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https://paperswithcode.com/paper/high-speed-kernelized-correlation-filters
|
1806.06406
| null | null |
Fast Kernelized Correlation Filters without Boundary Effect
|
In recent years, correlation filter based trackers (CF trackers) have attracted much attention from the vision community because of their top performance in both localization accuracy and efficiency. The society of visual tracking, however, still needs to deal with the following difficulty on CF trackers: avoiding or eliminating the boundary effect completely, in the meantime, exploiting non-linear kernels and running efficiently. In this paper, we propose a fast kernelized correlation filter without boundary effect (nBEKCF) to solve this problem. To avoid the boundary effect thoroughly, a set of \emph{real} and \emph{dense} patches is sampled through the traditional sliding window and used as the training samples to train nBEKCF to fit a Gaussian response map. Non-linear kernels can be applied naturally in nBEKCF due to its different theoretical foundation from the existing CF trackers'. To achieve the fast training and detection, a set of cyclic bases is introduced to construct the filter. Two algorithms, ACSII and CCIM, are developed to significantly accelerate the calculation of kernel correlation matrices. ACSII and CCIM fully exploit the density of training samples and cyclