ren2017faster, ji20133d}. However, two critical questions remain obscure: (1)
why do deep neural networks generalize better than shallow networks; and (2)
does it always hold that a deeper network leads to better performance?
Specifically, letting $L$ be the number of convolutional and pooling layers in
a deep neural network, and $n$ be the size of the training sample, we derive an
upper bound on the expected generalization error for this network, i.e.,
\begin{eqnarray*}
\mathbb{E}[R(W)-R_S(W)] \leq
\exp{\left(-\frac{L}{2}\log{\frac{1}{\eta}}\right)}\sqrt{\frac{2\sigma^2}{n}I(S,W)
}
\end{eqnarray*} where $\sigma >0$ is a constant depending on the loss
function, $0<\eta<1$ is a constant depending on the information loss for each
convolutional or pooling layer, and $I(S, W)$ is the mutual information between
the training sample $S$ and the output hypothesis $W$. This upper bound shows
that as the number of convolutional and pooling layers $L$ increases in the
network, the expected generalization error will decrease exponentially to zero.
Layers with strict information loss, such as the convolutional layers, reduce
the generalization error for the whole network; this answers the first
question. However, algorithms with zero expected generalization error does not
imply a small test error or $\mathbb{E}[R(W)]$. This is because
$\mathbb{E}[R_S(W)]$ is large when the information for fitting the data is lost
as the number of layers increases. This suggests that the claim `the deeper the
better' is conditioned on a small training error or $\mathbb{E}[R_S(W)]$.
Finally, we show that deep learning satisfies a weak notion of stability and
the sample complexity of deep neural networks will decrease as $L$ increases.
| null |
http://arxiv.org/abs/1804.09060v8
|
http://arxiv.org/pdf/1804.09060v8.pdf
| null |
[
"Jingwei Zhang",
"Tongliang Liu",
"DaCheng Tao"
] |
[
"Deep Learning",
"speech-recognition",
"Speech Recognition"
] | 2018-04-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/eigennetworks
|
1806.01455
| null | null |
EigenNetworks
|
Many applications donot have the benefit of the laws of physics to derive
succinct descriptive models for observed data. In alternative,
interdependencies among $N$ time series $\{ x_{nk}, k>0 \}_{n=1}^{N}$ are
nowadays often captured by a graph or network $G$ that in practice may be very
large. The network itself may change over time as well (i.e., as $G_k$).
Tracking brute force the changes of time varying networks presents major
challenges, including the associated computational problems. Further, a large
set of networks may not lend itself to useful analysis. This paper approximates
the time varying networks $\left\{G_k\right\}$ as weighted linear combinations
of eigennetworks. The eigennetworks are fixed building blocks that are
estimated by first learning the time series of graphs $G_k$ from the data $\{
x_{nk}, k>0 \}_{n=1}^{N}$, followed by a Principal Network Analysis procedure.
The weights of the eigennetwork representation are eigenfeatures and the time
varying networks $\left\{G_k\right\}$ describe a trajectory in eigennetwork
space. These eigentrajectories should be smooth since the networks $G_k$ vary
at a much slower rate than the data $x_{nk}$, except when structural network
shifts occur reflecting potentially an abrupt change in the underlying
application and sources of the data. Algorithms for learning the time series of
graphs $\left\{G_k\right\}$, deriving the eigennetworks, eigenfeatures and
eigentrajectories, and detecting changepoints are presented. Experiments on
simulated data and with two real time series data (a voting record of the US
senate and genetic expression data for the \textit{Drosophila Melanogaster} as
it goes through its life cycle) demonstrate the performance of the learning and
provide interesting interpretations of the eigennetworks.
| null |
http://arxiv.org/abs/1806.01455v2
|
http://arxiv.org/pdf/1806.01455v2.pdf
| null |
[
"Jonathan Mei",
"José M. F. Moura"
] |
[
"Descriptive",
"Time Series",
"Time Series Analysis"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/embedding-logical-queries-on-knowledge-graphs
|
1806.01445
| null | null |
Embedding Logical Queries on Knowledge Graphs
|
Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict "em what drugs are likely to target proteins involved with both diseases X and Y?" -- a query that requires reasoning about all possible proteins that {\em might} interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries -- a flexible but tractable subset of first-order logic -- on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.
|
Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities.
|
https://arxiv.org/abs/1806.01445v4
|
https://arxiv.org/pdf/1806.01445v4.pdf
|
NeurIPS 2018 12
|
[
"William L. Hamilton",
"Payal Bajaj",
"Marinka Zitnik",
"Dan Jurafsky",
"Jure Leskovec"
] |
[
"Complex Query Answering",
"Knowledge Graphs"
] | 2018-06-05T00:00:00 |
http://papers.nips.cc/paper/7473-embedding-logical-queries-on-knowledge-graphs
|
http://papers.nips.cc/paper/7473-embedding-logical-queries-on-knowledge-graphs.pdf
|
embedding-logical-queries-on-knowledge-graphs-1
| null |
[] |
https://paperswithcode.com/paper/weighted-unsupervised-learning-for-3d-object
|
1602.05920
| null | null |
Weighted Unsupervised Learning for 3D Object Detection
|
This paper introduces a novel weighted unsupervised learning for object
detection using an RGB-D camera. This technique is feasible for detecting the
moving objects in the noisy environments that are captured by an RGB-D camera.
The main contribution of this paper is a real-time algorithm for detecting each
object using weighted clustering as a separate cluster. In a preprocessing
step, the algorithm calculates the pose 3D position X, Y, Z and RGB color of
each data point and then it calculates each data point's normal vector using
the point's neighbor. After preprocessing, our algorithm calculates k-weights
for each data point; each weight indicates membership. Resulting in clustered
objects of the scene.
| null |
http://arxiv.org/abs/1602.05920v2
|
http://arxiv.org/pdf/1602.05920v2.pdf
| null |
[
"Kamran Kowsari",
"Manal H. Alassaf"
] |
[
"3D Object Detection",
"Clustering",
"Object",
"object-detection",
"Object Detection",
"Position"
] | 2016-02-18T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/analysis-of-dawnbench-a-time-to-accuracy
|
1806.01427
| null | null |
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
|
Researchers have proposed hardware, software, and algorithmic optimizations to improve the computational performance of deep learning. While some of these optimizations perform the same operations faster (e.g., increasing GPU clock speed), many others modify the semantics of the training procedure (e.g., reduced precision), and can impact the final model's accuracy on unseen data. Due to a lack of standard evaluation criteria that considers these trade-offs, it is difficult to directly compare these optimizations. To address this problem, we recently introduced DAWNBench, a benchmark competition focused on end-to-end training time to achieve near-state-of-the-art accuracy on an unseen dataset---a combined metric called time-to-accuracy (TTA). In this work, we analyze the entries from DAWNBench, which received optimized submissions from multiple industrial groups, to investigate the behavior of TTA as a metric as well as trends in the best-performing entries. We show that TTA has a low coefficient of variation and that models optimized for TTA generalize nearly as well as those trained using standard methods. Additionally, even though DAWNBench entries were able to train ImageNet models in under 3 minutes, we find they still underutilize hardware capabilities such as Tensor Cores. Furthermore, we find that distributed entries can spend more than half of their time on communication. We show similar findings with entries to the MLPERF v0.5 benchmark.
| null |
https://arxiv.org/abs/1806.01427v2
|
https://arxiv.org/pdf/1806.01427v2.pdf
| null |
[
"Cody Coleman",
"Daniel Kang",
"Deepak Narayanan",
"Luigi Nardi",
"Tian Zhao",
"Jian Zhang",
"Peter Bailis",
"Kunle Olukotun",
"Chris Re",
"Matei Zaharia"
] |
[
"Benchmarking",
"BIG-bench Machine Learning",
"GPU"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/composite-marginal-likelihood-methods-for
|
1806.01426
| null | null |
Composite Marginal Likelihood Methods for Random Utility Models
|
We propose a novel and flexible
rank-breaking-then-composite-marginal-likelihood (RBCML) framework for learning
random utility models (RUMs), which include the Plackett-Luce model. We
characterize conditions for the objective function of RBCML to be strictly
log-concave by proving that strict log-concavity is preserved under convolution
and marginalization. We characterize necessary and sufficient conditions for
RBCML to satisfy consistency and asymptotic normality. Experiments on synthetic
data show that RBCML for Gaussian RUMs achieves better statistical efficiency
and computational efficiency than the state-of-the-art algorithm and our RBCML
for the Plackett-Luce model provides flexible tradeoffs between running time
and statistical efficiency.
| null |
http://arxiv.org/abs/1806.01426v1
|
http://arxiv.org/pdf/1806.01426v1.pdf
|
ICML 2018 7
|
[
"Zhibing Zhao",
"Lirong Xia"
] |
[
"Computational Efficiency"
] | 2018-06-04T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1997
|
http://proceedings.mlr.press/v80/zhao18d/zhao18d.pdf
|
composite-marginal-likelihood-methods-for-1
| null |
[] |
https://paperswithcode.com/paper/bindsnet-a-machine-learning-oriented-spiking
|
1806.01423
| null | null |
BindsNET: A machine learning-oriented spiking neural networks library in Python
|
The development of spiking neural network simulation software is a critical
component enabling the modeling of neural systems and the development of
biologically inspired algorithms. Existing software frameworks support a wide
range of neural functionality, software abstraction levels, and hardware
devices, yet are typically not suitable for rapid prototyping or application to
problems in the domain of machine learning. In this paper, we describe a new
Python package for the simulation of spiking neural networks, specifically
geared towards machine learning and reinforcement learning. Our software,
called BindsNET, enables rapid building and simulation of spiking networks and
features user-friendly, concise syntax. BindsNET is built on top of the PyTorch
deep neural networks library, enabling fast CPU and GPU computation for large
spiking networks. The BindsNET framework can be adjusted to meet the needs of
other existing computing and hardware environments, e.g., TensorFlow. We also
provide an interface into the OpenAI gym library, allowing for training and
evaluation of spiking networks on reinforcement learning problems. We argue
that this package facilitates the use of spiking networks for large-scale
machine learning experimentation, and show some simple examples of how we
envision BindsNET can be used in practice. BindsNET code is available at
https://github.com/Hananel-Hazan/bindsnet
|
In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared towards machine learning and reinforcement learning.
|
http://arxiv.org/abs/1806.01423v2
|
http://arxiv.org/pdf/1806.01423v2.pdf
| null |
[
"Hananel Hazan",
"Daniel J. Saunders",
"Hassaan Khan",
"Darpan T. Sanghavi",
"Hava T. Siegelmann",
"Robert Kozma"
] |
[
"BIG-bench Machine Learning",
"CPU",
"GPU",
"Neural Network simulation",
"OpenAI Gym",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/mitigation-of-policy-manipulation-attacks-on
|
1806.02190
| null | null |
Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise
|
Recent developments have established the vulnerability of deep reinforcement
learning to policy manipulation attacks via intentionally perturbed inputs,
known as adversarial examples. In this work, we propose a technique for
mitigation of such attacks based on addition of noise to the parameter space of
deep reinforcement learners during training. We experimentally verify the
effect of parameter-space noise in reducing the transferability of adversarial
examples, and demonstrate the promising performance of this technique in
mitigating the impact of whitebox and blackbox attacks at both test and
training times.
| null |
http://arxiv.org/abs/1806.02190v1
|
http://arxiv.org/pdf/1806.02190v1.pdf
| null |
[
"Vahid Behzadan",
"Arslan Munir"
] |
[
"Deep Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/cfcm-segmentation-via-coarse-to-fine-context
|
1806.01413
| null | null |
CFCM: Segmentation via Coarse to Fine Context Memory
|
Recent neural-network-based architectures for image segmentation make
extensive usage of feature forwarding mechanisms to integrate information from
multiple scales. Although yielding good results, even deeper architectures and
alternative methods for feature fusion at different resolutions have been
scarcely investigated for medical applications. In this work we propose to
implement segmentation via an encoder-decoder architecture which differs from
any other previously published method since (i) it employs a very deep
architecture based on residual learning and (ii) combines features via a
convolutional Long Short Term Memory (LSTM), instead of concatenation or
summation. The intuition is that the memory mechanism implemented by LSTMs can
better integrate features from different scales through a coarse-to-fine
strategy; hence the name Coarse-to-Fine Context Memory (CFCM). We demonstrate
the remarkable advantages of this approach on two datasets: the Montgomery
county lung segmentation dataset, and the EndoVis 2015 challenge dataset for
surgical instrument segmentation.
|
Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales.
|
http://arxiv.org/abs/1806.01413v1
|
http://arxiv.org/pdf/1806.01413v1.pdf
| null |
[
"Fausto Milletari",
"Nicola Rieke",
"Maximilian Baust",
"Marco Esposito",
"Nassir Navab"
] |
[
"Decoder",
"Image Segmentation",
"Segmentation",
"Semantic Segmentation"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/flownet3d-learning-scene-flow-in-3d-point
|
1806.01411
| null | null |
FlowNet3D: Learning Scene Flow in 3D Point Clouds
|
Many applications in robotics and human-computer interaction can benefit from understanding 3D motion of points in a dynamic environment, widely noted as scene flow. While most previous methods focus on stereo and RGB-D images as input, few try to estimate scene flow directly from point clouds. In this work, we propose a novel deep neural network named $FlowNet3D$ that learns scene flow from point clouds in an end-to-end fashion. Our network simultaneously learns deep hierarchical features of point clouds and flow embeddings that represent point motions, supported by two newly proposed learning layers for point sets. We evaluate the network on both challenging synthetic data from FlyingThings3D and real Lidar scans from KITTI. Trained on synthetic data only, our network successfully generalizes to real scans, outperforming various baselines and showing competitive results to the prior art. We also demonstrate two applications of our scene flow output (scan registration and motion segmentation) to show its potential wide use cases.
|
In this work, we propose a novel deep neural network named $FlowNet3D$ that learns scene flow from point clouds in an end-to-end fashion.
|
https://arxiv.org/abs/1806.01411v3
|
https://arxiv.org/pdf/1806.01411v3.pdf
|
CVPR 2019 6
|
[
"Xingyu Liu",
"Charles R. Qi",
"Leonidas J. Guibas"
] |
[
"Motion Segmentation"
] | 2018-06-04T00:00:00 |
http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_FlowNet3D_Learning_Scene_Flow_in_3D_Point_Clouds_CVPR_2019_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_FlowNet3D_Learning_Scene_Flow_in_3D_Point_Clouds_CVPR_2019_paper.pdf
|
flownet3d-learning-scene-flow-in-3d-point-1
| null |
[] |
https://paperswithcode.com/paper/graphrnn-generating-realistic-graphs-with
|
1802.08773
| null | null |
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
|
Modeling and generating graphs is fundamental for studying networks in
biology, engineering, and social sciences. However, modeling complex
distributions over graphs and then efficiently sampling from these
distributions is challenging due to the non-unique, high-dimensional nature of
graphs and the complex, non-local dependencies that exist between edges in a
given graph. Here we propose GraphRNN, a deep autoregressive model that
addresses the above challenges and approximates any distribution of graphs with
minimal assumptions about their structure. GraphRNN learns to generate graphs
by training on a representative set of graphs and decomposes the graph
generation process into a sequence of node and edge formations, conditioned on
the graph structure generated so far.
In order to quantitatively evaluate the performance of GraphRNN, we introduce
a benchmark suite of datasets, baselines and novel evaluation metrics based on
Maximum Mean Discrepancy, which measure distances between sets of graphs. Our
experiments show that GraphRNN significantly outperforms all baselines,
learning to generate diverse graphs that match the structural characteristics
of a target set, while also scaling to graphs 50 times larger than previous
deep models.
|
Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences.
|
http://arxiv.org/abs/1802.08773v3
|
http://arxiv.org/pdf/1802.08773v3.pdf
|
ICML 2018 7
|
[
"Jiaxuan You",
"Rex Ying",
"Xiang Ren",
"William L. Hamilton",
"Jure Leskovec"
] |
[
"Graph Generation"
] | 2018-02-24T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2373
|
http://proceedings.mlr.press/v80/you18a/you18a.pdf
|
graphrnn-generating-realistic-graphs-with-1
| null |
[] |
https://paperswithcode.com/paper/learning-to-track-on-the-fly-using-a-particle
|
1806.01396
| null | null |
Learning to track on-the-fly using a particle filter with annealed- weighted QPSO modeled after a singular Dirac delta potential
|
This paper proposes an evolutionary Particle Filter with a memory guided
proposal step size update and an improved, fully-connected Quantum-behaved
Particle Swarm Optimization (QPSO) resampling scheme for visual tracking
applications. The proposal update step uses importance weights proportional to
velocities encountered in recent memory to limit the swarm movement within
probable regions of interest. The QPSO resampling scheme uses a fitness
weighted mean best update to bias the swarm towards the fittest section of
particles while also employing a simulated annealing operator to avoid subpar
fine tune during latter course of iterations. By moving particles closer to
high likelihood landscapes of the posterior distribution using such constructs,
the sample impoverishment problem that plagues the Particle Filter is mitigated
to a great extent. Experimental results using benchmark sequences imply that
the proposed method outperforms competitive candidate trackers such as the
Particle Filter and the traditional Particle Swarm Optimization based Particle
Filter on a suite of tracker performance indices.
| null |
http://arxiv.org/abs/1806.01396v1
|
http://arxiv.org/pdf/1806.01396v1.pdf
| null |
[
"Saptarshi Sengupta",
"Richard Alan Peters II"
] |
[
"Visual Tracking"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/hierarchical-text-generation-and-planning-for
|
1712.05846
| null | null |
Hierarchical Text Generation and Planning for Strategic Dialogue
|
End-to-end models for goal-orientated dialogue are challenging to train,
because linguistic and strategic aspects are entangled in latent state vectors.
We introduce an approach to learning representations of messages in dialogues
by maximizing the likelihood of subsequent sentences and actions, which
decouples the semantics of the dialogue utterance from its linguistic
realization. We then use these latent sentence representations for hierarchical
language generation, planning and reinforcement learning. Experiments show that
our approach increases the end-task reward achieved by the model, improves the
effectiveness of long-term planning using rollouts, and allows self-play
reinforcement learning to improve decision making without diverging from human
language. Our hierarchical latent-variable model outperforms previous work both
linguistically and strategically.
|
End-to-end models for goal-orientated dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors.
|
http://arxiv.org/abs/1712.05846v2
|
http://arxiv.org/pdf/1712.05846v2.pdf
|
ICML 2018 7
|
[
"Denis Yarats",
"Mike Lewis"
] |
[
"Decision Making",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"Sentence",
"Text Generation"
] | 2017-12-15T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2257
|
http://proceedings.mlr.press/v80/yarats18a/yarats18a.pdf
|
hierarchical-text-generation-and-planning-for-1
| null |
[] |
https://paperswithcode.com/paper/new-and-surprising-ways-to-be-mean
|
1806.01387
| null | null |
New And Surprising Ways to Be Mean. Adversarial NPCs with Coupled Empowerment Minimisation
|
Creating Non-Player Characters (NPCs) that can react robustly to unforeseen
player behaviour or novel game content is difficult and time-consuming. This
hinders the design of believable characters, and the inclusion of NPCs in games
that rely heavily on procedural content generation. We have previously
addressed this challenge by means of empowerment, a model of intrinsic
motivation, and demonstrated how a coupled empowerment maximisation (CEM)
policy can yield generic, companion-like behaviour. In this paper, we extend
the CEM framework with a minimisation policy to give rise to adversarial
behaviour. We conduct a qualitative, exploratory study in a dungeon-crawler
game, demonstrating that CEM can exploit the affordances of different content
facets in adaptive adversarial behaviour without modifications to the policy.
Changes to the level design, underlying mechanics and our character's actions
do not threaten our NPC's robustness, but yield new and surprising ways to be
mean.
| null |
http://arxiv.org/abs/1806.01387v1
|
http://arxiv.org/pdf/1806.01387v1.pdf
| null |
[
"Christian Guckelsberger",
"Christoph Salge",
"Julian Togelius"
] |
[] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-general-approach-to-multi-armed-bandits
|
1806.01380
| null | null |
A General Framework for Bandit Problems Beyond Cumulative Objectives
|
The stochastic multi-armed bandit (MAB) problem is a common model for sequential decision problems. In the standard setup, a decision maker has to choose at every instant between several competing arms, each of them provides a scalar random variable, referred to as a "reward." Nearly all research on this topic considers the total cumulative reward as the criterion of interest. This work focuses on other natural objectives that cannot be cast as a sum over rewards, but rather more involved functions of the reward stream. Unlike the case of cumulative criteria, in the problems we study here the oracle policy, that knows the problem parameters a priori and is used to "center" the regret, is not trivial. We provide a systematic approach to such problems, and derive general conditions under which the oracle policy is sufficiently tractable to facilitate the design of optimism-based (upper confidence bound) learning policies. These conditions elucidate an interesting interplay between the arm reward distributions and the performance metric. Our main findings are illustrated for several commonly used objectives such as conditional value-at-risk, mean-variance trade-offs, Sharpe-ratio, and more.
| null |
https://arxiv.org/abs/1806.01380v3
|
https://arxiv.org/pdf/1806.01380v3.pdf
| null |
[
"Asaf Cassel",
"Shie Mannor",
"Assaf Zeevi"
] |
[
"Multi-Armed Bandits"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/factorized-adversarial-networks-for
|
1806.01376
| null | null |
Factorized Adversarial Networks for Unsupervised Domain Adaptation
|
In this paper, we propose Factorized Adversarial Networks (FAN) to solve
unsupervised domain adaptation problems for image classification tasks. Our
networks map the data distribution into a latent feature space, which is
factorized into a domain-specific subspace that contains domain-specific
characteristics and a task-specific subspace that retains category information,
for both source and target domains, respectively. Unsupervised domain
adaptation is achieved by adversarial training to minimize the discrepancy
between the distributions of two task-specific subspaces from source and target
domains. We demonstrate that the proposed approach outperforms state-of-the-art
methods on multiple benchmark datasets used in the literature for unsupervised
domain adaptation. Furthermore, we collect two real-world tagging datasets that
are much larger than existing benchmark datasets, and get significant
improvement upon baselines, proving the practical value of our approach.
| null |
http://arxiv.org/abs/1806.01376v1
|
http://arxiv.org/pdf/1806.01376v1.pdf
| null |
[
"Jian Ren",
"Jianchao Yang",
"Ning Xu",
"David J. Foran"
] |
[
"Domain Adaptation",
"General Classification",
"image-classification",
"Image Classification",
"Unsupervised Domain Adaptation"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/investigating-human-priors-for-playing-video
|
1802.10217
| null |
Hk91SGWR-
|
Investigating Human Priors for Playing Video Games
|
What makes humans so good at solving seemingly complex video games? Unlike
computers, humans bring in a great deal of prior knowledge about the world,
enabling efficient decision making. This paper investigates the role of human
priors for solving video games. Given a sample game, we conduct a series of
ablation studies to quantify the importance of various priors on human
performance. We do this by modifying the video game environment to
systematically mask different types of visual information that could be used by
humans as priors. We find that removal of some prior knowledge causes a drastic
degradation in the speed with which human players solve the game, e.g. from 2
minutes to over 20 minutes. Furthermore, our results indicate that general
priors, such as the importance of objects and visual consistency, are critical
for efficient game-play. Videos and the game manipulations are available at
https://rach0012.github.io/humanRL_website/
|
What makes humans so good at solving seemingly complex video games?
|
http://arxiv.org/abs/1802.10217v3
|
http://arxiv.org/pdf/1802.10217v3.pdf
|
ICML 2018 7
|
[
"Rachit Dubey",
"Pulkit Agrawal",
"Deepak Pathak",
"Thomas L. Griffiths",
"Alexei A. Efros"
] |
[
"Decision Making"
] | 2018-02-28T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2133
|
http://proceedings.mlr.press/v80/dubey18a/dubey18a.pdf
|
investigating-human-priors-for-playing-video-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/learning-visually-grounded-sentence
|
1707.06320
| null | null |
Learning Visually Grounded Sentence Representations
|
We introduce a variety of models, trained on a supervised image captioning
corpus to predict the image features for a given caption, to perform sentence
representation grounding. We train a grounded sentence encoder that achieves
good performance on COCO caption and image retrieval and subsequently show that
this encoder can successfully be transferred to various NLP tasks, with
improved performance over text-only models. Lastly, we analyze the contribution
of grounding, and show that word embeddings learned by this system outperform
non-grounded ones.
| null |
http://arxiv.org/abs/1707.06320v2
|
http://arxiv.org/pdf/1707.06320v2.pdf
|
NAACL 2018 6
|
[
"Douwe Kiela",
"Alexis Conneau",
"Allan Jabri",
"Maximilian Nickel"
] |
[
"Language Modelling",
"Representation Learning",
"Retrieval",
"Sentence"
] | 2017-07-19T00:00:00 |
https://aclanthology.org/N18-1038
|
https://aclanthology.org/N18-1038.pdf
|
learning-visually-grounded-sentence-1
| null |
[] |
https://paperswithcode.com/paper/adversarial-reinforcement-learning-framework
|
1806.01368
| null | null |
Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles
|
With the rapidly growing interest in autonomous navigation, the body of
research on motion planning and collision avoidance techniques has enjoyed an
accelerating rate of novel proposals and developments. However, the complexity
of new techniques and their safety requirements render the bulk of current
benchmarking frameworks inadequate, thus leaving the need for efficient
comparison techniques unanswered. This work proposes a novel framework based on
deep reinforcement learning for benchmarking the behavior of collision
avoidance mechanisms under the worst-case scenario of dealing with an optimal
adversarial agent, trained to drive the system into unsafe states. We describe
the architecture and flow of this framework as a benchmarking solution, and
demonstrate its efficacy via a practical case study of comparing the
reliability of two collision avoidance mechanisms in response to intentional
collision attempts.
| null |
http://arxiv.org/abs/1806.01368v1
|
http://arxiv.org/pdf/1806.01368v1.pdf
| null |
[
"Vahid Behzadan",
"Arslan Munir"
] |
[
"Autonomous Navigation",
"Autonomous Vehicles",
"Benchmarking",
"Collision Avoidance",
"Deep Reinforcement Learning",
"Motion Planning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/playing-atari-with-six-neurons
|
1806.01363
| null | null |
Playing Atari with Six Neurons
|
Deep reinforcement learning, applied to vision-based problems like Atari
games, maps pixels directly to actions; internally, the deep neural network
bears the responsibility of both extracting useful information and making
decisions based on it. By separating the image processing from decision-making,
one could better understand the complexity of each task, as well as potentially
find smaller policy representations that are easier for humans to understand
and may generalize better. To this end, we propose a new method for learning
policies and compact state representations separately but simultaneously for
policy approximation in reinforcement learning. State representations are
generated by an encoder based on two novel algorithms: Increasing Dictionary
Vector Quantization makes the encoder capable of growing its dictionary size
over time, to address new observations as they appear in an open-ended
online-learning context; Direct Residuals Sparse Coding encodes observations by
disregarding reconstruction error minimization, and aiming instead for highest
information inclusion. The encoder autonomously selects observations online to
train on, in order to maximize code sparsity. As the dictionary size increases,
the encoder produces increasingly larger inputs for the neural network: this is
addressed by a variation of the Exponential Natural Evolution Strategies
algorithm which adapts its probability distribution dimensionality along the
run. We test our system on a selection of Atari games using tiny neural
networks of only 6 to 18 neurons (depending on the game's controls). These are
still capable of achieving results comparable---and occasionally superior---to
state-of-the-art techniques which use two orders of magnitude more neurons.
|
Deep reinforcement learning, applied to vision-based problems like Atari games, maps pixels directly to actions; internally, the deep neural network bears the responsibility of both extracting useful information and making decisions based on it.
|
http://arxiv.org/abs/1806.01363v2
|
http://arxiv.org/pdf/1806.01363v2.pdf
| null |
[
"Giuseppe Cuccu",
"Julian Togelius",
"Philippe Cudre-Mauroux"
] |
[
"Atari Games",
"Decision Making",
"Deep Reinforcement Learning",
"Quantization",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/modeling-camera-effects-to-improve-visual
|
1803.07721
| null | null |
Modeling Camera Effects to Improve Visual Learning from Synthetic Data
|
Recent work has focused on generating synthetic imagery to increase the size
and variability of training data for learning visual tasks in urban scenes.
This includes increasing the occurrence of occlusions or varying environmental
and weather effects. However, few have addressed modeling variation in the
sensor domain. Sensor effects can degrade real images, limiting
generalizability of network performance on visual tasks trained on synthetic
data and tested in real environments. This paper proposes an efficient,
automatic, physically-based augmentation pipeline to vary sensor effects
--chromatic aberration, blur, exposure, noise, and color cast-- for synthetic
imagery. In particular, this paper illustrates that augmenting synthetic
training datasets with the proposed pipeline reduces the domain gap between
synthetic and real domains for the task of object detection in urban driving
scenes.
|
Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes.
|
http://arxiv.org/abs/1803.07721v6
|
http://arxiv.org/pdf/1803.07721v6.pdf
| null |
[
"Alexandra Carlson",
"Katherine A. Skinner",
"Ram Vasudevan",
"Matthew Johnson-Roberson"
] |
[
"object-detection",
"Object Detection"
] | 2018-03-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/gprhog-and-the-popularity-of-histogram-of
|
1806.01349
| null | null |
gprHOG and the popularity of Histogram of Oriented Gradients (HOG) for Buried Threat Detection in Ground-Penetrating Radar
|
Substantial research has been devoted to the development of algorithms that
automate buried threat detection (BTD) with ground penetrating radar (GPR)
data, resulting in a large number of proposed algorithms. One popular algorithm
GPR-based BTD, originally applied by Torrione et al., 2012, is the Histogram of
Oriented Gradients (HOG) feature. In a recent large-scale comparison among five
veteran institutions, a modified version of HOG referred to here as "gprHOG",
performed poorly compared to other modern algorithms. In this paper, we provide
experimental evidence demonstrating that the modifications to HOG that comprise
gprHOG result in a substantially better-performing algorithm. The results here,
in conjunction with the large-scale algorithm comparison, suggest that HOG is
not competitive with modern GPR-based BTD algorithms. Given HOG's popularity,
these results raise some questions about many existing studies, and suggest
gprHOG (and especially HOG) should be employed with caution in future studies.
| null |
http://arxiv.org/abs/1806.01349v2
|
http://arxiv.org/pdf/1806.01349v2.pdf
| null |
[
"Daniel Reichman",
"Leslie M. Collins",
"Jordan M. Malof"
] |
[
"GPR"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/importance-sampling-policy-evaluation-with-an
|
1806.01347
| null | null |
Importance Sampling Policy Evaluation with an Estimated Behavior Policy
|
We consider the problem of off-policy evaluation in Markov decision processes. Off-policy evaluation is the task of evaluating the expected return of one policy with data generated by a different, behavior policy. Importance sampling is a technique for off-policy evaluation that re-weights off-policy returns to account for differences in the likelihood of the returns between the two policies. In this paper, we study importance sampling with an estimated behavior policy where the behavior policy estimate comes from the same set of data used to compute the importance sampling estimate. We find that this estimator often lowers the mean squared error of off-policy evaluation compared to importance sampling with the true behavior policy or using a behavior policy that is estimated from a separate data set. Intuitively, estimating the behavior policy in this way corrects for error due to sampling in the action-space. Our empirical results also extend to other popular variants of importance sampling and show that estimating a non-Markovian behavior policy can further lower large-sample mean squared error even when the true behavior policy is Markovian.
|
We find that this estimator often lowers the mean squared error of off-policy evaluation compared to importance sampling with the true behavior policy or using a behavior policy that is estimated from a separate data set.
|
https://arxiv.org/abs/1806.01347v3
|
https://arxiv.org/pdf/1806.01347v3.pdf
| null |
[
"Josiah P. Hanna",
"Scott Niekum",
"Peter Stone"
] |
[
"Off-policy evaluation"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/design-of-optimal-illumination-patterns-in
|
1806.01340
| null | null |
Design of optimal illumination patterns in single-pixel imaging using image dictionaries
|
Single-pixel imaging (SPI) has a major drawback that many sequential illuminations are required for capturing one single image with long acquisition time. Basis illumination patterns such as Fourier patterns and Hadamard patterns can achieve much better imaging efficiency than random patterns. But the performance is still sub-optimal since the basis patterns are fixed and non-adaptive for varying object images. This Letter proposes a novel scheme for designing and optimizing the illumination patterns adaptively from an image dictionary by extracting the common image features using principal component analysis (PCA). Simulation and experimental results reveal that our proposed scheme outperforms conventional Fourier SPI in terms of imaging efficiency.
| null |
https://arxiv.org/abs/1806.01340v2
|
https://arxiv.org/pdf/1806.01340v2.pdf
| null |
[
"Jun Feng",
"Shuming Jiao",
"Yang Gao",
"Ting Lei",
"Xiaocong Yuan"
] |
[] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/computing-the-spatial-probability-of
|
1806.01339
| null | null |
Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications
|
In Computer Vision, edge detection is one of the favored approaches for feature and object detection in images since it provides information about their objects boundaries. Other region-based approaches use probabilistic analysis such as clustering and Markov random fields, but those methods cannot be used to analyze edges and their interaction. In fact, only image segmentation can produce regions based on edges, but it requires thresholding by simply separating the regions into binary in-out information. Hence, there is currently a gap between edge-based and region-based algorithms, since edges cannot be used to study the properties of a region and vice versa. The objective of this paper is to present a novel spatial probability analysis that allows determining the probability of inclusion inside a set of partial contours (strokes). To answer this objective, we developed a new approach that uses electromagnetic convolutions and repulsion optimization to compute the required probabilities. Hence, it becomes possible to generate a continuous space of probability based only on the edge information, thus bridging the gap between the edge-based methods and the region-based methods. The developed method is consistent with the fundamental properties of inclusion probabilities and its results are validated by comparing an image with the probability-based estimation given by our algorithm. The method can also be generalized to take into consideration the intensity of the edges or to be used for 3D shapes. This is the first documented method that allows computing a space of probability based on interacting edges, which opens the path to broader applications such as image segmentation and contour completion.
| null |
https://arxiv.org/abs/1806.01339v2
|
https://arxiv.org/pdf/1806.01339v2.pdf
| null |
[
"Dominique Beaini",
"Sofiane Achiche",
"Fabrice Nonez",
"Maxime Raison"
] |
[
"Clustering",
"Edge Detection",
"Image Segmentation",
"object-detection",
"Object Detection",
"Semantic Segmentation"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/less-is-more-simultaneous-view-classification
|
1805.10376
| null | null |
Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images
|
An abdominal ultrasound examination, which is the most common ultrasound
examination, requires substantial manual efforts to acquire standard abdominal
organ views, annotate the views in texts, and record clinically relevant organ
measurements. Hence, automatic view classification and landmark detection of
the organs can be instrumental to streamline the examination workflow. However,
this is a challenging problem given not only the inherent difficulties from the
ultrasound modality, e.g., low contrast and large variations, but also the
heterogeneity across tasks, i.e., one classification task for all views, and
then one landmark detection task for each relevant view. While convolutional
neural networks (CNN) have demonstrated more promising outcomes on ultrasound
image analytics than traditional machine learning approaches, it becomes
impractical to deploy multiple networks (one for each task) due to the limited
computational and memory resources on most existing ultrasound scanners. To
overcome such limits, we propose a multi-task learning framework to handle all
the tasks by a single network. This network is integrated to perform view
classification and landmark detection simultaneously; it is also equipped with
global convolutional kernels, coordinate constraints, and a conditional
adversarial module to leverage the performances. In an experimental study based
on 187,219 ultrasound images, with the proposed simplified approach we achieve
(1) view classification accuracy better than the agreement between two clinical
experts and (2) landmark-based measurement errors on par with inter-user
variability. The multi-task approach also benefits from sharing the feature
extraction during the training process across all tasks and, as a result,
outperforms the approaches that address each task individually.
| null |
http://arxiv.org/abs/1805.10376v2
|
http://arxiv.org/pdf/1805.10376v2.pdf
| null |
[
"Zhoubing Xu",
"Yuankai Huo",
"Jin-Hyeong Park",
"Bennett Landman",
"Andy Milkowski",
"Sasa Grbic",
"Shaohua Zhou"
] |
[
"Classification",
"General Classification",
"Multi-Task Learning"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/backdrop-stochastic-backpropagation
|
1806.01337
| null |
B1l8iiA9tQ
|
Backdrop: Stochastic Backpropagation
|
We introduce backdrop, a flexible and simple-to-implement method, intuitively
described as dropout acting only along the backpropagation pipeline. Backdrop
is implemented via one or more masking layers which are inserted at specific
points along the network. Each backdrop masking layer acts as the identity in
the forward pass, but randomly masks parts of the backward gradient
propagation. Intuitively, inserting a backdrop layer after any convolutional
layer leads to stochastic gradients corresponding to features of that scale.
Therefore, backdrop is well suited for problems in which the data have a
multi-scale, hierarchical structure. Backdrop can also be applied to problems
with non-decomposable loss functions where standard SGD methods are not well
suited. We perform a number of experiments and demonstrate that backdrop leads
to significant improvements in generalization.
|
We introduce backdrop, a flexible and simple-to-implement method, intuitively described as dropout acting only along the backpropagation pipeline.
|
http://arxiv.org/abs/1806.01337v1
|
http://arxiv.org/pdf/1806.01337v1.pdf
|
ICLR 2019 5
|
[
"Siavash Golkar",
"Kyle Cranmer"
] |
[] | 2018-06-04T00:00:00 |
https://openreview.net/forum?id=B1l8iiA9tQ
|
https://openreview.net/pdf?id=B1l8iiA9tQ
|
backdrop-stochastic-backpropagation-1
| 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/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using all available data. That is for weights $w$ and a loss function $L$ we have:\r\n\r\n$$ w\\_{t+1} = w\\_{t} - \\eta\\hat{\\nabla}\\_{w}{L(w\\_{t})} $$\r\n\r\nWhere $\\eta$ is a learning rate. SGD reduces redundancy compared to batch gradient descent - which recomputes gradients for similar examples before each parameter update - so it is usually much faster.\r\n\r\n(Image Source: [here](http://rasbt.github.io/mlxtend/user_guide/general_concepts/gradient-optimization/))",
"full_name": "Stochastic Gradient Descent",
"introduced_year": 1951,
"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": "SGD",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/precise-runtime-analysis-for-plateaus
|
1806.01331
| null | null |
Precise Runtime Analysis for Plateau Functions
|
To gain a better theoretical understanding of how evolutionary algorithms (EAs) cope with plateaus of constant fitness, we propose the $n$-dimensional Plateau$_k$ function as natural benchmark and analyze how different variants of the $(1 + 1)$ EA optimize it. The Plateau$_k$ function has a plateau of second-best fitness in a ball of radius $k$ around the optimum. As evolutionary algorithm, we regard the $(1 + 1)$ EA using an arbitrary unbiased mutation operator. Denoting by $\alpha$ the random number of bits flipped in an application of this operator and assuming that $\Pr[\alpha = 1]$ has at least some small sub-constant value, we show the surprising result that for all constant $k \ge 2$, the runtime $T$ follows a distribution close to the geometric one with success probability equal to the probability to flip between $1$ and $k$ bits divided by the size of the plateau. Consequently, the expected runtime is the inverse of this number, and thus only depends on the probability to flip between $1$ and $k$ bits, but not on other characteristics of the mutation operator. Our result also implies that the optimal mutation rate for standard bit mutation here is approximately $k/(en)$. Our main analysis tool is a combined analysis of the Markov chains on the search point space and on the Hamming level space, an approach that promises to be useful also for other plateau problems.
| null |
https://arxiv.org/abs/1806.01331v4
|
https://arxiv.org/pdf/1806.01331v4.pdf
| null |
[
"Denis Antipov",
"Benjamin Doerr"
] |
[
"Evolutionary Algorithms"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/absolute-orientation-for-word-embedding
|
1806.01330
| null | null |
Closed Form Word Embedding Alignment
|
We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two embeddings of the same vocabulary into the same dimensional space. Our methods extend approaches known as Absolute Orientation, which are popular for aligning objects in three-dimensions, and generalize an approach by Smith etal (ICLR 2017). We prove new results for optimal scaling and for maximizing cosine similarity. Then we demonstrate how to evaluate the similarity of embeddings from different sources or mechanisms, and that certain properties like synonyms and analogies are preserved across the embeddings and can be enhanced by simply aligning and averaging ensembles of embeddings.
| null |
https://arxiv.org/abs/1806.01330v4
|
https://arxiv.org/pdf/1806.01330v4.pdf
| null |
[
"Sunipa Dev",
"Safia Hassan",
"Jeff M. Phillips"
] |
[
"Form",
"Word Embeddings"
] | 2018-06-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**GloVe Embeddings** are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares objective $J$ that minimizes the difference between the dot product of the vectors of two words and the logarithm of their number of co-occurrences:\r\n\r\n$$ J=\\sum\\_{i, j=1}^{V}f\\left(𝑋\\_{i j}\\right)(w^{T}\\_{i}\\tilde{w}_{j} + b\\_{i} + \\tilde{b}\\_{j} - \\log{𝑋}\\_{ij})^{2} $$\r\n\r\nwhere $w\\_{i}$ and $b\\_{i}$ are the word vector and bias respectively of word $i$, $\\tilde{w}_{j}$ and $b\\_{j}$ are the context word vector and bias respectively of word $j$, $X\\_{ij}$ is the number of times word $i$ occurs in the context of word $j$, and $f$ is a weighting function that assigns lower weights to rare and frequent co-occurrences.",
"full_name": "GloVe Embeddings",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Word Embeddings",
"parent": null
},
"name": "GloVe",
"source_title": "GloVe: Global Vectors for Word Representation",
"source_url": "https://aclanthology.org/D14-1162"
}
] |
https://paperswithcode.com/paper/past-visions-of-artificial-futures-one
|
1806.01322
| null | null |
Past Visions of Artificial Futures: One Hundred and Fifty Years under the Spectre of Evolving Machines
|
The influence of Artificial Intelligence (AI) and Artificial Life (ALife)
technologies upon society, and their potential to fundamentally shape the
future evolution of humankind, are topics very much at the forefront of current
scientific, governmental and public debate. While these might seem like very
modern concerns, they have a long history that is often disregarded in
contemporary discourse. Insofar as current debates do acknowledge the history
of these ideas, they rarely look back further than the origin of the modern
digital computer age in the 1940s-50s. In this paper we explore the earlier
history of these concepts. We focus in particular on the idea of
self-reproducing and evolving machines, and potential implications for our own
species. We show that discussion of these topics arose in the 1860s, within a
decade of the publication of Darwin's The Origin of Species, and attracted
increasing interest from scientists, novelists and the general public in the
early 1900s. After introducing the relevant work from this period, we
categorise the various visions presented by these authors of the future
implications of evolving machines for humanity. We suggest that current debates
on the co-evolution of society and technology can be enriched by a proper
appreciation of the long history of the ideas involved.
| null |
http://arxiv.org/abs/1806.01322v1
|
http://arxiv.org/pdf/1806.01322v1.pdf
| null |
[
"Tim Taylor",
"Alan Dorin"
] |
[
"Artificial Life"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/cube-padding-for-weakly-supervised-saliency
|
1806.01320
| null | null |
Cube Padding for Weakly-Supervised Saliency Prediction in 360° Videos
|
Automatic saliency prediction in 360{\deg} videos is critical for viewpoint
guidance applications (e.g., Facebook 360 Guide). We propose a spatial-temporal
network which is (1) weakly-supervised trained and (2) tailor-made for
360{\deg} viewing sphere. Note that most existing methods are less scalable
since they rely on annotated saliency map for training. Most importantly, they
convert 360{\deg} sphere to 2D images (e.g., a single equirectangular image or
multiple separate Normal Field-of-View (NFoV) images) which introduces
distortion and image boundaries. In contrast, we propose a simple and effective
Cube Padding (CP) technique as follows. Firstly, we render the 360{\deg} view
on six faces of a cube using perspective projection. Thus, it introduces very
little distortion. Then, we concatenate all six faces while utilizing the
connectivity between faces on the cube for image padding (i.e., Cube Padding)
in convolution, pooling, convolutional LSTM layers. In this way, CP introduces
no image boundary while being applicable to almost all Convolutional Neural
Network (CNN) structures. To evaluate our method, we propose Wild-360, a new
360{\deg} video saliency dataset, containing challenging videos with saliency
heatmap annotations. In experiments, our method outperforms baseline methods in
both speed and quality.
| null |
http://arxiv.org/abs/1806.01320v1
|
http://arxiv.org/pdf/1806.01320v1.pdf
|
CVPR 2018
|
[
"Hsien-Tzu Cheng",
"Chun-Hung Chao",
"Jin-Dong Dong",
"Hao-Kai Wen",
"Tyng-Luh Liu",
"Min Sun"
] |
[
"Saliency Prediction"
] | 2018-06-04T00:00:00 | 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": "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/data-driven-localization-and-estimation-of
|
1806.01318
| null | null |
Data-driven Localization and Estimation of Disturbance in the Interconnected Power System
|
Identifying the location of a disturbance and its magnitude is an important
component for stable operation of power systems. We study the problem of
localizing and estimating a disturbance in the interconnected power system. We
take a model-free approach to this problem by using frequency data from
generators. Specifically, we develop a logistic regression based method for
localization and a linear regression based method for estimation of the
magnitude of disturbance. Our model-free approach does not require the
knowledge of system parameters such as inertia constants and topology, and is
shown to achieve highly accurate localization and estimation performance even
in the presence of measurement noise and missing data.
| null |
http://arxiv.org/abs/1806.01318v1
|
http://arxiv.org/pdf/1806.01318v1.pdf
| null |
[
"Hyang-Won Lee",
"Jianan Zhang",
"Eytan Modiano"
] |
[
"regression"
] | 2018-06-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Logistic Regression**, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function.\r\n\r\nSource: [scikit-learn](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)\r\n\r\nImage: [Michaelg2015](https://commons.wikimedia.org/wiki/User:Michaelg2015)",
"full_name": "Logistic 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": "Logistic Regression",
"source_title": null,
"source_url": 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/universal-statistics-of-fisher-information-in
|
1806.01316
| null | null |
Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach
|
The Fisher information matrix (FIM) is a fundamental quantity to represent the characteristics of a stochastic model, including deep neural networks (DNNs). The present study reveals novel statistics of FIM that are universal among a wide class of DNNs. To this end, we use random weights and large width limits, which enables us to utilize mean field theories. We investigate the asymptotic statistics of the FIM's eigenvalues and reveal that most of them are close to zero while the maximum eigenvalue takes a huge value. Because the landscape of the parameter space is defined by the FIM, it is locally flat in most dimensions, but strongly distorted in others. Moreover, we demonstrate the potential usage of the derived statistics in learning strategies. First, small eigenvalues that induce flatness can be connected to a norm-based capacity measure of generalization ability. Second, the maximum eigenvalue that induces the distortion enables us to quantitatively estimate an appropriately sized learning rate for gradient methods to converge.
| null |
https://arxiv.org/abs/1806.01316v3
|
https://arxiv.org/pdf/1806.01316v3.pdf
| null |
[
"Ryo Karakida",
"Shotaro Akaho",
"Shun-ichi Amari"
] |
[] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/finite-sample-analysis-of-two-timescale
|
1703.05376
| null | null |
Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning
|
Two-timescale Stochastic Approximation (SA) algorithms are widely used in
Reinforcement Learning (RL). Their iterates have two parts that are updated
using distinct stepsizes. In this work, we develop a novel recipe for their
finite sample analysis. Using this, we provide a concentration bound, which is
the first such result for a two-timescale SA. The type of bound we obtain is
known as `lock-in probability'. We also introduce a new projection scheme, in
which the time between successive projections increases exponentially. This
scheme allows one to elegantly transform a lock-in probability into a
convergence rate result for projected two-timescale SA. From this latter
result, we then extract key insights on stepsize selection. As an application,
we finally obtain convergence rates for the projected two-timescale RL
algorithms GTD(0), GTD2, and TDC.
| null |
http://arxiv.org/abs/1703.05376v5
|
http://arxiv.org/pdf/1703.05376v5.pdf
| null |
[
"Gal Dalal",
"Balazs Szorenyi",
"Gugan Thoppe",
"Shie Mannor"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2017-03-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/y-net-joint-segmentation-and-classification
|
1806.01313
| null | null |
Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images
|
In this paper, we introduce a conceptually simple network for generating
discriminative tissue-level segmentation masks for the purpose of breast cancer
diagnosis. Our method efficiently segments different types of tissues in breast
biopsy images while simultaneously predicting a discriminative map for
identifying important areas in an image. Our network, Y-Net, extends and
generalizes U-Net by adding a parallel branch for discriminative map generation
and by supporting convolutional block modularity, which allows the user to
adjust network efficiency without altering the network topology. Y-Net delivers
state-of-the-art segmentation accuracy while learning 6.6x fewer parameters
than its closest competitors. The addition of descriptive power from Y-Net's
discriminative segmentation masks improve diagnostic classification accuracy by
7% over state-of-the-art methods for diagnostic classification. Source code is
available at: https://sacmehta.github.io/YNet.
|
In this paper, we introduce a conceptually simple network for generating discriminative tissue-level segmentation masks for the purpose of breast cancer diagnosis.
|
http://arxiv.org/abs/1806.01313v1
|
http://arxiv.org/pdf/1806.01313v1.pdf
| null |
[
"Sachin Mehta",
"Ezgi Mercan",
"Jamen Bartlett",
"Donald Weave",
"Joann G. Elmore",
"Linda Shapiro"
] |
[
"Descriptive",
"Diagnostic",
"General Classification",
"Medical Image Segmentation",
"Segmentation"
] | 2018-06-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/densenet.py#L113",
"description": "A **Concatenated Skip Connection** is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from previous layers of the network. This contrasts with say, residual connections, where element-wise summation is used instead to incorporate information from previous layers. This type of skip connection is prominently used in DenseNets (and also Inception networks), which the Figure to the right illustrates.",
"full_name": "Concatenated Skip 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": "Concatenated Skip Connection",
"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": 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": "",
"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/milesial/Pytorch-UNet/blob/67bf11b4db4c5f2891bd7e8e7f58bcde8ee2d2db/unet/unet_model.py#L8",
"description": "**U-Net** is an architecture for semantic segmentation. It consists of a contracting path and an expansive path. The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit ([ReLU](https://paperswithcode.com/method/relu)) and a 2x2 [max pooling](https://paperswithcode.com/method/max-pooling) operation with stride 2 for downsampling. At each downsampling step we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 [convolution](https://paperswithcode.com/method/convolution) (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each followed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer a [1x1 convolution](https://paperswithcode.com/method/1x1-convolution) is used to map each 64-component feature vector to the desired number of classes. In total the network has 23 convolutional layers.\r\n\r\n[Original MATLAB Code](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/u-net-release-2015-10-02.tar.gz)",
"full_name": "U-Net",
"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": "U-Net",
"source_title": "U-Net: Convolutional Networks for Biomedical Image Segmentation",
"source_url": "http://arxiv.org/abs/1505.04597v1"
}
] |
https://paperswithcode.com/paper/end-to-end-brain-fiber-orientation-estimation
|
1806.03969
| null | null |
End to End Brain Fiber Orientation Estimation using Deep Learning
|
In this work, we explore the various Brain Neuron tracking techniques, which
is one of the most significant applications of Diffusion Tensor Imaging.
Tractography provides us with a non-invasive method to analyze underlying
tissue micro-structure. Understanding the structure and organization of the
tissues facilitates us with a diagnosis method to identify any aberrations and
provide acute information on the occurrences of brain ischemia or stroke, the
mutation of neurological diseases such as Alzheimer, multiple sclerosis and so
on. Time if of essence and accurate localization of the aberrations can help
save or change a diseased life. Following up with the limitations introduced by
the current Tractography techniques such as computational complexity,
reconstruction errors during tensor estimation and standardization, we aim to
elucidate these limitations through our research findings. We introduce an end
to end Deep Learning framework which can accurately estimate the most probable
likelihood orientation at each voxel along a neuronal pathway. We use
Probabilistic Tractography as our baseline model to obtain the training data
and which also serve as a Tractography Gold Standard for our evaluations.
Through experiments we show that our Deep Network can do a significant
improvement over current Tractography implementations by reducing the run-time
complexity to a significant new level. Our architecture also allows for
variable sized input DWI signals eliminating the need to worry about memory
issues as seen with the traditional techniques. The advantage of this
architecture is that it is perfectly desirable to be processed on a cloud setup
and utilize the existing multi GPU frameworks to perform whole brain
Tractography in minutes rather than hours. We evaluate our network with Gold
Standard and benchmark its performance across several parameters.
| null |
http://arxiv.org/abs/1806.03969v1
|
http://arxiv.org/pdf/1806.03969v1.pdf
| null |
[
"Nandakishore Puttashamachar",
"Ulas Bagci"
] |
[
"Deep Learning",
"GPU"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/infrared-safety-of-a-neural-net-top-tagging
|
1806.01263
| null | null |
Infrared Safety of a Neural-Net Top Tagging Algorithm
|
Neural network-based algorithms provide a promising approach to jet
classification problems, such as boosted top jet tagging. To date, NN-based top
taggers demonstrated excellent performance in Monte Carlo studies. In this
paper, we construct a top-jet tagger based on a Convolutional Neural Network
(CNN), and apply it to parton-level boosted top samples, with and without an
additional gluon in the final state. We show that the jet observable defined by
the CNN obeys the canonical definition of infrared safety: it is unaffected by
the presence of the extra gluon, as long as it is soft or collinear with one of
the quarks. Our results indicate that the CNN tagger is robust with respect to
possible mis-modeling of soft and collinear final-state radiation by Monte
Carlo generators.
| null |
http://arxiv.org/abs/1806.01263v2
|
http://arxiv.org/pdf/1806.01263v2.pdf
| null |
[
"Suyong Choi",
"Seung J. Lee",
"Maxim Perelstein"
] |
[
"General Classification",
"Jet Tagging"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-a-code-machine-learning-for
|
1806.01259
| null | null |
Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation
|
Machine learning algorithms are typically run on large scale, distributed
compute infrastructure that routinely face a number of unavailabilities such as
failures and temporary slowdowns. Adding redundant computations using
coding-theoretic tools called "codes" is an emerging technique to alleviate the
adverse effects of such unavailabilities. A code consists of an encoding
function that proactively introduces redundant computation and a decoding
function that reconstructs unavailable outputs using the available ones. Past
work focuses on using codes to provide resilience for linear computations and
specific iterative optimization algorithms. However, computations performed for
a variety of applications including inference on state-of-the-art machine
learning algorithms, such as neural networks, typically fall outside this
realm. In this paper, we propose taking a learning-based approach to designing
codes that can handle non-linear computations. We present carefully designed
neural network architectures and a training methodology for learning encoding
and decoding functions that produce approximate reconstructions of unavailable
computation results. We present extensive experimental results demonstrating
the effectiveness of the proposed approach: we show that the our learned codes
can accurately reconstruct $64 - 98\%$ of the unavailable predictions from
neural-network based image classifiers on the MNIST, Fashion-MNIST, and
CIFAR-10 datasets. To the best of our knowledge, this work proposes the first
learning-based approach for designing codes, and also presents the first
coding-theoretic solution that can provide resilience for any non-linear
(differentiable) computation. Our results show that learning can be an
effective technique for designing codes, and that learned codes are a highly
promising approach for bringing the benefits of coding to non-linear
computations.
|
To the best of our knowledge, this work proposes the first learning-based approach for designing codes, and also presents the first coding-theoretic solution that can provide resilience for any non-linear (differentiable) computation.
|
http://arxiv.org/abs/1806.01259v1
|
http://arxiv.org/pdf/1806.01259v1.pdf
| null |
[
"Jack Kosaian",
"K. V. Rashmi",
"Shivaram Venkataraman"
] |
[
"BIG-bench Machine Learning"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/agreement-based-learning
|
1806.01258
| null | null |
Agreement-based Learning
|
Model selection is a problem that has occupied machine learning researchers
for a long time. Recently, its importance has become evident through
applications in deep learning. We propose an agreement-based learning framework
that prevents many of the pitfalls associated with model selection. It relies
on coupling the training of multiple models by encouraging them to agree on
their predictions while training. In contrast with other model selection and
combination approaches used in machine learning, the proposed framework is
inspired by human learning. We also propose a learning algorithm defined within
this framework which manages to significantly outperform alternatives in
practice, and whose performance improves further with the availability of
unlabeled data. Finally, we describe a number of potential directions for
developing more flexible agreement-based learning algorithms.
| null |
http://arxiv.org/abs/1806.01258v1
|
http://arxiv.org/pdf/1806.01258v1.pdf
| null |
[
"Emmanouil Antonios Platanios"
] |
[
"BIG-bench Machine Learning",
"Model Selection"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ml-leaks-model-and-data-independent
|
1806.01246
| null | null |
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models
|
Machine learning (ML) has become a core component of many real-world
applications and training data is a key factor that drives current progress.
This huge success has led Internet companies to deploy machine learning as a
service (MLaaS). Recently, the first membership inference attack has shown that
extraction of information on the training set is possible in such MLaaS
settings, which has severe security and privacy implications.
However, the early demonstrations of the feasibility of such attacks have
many assumptions on the adversary, such as using multiple so-called shadow
models, knowledge of the target model structure, and having a dataset from the
same distribution as the target model's training data. We relax all these key
assumptions, thereby showing that such attacks are very broadly applicable at
low cost and thereby pose a more severe risk than previously thought. We
present the most comprehensive study so far on this emerging and developing
threat using eight diverse datasets which show the viability of the proposed
attacks across domains.
In addition, we propose the first effective defense mechanisms against such
broader class of membership inference attacks that maintain a high level of
utility of the ML model.
|
In addition, we propose the first effective defense mechanisms against such broader class of membership inference attacks that maintain a high level of utility of the ML model.
|
http://arxiv.org/abs/1806.01246v2
|
http://arxiv.org/pdf/1806.01246v2.pdf
| null |
[
"Ahmed Salem",
"Yang Zhang",
"Mathias Humbert",
"Pascal Berrang",
"Mario Fritz",
"Michael Backes"
] |
[
"BIG-bench Machine Learning",
"Inference Attack",
"Membership Inference Attack"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/marginal-singularity-and-the-benefits-of
|
1803.01833
| null | null |
Marginal Singularity, and the Benefits of Labels in Covariate-Shift
|
We present new minimax results that concisely capture the relative benefits of source and target labeled data, under covariate-shift. Namely, we show that the benefits of target labels are controlled by a transfer-exponent $\gamma$ that encodes how singular Q is locally w.r.t. P, and interestingly allows situations where transfer did not seem possible under previous insights. In fact, our new minimax analysis - in terms of $\gamma$ - reveals a continuum of regimes ranging from situations where target labels have little benefit, to regimes where target labels dramatically improve classification. We then show that a recently proposed semi-supervised procedure can be extended to adapt to unknown $\gamma$, and therefore requests labels only when beneficial, while achieving minimax transfer rates.
| null |
https://arxiv.org/abs/1803.01833v3
|
https://arxiv.org/pdf/1803.01833v3.pdf
| null |
[
"Samory Kpotufe",
"Guillaume Martinet"
] |
[
"General Classification"
] | 2018-03-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/graph-networks-as-learnable-physics-engines
|
1806.01242
| null | null |
Graph networks as learnable physics engines for inference and control
|
Understanding and interacting with everyday physical scenes requires rich
knowledge about the structure of the world, represented either implicitly in a
value or policy function, or explicitly in a transition model. Here we
introduce a new class of learnable models--based on graph networks--which
implement an inductive bias for object- and relation-centric representations of
complex, dynamical systems. Our results show that as a forward model, our
approach supports accurate predictions from real and simulated data, and
surprisingly strong and efficient generalization, across eight distinct
physical systems which we varied parametrically and structurally. We also found
that our inference model can perform system identification. Our models are also
differentiable, and support online planning via gradient-based trajectory
optimization, as well as offline policy optimization. Our framework offers new
opportunities for harnessing and exploiting rich knowledge about the world, and
takes a key step toward building machines with more human-like representations
of the world.
|
Understanding and interacting with everyday physical scenes requires rich knowledge about the structure of the world, represented either implicitly in a value or policy function, or explicitly in a transition model.
|
http://arxiv.org/abs/1806.01242v1
|
http://arxiv.org/pdf/1806.01242v1.pdf
|
ICML 2018 7
|
[
"Alvaro Sanchez-Gonzalez",
"Nicolas Heess",
"Jost Tobias Springenberg",
"Josh Merel",
"Martin Riedmiller",
"Raia Hadsell",
"Peter Battaglia"
] |
[
"Inductive Bias",
"Weather Forecasting"
] | 2018-06-04T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2220
|
http://proceedings.mlr.press/v80/sanchez-gonzalez18a/sanchez-gonzalez18a.pdf
|
graph-networks-as-learnable-physics-engines-1
| null |
[] |
https://paperswithcode.com/paper/diffeomorphic-learning
|
1806.01240
| null | null |
Diffeomorphic Learning
|
We introduce in this paper a learning paradigm in which the training data is transformed by a diffeomorphic transformation before prediction. The learning algorithm minimizes a cost function evaluating the prediction error on the training set penalized by the distance between the diffeomorphism and the identity. The approach borrows ideas from shape analysis where diffeomorphisms are estimated for shape and image alignment, and brings them in a previously unexplored setting, estimating, in particular diffeomorphisms in much larger dimensions. After introducing the concept and describing a learning algorithm, we present diverse applications, mostly with synthetic examples, demonstrating the potential of the approach, as well as some insight on how it can be improved.
| null |
https://arxiv.org/abs/1806.01240v3
|
https://arxiv.org/pdf/1806.01240v3.pdf
| null |
[
"Laurent Younes"
] |
[] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-graphs
|
1806.01235
| null | null |
Deep Graphs
|
We propose an algorithm for deep learning on networks and graphs. It relies
on the notion that many graph algorithms, such as PageRank, Weisfeiler-Lehman,
or Message Passing can be expressed as iterative vertex updates. Unlike
previous methods which rely on the ingenuity of the designer, Deep Graphs are
adaptive to the estimation problem. Training and deployment are both efficient,
since the cost is $O(|E| + |V|)$, where $E$ and $V$ are the sets of edges and
vertices respectively. In short, we learn the recurrent update functions rather
than positing their specific functional form. This yields an algorithm that
achieves excellent accuracy on both graph labeling and regression tasks.
| null |
http://arxiv.org/abs/1806.01235v1
|
http://arxiv.org/pdf/1806.01235v1.pdf
| null |
[
"Emmanouil Antonios Platanios",
"Alex Smola"
] |
[
"Ingenuity",
"regression"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/image-reconstruction-through-metamorphosis
|
1806.01225
| null | null |
Image reconstruction through metamorphosis
|
This article adapts the framework of metamorphosis to solve inverse problems
in imaging that includes joint reconstruction and image registration. The
deformations in question have two components, one that is a geometric
deformation moving intensities and the other a deformation of intensity values
itself, which, e.g., allows for appearance of a new structure. The idea
developed here is to reconstruct an image from noisy and indirect observations
by registering, via metamorphosis, a template to the observed data. Unlike a
registration with only geometrical changes, this framework gives good results
when intensities of the template are poorly chosen. We show that this method is
a well-defined regularisation method (proving existence, stability and
convergence) and present several numerical examples.
| null |
http://arxiv.org/abs/1806.01225v2
|
http://arxiv.org/pdf/1806.01225v2.pdf
| null |
[
"Gris Barbara",
"Chen Chong",
"Öktem Ozan"
] |
[
"Image Reconstruction",
"Image Registration"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/challenges-in-high-dimensional-reinforcement
|
1806.01224
| null | null |
Challenges in High-dimensional Reinforcement Learning with Evolution Strategies
|
Evolution Strategies (ESs) have recently become popular for training deep
neural networks, in particular on reinforcement learning tasks, a special form
of controller design. Compared to classic problems in continuous direct search,
deep networks pose extremely high-dimensional optimization problems, with many
thousands or even millions of variables. In addition, many control problems
give rise to a stochastic fitness function. Considering the relevance of the
application, we study the suitability of evolution strategies for
high-dimensional, stochastic problems. Our results give insights into which
algorithmic mechanisms of modern ES are of value for the class of problems at
hand, and they reveal principled limitations of the approach. They are in line
with our theoretical understanding of ESs. We show that combining ESs that
offer reduced internal algorithm cost with uncertainty handling techniques
yields promising methods for this class of problems.
|
Our results give insights into which algorithmic mechanisms of modern ES are of value for the class of problems at hand, and they reveal principled limitations of the approach.
|
http://arxiv.org/abs/1806.01224v2
|
http://arxiv.org/pdf/1806.01224v2.pdf
| null |
[
"Nils Müller",
"Tobias Glasmachers"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"Vocal Bursts Intensity Prediction"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/relational-inductive-bias-for-physical
|
1806.01203
| null | null |
Relational inductive bias for physical construction in humans and machines
|
While current deep learning systems excel at tasks such as object
classification, language processing, and gameplay, few can construct or modify
a complex system such as a tower of blocks. We hypothesize that what these
systems lack is a "relational inductive bias": a capacity for reasoning about
inter-object relations and making choices over a structured description of a
scene. To test this hypothesis, we focus on a task that involves gluing pairs
of blocks together to stabilize a tower, and quantify how well humans perform.
We then introduce a deep reinforcement learning agent which uses object- and
relation-centric scene and policy representations and apply it to the task. Our
results show that these structured representations allow the agent to
outperform both humans and more naive approaches, suggesting that relational
inductive bias is an important component in solving structured reasoning
problems and for building more intelligent, flexible machines.
| null |
http://arxiv.org/abs/1806.01203v1
|
http://arxiv.org/pdf/1806.01203v1.pdf
| null |
[
"Jessica B. Hamrick",
"Kelsey R. Allen",
"Victor Bapst",
"Tina Zhu",
"Kevin R. McKee",
"Joshua B. Tenenbaum",
"Peter W. Battaglia"
] |
[
"Deep Reinforcement Learning",
"Inductive Bias",
"Object",
"Reinforcement Learning"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face
|
1806.01196
| null | null |
Face Synthesis for Eyeglass-Robust Face Recognition
|
In the application of face recognition, eyeglasses could significantly degrade the recognition accuracy. A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods. However, it is difficult to collect the images with and without glasses of the same identity, so that it is difficult to optimize the intra-variations caused by eyeglasses. In this paper, we propose to address this problem in a virtual synthesis manner. The high-fidelity face images with eyeglasses are synthesized based on 3D face model and 3D eyeglasses. Models based on deep learning methods are then trained on the synthesized eyeglass face dataset, achieving better performance than previous ones. Experiments on the real face database validate the effectiveness of our synthesized data for improving eyeglass face recognition performance.
|
A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods.
|
https://arxiv.org/abs/1806.01196v2
|
https://arxiv.org/pdf/1806.01196v2.pdf
| null |
[
"Jianzhu Guo",
"Xiangyu Zhu",
"Zhen Lei",
"Stan Z. Li"
] |
[
"Deep Learning",
"Face Generation",
"Face Model",
"Face Recognition",
"Robust Face Recognition"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/measuring-and-avoiding-side-effects-using
|
1806.01186
| null | null |
Penalizing side effects using stepwise relative reachability
|
How can we design safe reinforcement learning agents that avoid unnecessary
disruptions to their environment? We show that current approaches to penalizing
side effects can introduce bad incentives, e.g. to prevent any irreversible
changes in the environment, including the actions of other agents. To isolate
the source of such undesirable incentives, we break down side effects penalties
into two components: a baseline state and a measure of deviation from this
baseline state. We argue that some of these incentives arise from the choice of
baseline, and others arise from the choice of deviation measure. We introduce a
new variant of the stepwise inaction baseline and a new deviation measure based
on relative reachability of states. The combination of these design choices
avoids the given undesirable incentives, while simpler baselines and the
unreachability measure fail. We demonstrate this empirically by comparing
different combinations of baseline and deviation measure choices on a set of
gridworld experiments designed to illustrate possible bad incentives.
| null |
http://arxiv.org/abs/1806.01186v2
|
http://arxiv.org/pdf/1806.01186v2.pdf
| null |
[
"Victoria Krakovna",
"Laurent Orseau",
"Ramana Kumar",
"Miljan Martic",
"Shane Legg"
] |
[
"Reinforcement Learning",
"Safe Reinforcement Learning"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/an-unsupervised-approach-to-solving-inverse
|
1805.07281
| null | null |
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks
|
Solving inverse problems continues to be a challenge in a wide array of
applications ranging from deblurring, image inpainting, source separation etc.
Most existing techniques solve such inverse problems by either explicitly or
implicitly finding the inverse of the model. The former class of techniques
require explicit knowledge of the measurement process which can be unrealistic,
and rely on strong analytical regularizers to constrain the solution space,
which often do not generalize well. The latter approaches have had remarkable
success in part due to deep learning, but require a large collection of
source-observation pairs, which can be prohibitively expensive. In this paper,
we propose an unsupervised technique to solve inverse problems with generative
adversarial networks (GANs). Using a pre-trained GAN in the space of source
signals, we show that one can reliably recover solutions to under determined
problems in a `blind' fashion, i.e., without knowledge of the measurement
process. We solve this by making successive estimates on the model and the
solution in an iterative fashion. We show promising results in three
challenging applications -- blind source separation, image deblurring, and
recovering an image from its edge map, and perform better than several
baselines.
| null |
http://arxiv.org/abs/1805.07281v2
|
http://arxiv.org/pdf/1805.07281v2.pdf
| null |
[
"Rushil Anirudh",
"Jayaraman J. Thiagarajan",
"Bhavya Kailkhura",
"Timo Bremer"
] |
[
"blind source separation",
"Deblurring",
"Image Deblurring",
"Image Inpainting"
] | 2018-05-18T00:00:00 | null | null | null | 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": "",
"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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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/history-playground-a-tool-for-discovering
|
1806.01185
| null | null |
History Playground: A Tool for Discovering Temporal Trends in Massive Textual Corpora
|
Recent studies have shown that macroscopic patterns of continuity and change
over the course of centuries can be detected through the analysis of time
series extracted from massive textual corpora. Similar data-driven approaches
have already revolutionised the natural sciences, and are widely believed to
hold similar potential for the humanities and social sciences, driven by the
mass-digitisation projects that are currently under way, and coupled with the
ever-increasing number of documents which are "born digital". As such, new
interactive tools are required to discover and extract macroscopic patterns
from these vast quantities of textual data. Here we present History Playground,
an interactive web-based tool for discovering trends in massive textual
corpora. The tool makes use of scalable algorithms to first extract trends from
textual corpora, before making them available for real-time search and
discovery, presenting users with an interface to explore the data. Included in
the tool are algorithms for standardization, regression, change-point detection
in the relative frequencies of ngrams, multi-term indices and comparison of
trends across different corpora.
| null |
http://arxiv.org/abs/1806.01185v1
|
http://arxiv.org/pdf/1806.01185v1.pdf
| null |
[
"Thomas Lansdall-Welfare",
"Nello Cristianini"
] |
[
"Change Point Detection",
"Time Series",
"Time Series Analysis"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/gradient-based-filter-design-for-the-dual
|
1806.01793
| null | null |
Gradient-based Filter Design for the Dual-tree Wavelet Transform
|
The wavelet transform has seen success when incorporated into neural network
architectures, such as in wavelet scattering networks. More recently, it has
been shown that the dual-tree complex wavelet transform can provide better
representations than the standard transform. With this in mind, we extend our
previous method for learning filters for the 1D and 2D wavelet transforms into
the dual-tree domain. We show that with few modifications to our original
model, we can learn directional filters that leverage the properties of the
dual-tree wavelet transform.
| null |
http://arxiv.org/abs/1806.01793v1
|
http://arxiv.org/pdf/1806.01793v1.pdf
| null |
[
"Daniel Recoskie",
"Richard Mann"
] |
[] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/experimental-tests-of-spirituality
|
1806.01661
| null | null |
Experimental Tests of Spirituality
|
We currently harness technologies that could shed new light on old
philosophical questions, such as whether our mind entails anything beyond our
body or whether our moral values reflect universal truth.
| null |
http://arxiv.org/abs/1806.01661v1
|
http://arxiv.org/pdf/1806.01661v1.pdf
| null |
[
"Abraham Loeb"
] |
[] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-continuous-conditional-random-fields
|
1806.01183
| null | null |
Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking
|
Online Multi-Object Tracking (MOT) is a challenging problem and has many
important applications including intelligence surveillance, robot navigation
and autonomous driving. In existing MOT methods, individual object's movements
and inter-object relations are mostly modeled separately and relations between
them are still manually tuned. In addition, inter-object relations are mostly
modeled in a symmetric way, which we argue is not an optimal setting. To tackle
those difficulties, in this paper, we propose a Deep Continuous Conditional
Random Field (DCCRF) for solving the online MOT problem in a track-by-detection
framework. The DCCRF consists of unary and pairwise terms. The unary terms
estimate tracked objects' displacements across time based on visual appearance
information. They are modeled as deep Convolution Neural Networks, which are
able to learn discriminative visual features for tracklet association. The
asymmetric pairwise terms model inter-object relations in an asymmetric way,
which encourages high-confidence tracklets to help correct errors of
low-confidence tracklets and not to be affected by low-confidence ones much.
The DCCRF is trained in an end-to-end manner for better adapting the influences
of visual information as well as inter-object relations. Extensive experimental
comparisons with state-of-the-arts as well as detailed component analysis of
our proposed DCCRF on two public benchmarks demonstrate the effectiveness of
our proposed MOT framework.
| null |
http://arxiv.org/abs/1806.01183v1
|
http://arxiv.org/pdf/1806.01183v1.pdf
| null |
[
"Hui Zhou",
"Wanli Ouyang",
"Jian Cheng",
"Xiaogang Wang",
"Hongsheng Li"
] |
[
"Autonomous Driving",
"Multi-Object Tracking",
"Object",
"Object Tracking",
"Online Multi-Object Tracking",
"Robot Navigation"
] | 2018-06-04T00:00:00 | null | null | null | 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
}
] |
https://paperswithcode.com/paper/online-reciprocal-recommendation-with
|
1806.01182
| null | null |
Online Reciprocal Recommendation with Theoretical Performance Guarantees
|
A reciprocal recommendation problem is one where the goal of learning is not
just to predict a user's preference towards a passive item (e.g., a book), but
to recommend the targeted user on one side another user from the other side
such that a mutual interest between the two exists. The problem thus is sharply
different from the more traditional items-to-users recommendation, since a good
match requires meeting the preferences of both users. We initiate a rigorous
theoretical investigation of the reciprocal recommendation task in a specific
framework of sequential learning. We point out general limitations, formulate
reasonable assumptions enabling effective learning and, under these
assumptions, we design and analyze a computationally efficient algorithm that
uncovers mutual likes at a pace comparable to those achieved by a clearvoyant
algorithm knowing all user preferences in advance. Finally, we validate our
algorithm against synthetic and real-world datasets, showing improved empirical
performance over simple baselines.
| null |
http://arxiv.org/abs/1806.01182v1
|
http://arxiv.org/pdf/1806.01182v1.pdf
|
NeurIPS 2018 12
|
[
"Fabio Vitale",
"Nikos Parotsidis",
"Claudio Gentile"
] |
[] | 2018-06-04T00:00:00 |
http://papers.nips.cc/paper/8047-online-reciprocal-recommendation-with-theoretical-performance-guarantees
|
http://papers.nips.cc/paper/8047-online-reciprocal-recommendation-with-theoretical-performance-guarantees.pdf
|
online-reciprocal-recommendation-with-1
| null |
[] |
https://paperswithcode.com/paper/the-anatomy-of-a-modular-system-for-media
|
1402.6208
| null | null |
The Anatomy of a Modular System for Media Content Analysis
|
Intelligent systems for the annotation of media content are increasingly
being used for the automation of parts of social science research. In this
domain the problem of integrating various Artificial Intelligence (AI)
algorithms into a single intelligent system arises spontaneously. As part of
our ongoing effort in automating media content analysis for the social
sciences, we have built a modular system by combining multiple AI modules into
a flexible framework in which they can cooperate in complex tasks. Our system
combines data gathering, machine translation, topic classification, extraction
and annotation of entities and social networks, as well as many other tasks
that have been perfected over the past years of AI research. Over the last few
years, it has allowed us to realise a series of scientific studies over a vast
range of applications including comparative studies between news outlets and
media content in different countries, modelling of user preferences, and
monitoring public mood. The framework is flexible and allows the design and
implementation of modular agents, where simple modules cooperate in the
annotation of a large dataset without central coordination.
| null |
http://arxiv.org/abs/1402.6208v2
|
http://arxiv.org/pdf/1402.6208v2.pdf
| null |
[
"Ilias Flaounas",
"Thomas Lansdall-Welfare",
"Panagiota Antonakaki",
"Nello Cristianini"
] |
[
"Anatomy",
"Machine Translation",
"Topic Classification",
"Translation"
] | 2014-02-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/td-or-not-td-analyzing-the-role-of-temporal
|
1806.01175
| null |
HyiAuyb0b
|
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
|
Our understanding of reinforcement learning (RL) has been shaped by
theoretical and empirical results that were obtained decades ago using tabular
representations and linear function approximators. These results suggest that
RL methods that use temporal differencing (TD) are superior to direct Monte
Carlo estimation (MC). How do these results hold up in deep RL, which deals
with perceptually complex environments and deep nonlinear models? In this
paper, we re-examine the role of TD in modern deep RL, using specially designed
environments that control for specific factors that affect performance, such as
reward sparsity, reward delay, and the perceptual complexity of the task. When
comparing TD with infinite-horizon MC, we are able to reproduce classic results
in modern settings. Yet we also find that finite-horizon MC is not inferior to
TD, even when rewards are sparse or delayed. This makes MC a viable alternative
to TD in deep RL.
|
Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using tabular representations and linear function approximators.
|
http://arxiv.org/abs/1806.01175v1
|
http://arxiv.org/pdf/1806.01175v1.pdf
|
ICLR 2018 1
|
[
"Artemij Amiranashvili",
"Alexey Dosovitskiy",
"Vladlen Koltun",
"Thomas Brox"
] |
[
"Deep Reinforcement Learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-04T00:00:00 |
https://openreview.net/forum?id=HyiAuyb0b
|
https://openreview.net/pdf?id=HyiAuyb0b
|
td-or-not-td-analyzing-the-role-of-temporal-1
| null |
[] |
https://paperswithcode.com/paper/do-neural-network-cross-modal-mappings-really
|
1805.07616
| null | null |
Do Neural Network Cross-Modal Mappings Really Bridge Modalities?
|
Feed-forward networks are widely used in cross-modal applications to bridge
modalities by mapping distributed vectors of one modality to the other, or to a
shared space. The predicted vectors are then used to perform e.g., retrieval or
labeling. Thus, the success of the whole system relies on the ability of the
mapping to make the neighborhood structure (i.e., the pairwise similarities) of
the predicted vectors akin to that of the target vectors. However, whether this
is achieved has not been investigated yet. Here, we propose a new similarity
measure and two ad hoc experiments to shed light on this issue. In three
cross-modal benchmarks we learn a large number of language-to-vision and
vision-to-language neural network mappings (up to five layers) using a rich
diversity of image and text features and loss functions. Our results reveal
that, surprisingly, the neighborhood structure of the predicted vectors
consistently resembles more that of the input vectors than that of the target
vectors. In a second experiment, we further show that untrained nets do not
significantly disrupt the neighborhood (i.e., semantic) structure of the input
vectors.
| null |
http://arxiv.org/abs/1805.07616v2
|
http://arxiv.org/pdf/1805.07616v2.pdf
|
ACL 2018 7
|
[
"Guillem Collell",
"Marie-Francine Moens"
] |
[
"Diversity",
"Retrieval"
] | 2018-05-19T00:00:00 |
https://aclanthology.org/P18-2074
|
https://aclanthology.org/P18-2074.pdf
|
do-neural-network-cross-modal-mappings-really-1
| null |
[] |
https://paperswithcode.com/paper/efficient-online-scalar-annotation-with
|
1806.01170
| null | null |
Efficient Online Scalar Annotation with Bounded Support
|
We describe a novel method for efficiently eliciting scalar annotations for
dataset construction and system quality estimation by human judgments. We
contrast direct assessment (annotators assign scores to items directly), online
pairwise ranking aggregation (scores derive from annotator comparison of
items), and a hybrid approach (EASL: Efficient Annotation of Scalar Labels)
proposed here. Our proposal leads to increased correlation with ground truth,
at far greater annotator efficiency, suggesting this strategy as an improved
mechanism for dataset creation and manual system evaluation.
| null |
http://arxiv.org/abs/1806.01170v1
|
http://arxiv.org/pdf/1806.01170v1.pdf
|
ACL 2018 7
|
[
"Keisuke Sakaguchi",
"Benjamin Van Durme"
] |
[] | 2018-06-04T00:00:00 |
https://aclanthology.org/P18-1020
|
https://aclanthology.org/P18-1020.pdf
|
efficient-online-scalar-annotation-with-1
| null |
[] |
https://paperswithcode.com/paper/an-ideal-observer-model-to-probe-human-visual
|
1806.00111
| null | null |
Probabilistic Model of Visual Segmentation
|
Visual segmentation is a key perceptual function that partitions visual space
and allows for detection, recognition and discrimination of objects in complex
environments. The processes underlying human segmentation of natural images are
still poorly understood. In part, this is because we lack segmentation models
consistent with experimental and theoretical knowledge in visual neuroscience.
Biological sensory systems have been shown to approximate probabilistic
inference to interpret their inputs. This requires a generative model that
captures both the statistics of the sensory inputs and expectations about the
causes of those inputs. Following this hypothesis, we propose a probabilistic
generative model of visual segmentation that combines knowledge about 1) the
sensitivity of neurons in the visual cortex to statistical regularities in
natural images; and 2) the preference of humans to form contiguous partitions
of visual space. We develop an efficient algorithm for training and inference
based on expectation-maximization and validate it on synthetic data.
Importantly, with the appropriate choice of the prior, we derive an intuitive
closed--form update rule for assigning pixels to segments: at each iteration,
the pixel assignment probabilities to segments is the sum of the evidence (i.e.
local pixel statistics) and prior (i.e. the assignments of neighboring pixels)
weighted by their relative uncertainty. The model performs competitively on
natural images from the Berkeley Segmentation Dataset (BSD), and we illustrate
how the likelihood and prior components improve segmentation relative to
traditional mixture models. Furthermore, our model explains some variability
across human subjects as reflecting local uncertainty about the number of
segments. Our model thus provides a viable approach to probe human visual
segmentation.
| null |
http://arxiv.org/abs/1806.00111v3
|
http://arxiv.org/pdf/1806.00111v3.pdf
| null |
[
"Jonathan Vacher",
"Pascal Mamassian",
"Ruben Coen-Cagli"
] |
[
"model",
"Segmentation",
"Semantic Segmentation"
] | 2018-05-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/explaining-explanations-an-overview-of
|
1806.00069
| null | null |
Explaining Explanations: An Overview of Interpretability of Machine Learning
|
There has recently been a surge of work in explanatory artificial
intelligence (XAI). This research area tackles the important problem that
complex machines and algorithms often cannot provide insights into their
behavior and thought processes. XAI allows users and parts of the internal
system to be more transparent, providing explanations of their decisions in
some level of detail. These explanations are important to ensure algorithmic
fairness, identify potential bias/problems in the training data, and to ensure
that the algorithms perform as expected. However, explanations produced by
these systems is neither standardized nor systematically assessed. In an effort
to create best practices and identify open challenges, we provide our
definition of explainability and show how it can be used to classify existing
literature. We discuss why current approaches to explanatory methods especially
for deep neural networks are insufficient. Finally, based on our survey, we
conclude with suggested future research directions for explanatory artificial
intelligence.
|
There has recently been a surge of work in explanatory artificial intelligence (XAI).
|
http://arxiv.org/abs/1806.00069v3
|
http://arxiv.org/pdf/1806.00069v3.pdf
| null |
[
"Leilani H. Gilpin",
"David Bau",
"Ben Z. Yuan",
"Ayesha Bajwa",
"Michael Specter",
"Lalana Kagal"
] |
[
"BIG-bench Machine Learning",
"Explainable Artificial Intelligence (XAI)",
"Fairness"
] | 2018-05-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/unleashing-linear-optimizers-for-group-fair
|
1804.04503
| null | null |
Unleashing Linear Optimizers for Group-Fair Learning and Optimization
|
Most systems and learning algorithms optimize average performance or average
loss -- one reason being computational complexity. However, many objectives of
practical interest are more complex than simply average loss. This arises, for
example, when balancing performance or loss with fairness across people. We
prove that, from a computational perspective, optimizing arbitrary objectives
that take into account performance over a small number of groups is not
significantly harder to optimize than average performance. Our main result is a
polynomial-time reduction that uses a linear optimizer to optimize an arbitrary
(Lipschitz continuous) function of performance over a (constant) number of
possibly-overlapping groups. This includes fairness objectives over small
numbers of groups, and we further point out that other existing notions of
fairness such as individual fairness can be cast as convex optimization and
hence more standard convex techniques can be used. Beyond learning, our
approach applies to multi-objective optimization, more generally.
| null |
http://arxiv.org/abs/1804.04503v2
|
http://arxiv.org/pdf/1804.04503v2.pdf
| null |
[
"Daniel Alabi",
"Nicole Immorlica",
"Adam Tauman Kalai"
] |
[
"Fairness"
] | 2018-04-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-and-scalable-batch-bayesian
|
1806.01159
| null | null |
Efficient and Scalable Batch Bayesian Optimization Using K-Means
|
We present K-Means Batch Bayesian Optimization (KMBBO), a novel batch
sampling algorithm for Bayesian Optimization (BO). KMBBO uses unsupervised
learning to efficiently estimate peaks of the model acquisition function. We
show in empirical experiments that our method outperforms the current
state-of-the-art batch allocation algorithms on a variety of test problems
including tuning of algorithm hyper-parameters and a challenging drug discovery
problem. In order to accommodate the real-world problem of high dimensional
data, we propose a modification to KMBBO by combining it with compressed
sensing to project the optimization into a lower dimensional subspace. We
demonstrate empirically that this 2-step method outperforms algorithms where no
dimensionality reduction has taken place.
| null |
http://arxiv.org/abs/1806.01159v2
|
http://arxiv.org/pdf/1806.01159v2.pdf
| null |
[
"Matthew Groves",
"Edward O. Pyzer-Knapp"
] |
[
"Bayesian Optimization",
"compressed sensing",
"Dimensionality Reduction",
"Drug Discovery"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/shallow-decision-making-analysis-in-general
|
1806.01151
| null | null |
Shallow decision-making analysis in General Video Game Playing
|
The General Video Game AI competitions have been the testing ground for
several techniques for game playing, such as evolutionary computation
techniques, tree search algorithms, hyper heuristic based or knowledge based
algorithms. So far the metrics used to evaluate the performance of agents have
been win ratio, game score and length of games. In this paper we provide a
wider set of metrics and a comparison method for evaluating and comparing
agents. The metrics and the comparison method give shallow introspection into
the agent's decision making process and they can be applied to any agent
regardless of its algorithmic nature. In this work, the metrics and the
comparison method are used to measure the impact of the terms that compose a
tree policy of an MCTS based agent, comparing with several baseline agents. The
results clearly show how promising such general approach is and how it can be
useful to understand the behaviour of an AI agent, in particular, how the
comparison with baseline agents can help understanding the shape of the agent
decision landscape. The presented metrics and comparison method represent a
step toward to more descriptive ways of logging and analysing agent's
behaviours.
|
The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hyper heuristic based or knowledge based algorithms.
|
http://arxiv.org/abs/1806.01151v1
|
http://arxiv.org/pdf/1806.01151v1.pdf
| null |
[
"Ivan Bravi",
"Jialin Liu",
"Diego Perez-Liebana",
"Simon Lucas"
] |
[
"AI Agent",
"Decision Making",
"Descriptive"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-from-exemplars-and-prototypes-in
|
1806.01130
| null | null |
Learning from Exemplars and Prototypes in Machine Learning and Psychology
|
This paper draws a parallel between similarity-based categorisation models
developed in cognitive psychology and the nearest neighbour classifier (1-NN)
in machine learning. Conceived as a result of the historical rivalry between
prototype theories (abstraction) and exemplar theories (memorisation), recent
models of human categorisation seek a compromise in-between. Regarding the
stimuli (entities to be categorised) as points in a metric space, machine
learning offers a large collection of methods to select a small, representative
and discriminative point set. These methods are known under various names:
instance selection, data editing, prototype selection, prototype generation or
prototype replacement. The nearest neighbour classifier is used with the
selected reference set. Such a set can be interpreted as a data-driven
categorisation model. We juxtapose the models from the two fields to enable
cross-referencing. We believe that both machine learning and cognitive
psychology can draw inspiration from the comparison and enrich their repertoire
of similarity-based models.
| null |
http://arxiv.org/abs/1806.01130v1
|
http://arxiv.org/pdf/1806.01130v1.pdf
| null |
[
"Julian Zubek",
"Ludmila Kuncheva"
] |
[
"BIG-bench Machine Learning",
"Prototype Selection"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ring-migration-topology-helps-bypassing-local
|
1806.01128
| null | null |
Ring Migration Topology Helps Bypassing Local Optima
|
Running several evolutionary algorithms in parallel and occasionally
exchanging good solutions is referred to as island models. The idea is that the
independence of the different islands leads to diversity, thus possibly
exploring the search space better. Many theoretical analyses so far have found
a complete (or sufficiently quickly expanding) topology as underlying migration
graph most efficient for optimization, even though a quick dissemination of
individuals leads to a loss of diversity. We suggest a simple fitness function
FORK with two local optima parametrized by $r \geq 2$ and a scheme for
composite fitness functions. We show that, while the (1+1) EA gets stuck in a
bad local optimum and incurs a run time of $\Theta(n^{2r})$ fitness evaluations
on FORK, island models with a complete topology can achieve a run time of
$\Theta(n^{1.5r})$ by making use of rare migrations in order to explore the
search space more effectively. Finally, the ring topology, making use of rare
migrations and a large diameter, can achieve a run time of
$\tilde{\Theta}(n^r)$, the black box complexity of FORK. This shows that the
ring topology can be preferable over the complete topology in order to maintain
diversity.
| null |
http://arxiv.org/abs/1806.01128v1
|
http://arxiv.org/pdf/1806.01128v1.pdf
| null |
[
"Clemens Frahnow",
"Timo Kötzing"
] |
[
"Diversity",
"Evolutionary Algorithms"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/modeling-realistic-degradations-in-non-blind
|
1806.01097
| null | null |
Modeling Realistic Degradations in Non-blind Deconvolution
|
Most image deblurring methods assume an over-simplistic image formation model
and as a result are sensitive to more realistic image degradations. We propose
a novel variational framework, that explicitly handles pixel saturation, noise,
quantization, as well as non-linear camera response function due to e.g., gamma
correction. We show that accurately modeling a more realistic image acquisition
pipeline leads to significant improvements, both in terms of image quality and
PSNR. Furthermore, we show that incorporating the non-linear response in both
the data and the regularization terms of the proposed energy leads to a more
detailed restoration than a naive inversion of the non-linear curve. The
minimization of the proposed energy is performed using stochastic optimization.
A dataset consisting of realistically degraded images is created in order to
evaluate the method.
| null |
http://arxiv.org/abs/1806.01097v1
|
http://arxiv.org/pdf/1806.01097v1.pdf
| null |
[
"Jérémy Anger",
"Mauricio Delbracio",
"Gabriele Facciolo"
] |
[
"Deblurring",
"Image Deblurring",
"Quantization",
"Stochastic Optimization"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/robustifying-independent-component-analysis
|
1806.01094
| null | null |
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise
|
We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding. It extends the ordinary ICA model in a theoretically sound and explicit way to incorporate group-wise (or environment-wise) confounding. We show that our proposed general noise model allows to perform ICA in settings where other noisy ICA procedures fail. Additionally, it can be used for applications with grouped data by adjusting for different stationary noise within each group. Our proposed noise model has a natural relation to causality and we explain how it can be applied in the context of causal inference. In addition to our theoretical framework, we provide an efficient estimation procedure and prove identifiability of the unmixing matrix under mild assumptions. Finally, we illustrate the performance and robustness of our method on simulated data, provide audible and visual examples, and demonstrate the applicability to real-world scenarios by experiments on publicly available Antarctic ice core data as well as two EEG data sets. We provide a scikit-learn compatible pip-installable Python package coroICA as well as R and Matlab implementations accompanied by a documentation at https://sweichwald.de/coroICA/
|
We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding.
|
https://arxiv.org/abs/1806.01094v3
|
https://arxiv.org/pdf/1806.01094v3.pdf
| null |
[
"Niklas Pfister",
"Sebastian Weichwald",
"Peter Bühlmann",
"Bernhard Schölkopf"
] |
[
"Causal Inference",
"EEG",
"Electroencephalogram (EEG)"
] | 2018-06-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "_**Independent component analysis** (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals._\r\n\r\n_ICA defines a generative model for the observed multivariate data, which is typically given as a large database of samples. In the model, the data variables are assumed to be linear mixtures of some unknown latent variables, and the mixing system is also unknown. The latent variables are assumed nongaussian and mutually independent, and they are called the independent components of the observed data. These independent components, also called sources or factors, can be found by ICA._\r\n\r\n_ICA is superficially related to principal component analysis and factor analysis. ICA is a much more powerful technique, however, capable of finding the underlying factors or sources when these classic methods fail completely._\r\n\r\n\r\nExtracted from (https://www.cs.helsinki.fi/u/ahyvarin/whatisica.shtml)\r\n\r\n**Source papers**:\r\n\r\n[Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture](https://doi.org/10.1016/0165-1684(91)90079-X)\r\n\r\n[Independent component analysis, A new concept?](https://doi.org/10.1016/0165-1684(94)90029-9)\r\n\r\n[Independent component analysis: algorithms and applications](https://doi.org/10.1016/S0893-6080(00)00026-5)",
"full_name": "Independent Component Analysis",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Dimensionality Reduction** methods transform data from a high-dimensional space into a low-dimensional space so that the low-dimensional space retains the most important properties of the original data. Below you can find a continuously updating list of dimensionality reduction methods.",
"name": "Dimensionality Reduction",
"parent": null
},
"name": "ICA",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/the-expanding-approvals-rule-improving
|
1708.07580
| null | null |
The Expanding Approvals Rule: Improving Proportional Representation and Monotonicity
|
Proportional representation (PR) is often discussed in voting settings as a
major desideratum. For the past century or so, it is common both in practice
and in the academic literature to jump to single transferable vote (STV) as the
solution for achieving PR. Some of the most prominent electoral reform
movements around the globe are pushing for the adoption of STV. It has been
termed a major open problem to design a voting rule that satisfies the same PR
properties as STV and better monotonicity properties. In this paper, we first
present a taxonomy of proportional representation axioms for general weak order
preferences, some of which generalise and strengthen previously introduced
concepts. We then present a rule called Expanding Approvals Rule (EAR) that
satisfies properties stronger than the central PR axiom satisfied by STV, can
handle indifferences in a convenient and computationally efficient manner, and
also satisfies better candidate monotonicity properties. In view of this, our
proposed rule seems to be a compelling solution for achieving proportional
representation in voting settings.
| null |
http://arxiv.org/abs/1708.07580v2
|
http://arxiv.org/pdf/1708.07580v2.pdf
| null |
[
"Haris Aziz",
"Barton Lee"
] |
[] | 2017-08-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ell_1-regression-with-heavy-tailed
|
1805.00616
| null | null |
$\ell_1$-regression with Heavy-tailed Distributions
|
In this paper, we consider the problem of linear regression with heavy-tailed
distributions. Different from previous studies that use the squared loss to
measure the performance, we choose the absolute loss, which is capable of
estimating the conditional median. To address the challenge that both the input
and output could be heavy-tailed, we propose a truncated minimization problem,
and demonstrate that it enjoys an $\widetilde{O}(\sqrt{d/n})$ excess risk,
where $d$ is the dimensionality and $n$ is the number of samples. Compared with
traditional work on $\ell_1$-regression, the main advantage of our result is
that we achieve a high-probability risk bound without exponential moment
conditions on the input and output. Furthermore, if the input is bounded, we
show that the classical empirical risk minimization is competent for
$\ell_1$-regression even when the output is heavy-tailed.
| null |
http://arxiv.org/abs/1805.00616v4
|
http://arxiv.org/pdf/1805.00616v4.pdf
|
NeurIPS 2018
|
[
"Lijun Zhang",
"Zhi-Hua Zhou"
] |
[
"regression"
] | 2018-05-02T00:00:00 | null | null | null | 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/dynamic-regret-of-strongly-adaptive-methods
|
1701.07570
| null | null |
Dynamic Regret of Strongly Adaptive Methods
|
To cope with changing environments, recent developments in online learning
have introduced the concepts of adaptive regret and dynamic regret
independently. In this paper, we illustrate an intrinsic connection between
these two concepts by showing that the dynamic regret can be expressed in terms
of the adaptive regret and the functional variation. This observation implies
that strongly adaptive algorithms can be directly leveraged to minimize the
dynamic regret. As a result, we present a series of strongly adaptive
algorithms that have small dynamic regrets for convex functions, exponentially
concave functions, and strongly convex functions, respectively. To the best of
our knowledge, this is the first time that exponential concavity is utilized to
upper bound the dynamic regret. Moreover, all of those adaptive algorithms do
not need any prior knowledge of the functional variation, which is a
significant advantage over previous specialized methods for minimizing dynamic
regret.
| null |
http://arxiv.org/abs/1701.07570v3
|
http://arxiv.org/pdf/1701.07570v3.pdf
|
ICML 2018 7
|
[
"Lijun Zhang",
"Tianbao Yang",
"Rong Jin",
"Zhi-Hua Zhou"
] |
[] | 2017-01-26T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1989
|
http://proceedings.mlr.press/v80/zhang18o/zhang18o.pdf
|
dynamic-regret-of-strongly-adaptive-methods-1
| null |
[] |
https://paperswithcode.com/paper/optimal-balancing-of-time-dependent
|
1806.01083
| null | null |
Optimal Balancing of Time-Dependent Confounders for Marginal Structural Models
|
Marginal structural models (MSMs) estimate the causal effect of a time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approach of using inverse probability of treatment weighting (IPTW) can lead to high-variance estimates due to extreme weights and be sensitive to model misspecification. Various methods have been proposed to partially address this, including truncation and stabilized-IPTW to temper extreme weights and covariate balancing propensity score (CBPS) to address treatment model misspecification. In this paper, we present Kernel Optimal Weighting (KOW), a convex-optimization-based approach that finds weights for fitting the MSM that optimally balance time-dependent confounders while simultaneously controlling for precision, directly addressing the above limitations. KOW directly minimizes the error in estimation due to time-dependent confounding via a new decomposition as a functional. We further extend KOW to control for informative censoring. We evaluate the performance of KOW in a simulation study, comparing it with IPTW, stabilized-IPTW, and CBPS. We demonstrate the use of KOW in studying the effect of treatment initiation on time-to-death among people living with HIV and the effect of negative advertising on elections in the United States.
|
Marginal structural models (MSMs) estimate the causal effect of a time-varying treatment in the presence of time-dependent confounding via weighted regression.
|
https://arxiv.org/abs/1806.01083v2
|
https://arxiv.org/pdf/1806.01083v2.pdf
| null |
[
"Nathan Kallus",
"Michele Santacatterina"
] |
[] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/wasserstein-variational-inference
|
1805.11284
| null | null |
Wasserstein Variational Inference
|
This paper introduces Wasserstein variational inference, a new form of
approximate Bayesian inference based on optimal transport theory. Wasserstein
variational inference uses a new family of divergences that includes both
f-divergences and the Wasserstein distance as special cases. The gradients of
the Wasserstein variational loss are obtained by backpropagating through the
Sinkhorn iterations. This technique results in a very stable likelihood-free
training method that can be used with implicit distributions and probabilistic
programs. Using the Wasserstein variational inference framework, we introduce
several new forms of autoencoders and test their robustness and performance
against existing variational autoencoding techniques.
| null |
http://arxiv.org/abs/1805.11284v2
|
http://arxiv.org/pdf/1805.11284v2.pdf
|
NeurIPS 2018 12
|
[
"Luca Ambrogioni",
"Umut Güçlü",
"Yağmur Güçlütürk",
"Max Hinne",
"Eric Maris",
"Marcel A. J. van Gerven"
] |
[
"Bayesian Inference",
"Variational Inference"
] | 2018-05-29T00:00:00 |
http://papers.nips.cc/paper/7514-wasserstein-variational-inference
|
http://papers.nips.cc/paper/7514-wasserstein-variational-inference.pdf
|
wasserstein-variational-inference-1
| null |
[] |
https://paperswithcode.com/paper/deep-multi-structural-shape-analysis
|
1806.01069
| null | null |
Deep Multi-Structural Shape Analysis: Application to Neuroanatomy
|
We propose a deep neural network for supervised learning on neuroanatomical
shapes. The network directly operates on raw point clouds without the need for
mesh processing or the identification of point correspondences, as spatial
transformer networks map the data to a canonical space. Instead of relying on
hand-crafted shape descriptors, an optimal representation is learned in the
end-to-end training stage of the network. The proposed network consists of
multiple branches, so that features for multiple structures are learned
simultaneously. We demonstrate the performance of our method on two
applications: (i) the prediction of Alzheimer's disease and mild cognitive
impairment and (ii) the regression of the brain age. Finally, we visualize the
important parts of the anatomy for the prediction by adapting the occlusion
method to point clouds.
| null |
http://arxiv.org/abs/1806.01069v1
|
http://arxiv.org/pdf/1806.01069v1.pdf
| null |
[
"Benjamin Gutierrez-Becker",
"Christian Wachinger"
] |
[
"Anatomy",
"regression"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/open-information-extraction-on-scientific
|
1802.05574
| null | null |
Open Information Extraction on Scientific Text: An Evaluation
|
Open Information Extraction (OIE) is the task of the unsupervised creation of
structured information from text. OIE is often used as a starting point for a
number of downstream tasks including knowledge base construction, relation
extraction, and question answering. While OIE methods are targeted at being
domain independent, they have been evaluated primarily on newspaper,
encyclopedic or general web text. In this article, we evaluate the performance
of OIE on scientific texts originating from 10 different disciplines. To do so,
we use two state-of-the-art OIE systems applying a crowd-sourcing approach. We
find that OIE systems perform significantly worse on scientific text than
encyclopedic text. We also provide an error analysis and suggest areas of work
to reduce errors. Our corpus of sentences and judgments are made available.
| null |
http://arxiv.org/abs/1802.05574v2
|
http://arxiv.org/pdf/1802.05574v2.pdf
|
COLING 2018 8
|
[
"Paul Groth",
"Michael Lauruhn",
"Antony Scerri",
"Ron Daniel Jr"
] |
[
"Knowledge Base Construction",
"Open Information Extraction",
"Question Answering",
"Relation Extraction"
] | 2018-02-15T00:00:00 |
https://aclanthology.org/C18-1289
|
https://aclanthology.org/C18-1289.pdf
|
open-information-extraction-on-scientific-2
| null |
[] |
https://paperswithcode.com/paper/a-comparison-of-machine-learning-algorithms
|
1804.06223
| null | null |
A Comparison of Machine Learning Algorithms for the Surveillance of Autism Spectrum Disorder
|
The Centers for Disease Control and Prevention (CDC) coordinates a
labor-intensive process to measure the prevalence of autism spectrum disorder
(ASD) among children in the United States. Random forests methods have shown
promise in speeding up this process, but they lag behind human classification
accuracy by about 5%. We explore whether more recently available document
classification algorithms can close this gap. We applied 8 supervised learning
algorithms to predict whether children meet the case definition for ASD based
solely on the words in their evaluations. We compared the algorithms'
performance across 10 random train-test splits of the data, using
classification accuracy, F1 score, and number of positive calls to evaluate
their potential use for surveillance. Across the 10 train-test cycles, the
random forest and support vector machine with Naive Bayes features (NB-SVM)
each achieved slightly more than 87% mean accuracy. The NB-SVM produced
significantly more false negatives than false positives (P = 0.027), but the
random forest did not, making its prevalence estimates very close to the true
prevalence in the data. The best-performing neural network performed similarly
to the random forest on both measures. The random forest performed as well as
more recently available models like the NB-SVM and the neural network, and it
also produced good prevalence estimates. NB-SVM may not be a good candidate for
use in a fully-automated surveillance workflow due to increased false
negatives. More sophisticated algorithms, like hierarchical convolutional
neural networks, may not be feasible to train due to characteristics of the
data. Current algorithms might perform better if the data are abstracted and
processed differently and if they take into account information about the
children in addition to their evaluations.
|
NB-SVM may not be a good candidate for use in a fully-automated surveillance workflow due to increased false negatives.
|
http://arxiv.org/abs/1804.06223v3
|
http://arxiv.org/pdf/1804.06223v3.pdf
| null |
[
"Scott H Lee",
"Matthew J Maenner",
"Charles M Heilig"
] |
[
"BIG-bench Machine Learning",
"Document Classification",
"General Classification"
] | 2018-04-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ifair-learning-individually-fair-data
|
1806.01059
| null | null |
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
|
People are rated and ranked, towards algorithmic decision making in an
increasing number of applications, typically based on machine learning.
Research on how to incorporate fairness into such tasks has prevalently pursued
the paradigm of group fairness: giving adequate success rates to specifically
protected groups. In contrast, the alternative paradigm of individual fairness
has received relatively little attention, and this paper advances this less
explored direction. The paper introduces a method for probabilistically mapping
user records into a low-rank representation that reconciles individual fairness
and the utility of classifiers and rankings in downstream applications. Our
notion of individual fairness requires that users who are similar in all
task-relevant attributes such as job qualification, and disregarding all
potentially discriminating attributes such as gender, should have similar
outcomes. We demonstrate the versatility of our method by applying it to
classification and learning-to-rank tasks on a variety of real-world datasets.
Our experiments show substantial improvements over the best prior work for this
setting.
| null |
http://arxiv.org/abs/1806.01059v2
|
http://arxiv.org/pdf/1806.01059v2.pdf
| null |
[
"Preethi Lahoti",
"Krishna P. Gummadi",
"Gerhard Weikum"
] |
[
"Decision Making",
"Fairness",
"Learning-To-Rank"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/rednet-residual-encoder-decoder-network-for
|
1806.01054
| null | null |
RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation
|
Indoor semantic segmentation has always been a difficult task in computer
vision. In this paper, we propose an RGB-D residual encoder-decoder
architecture, named RedNet, for indoor RGB-D semantic segmentation. In RedNet,
the residual module is applied to both the encoder and decoder as the basic
building block, and the skip-connection is used to bypass the spatial feature
between the encoder and decoder. In order to incorporate the depth information
of the scene, a fusion structure is constructed, which makes inference on RGB
image and depth image separately, and fuses their features over several layers.
In order to efficiently optimize the network's parameters, we propose a
`pyramid supervision' training scheme, which applies supervised learning over
different layers in the decoder, to cope with the problem of gradients
vanishing. Experiment results show that the proposed RedNet(ResNet-50) achieves
a state-of-the-art mIoU accuracy of 47.8% on the SUN RGB-D benchmark dataset.
|
Indoor semantic segmentation has always been a difficult task in computer vision.
|
http://arxiv.org/abs/1806.01054v2
|
http://arxiv.org/pdf/1806.01054v2.pdf
| null |
[
"Jindong Jiang",
"Lunan Zheng",
"Fei Luo",
"Zhijun Zhang"
] |
[
"Decoder",
"Segmentation",
"Semantic Segmentation"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
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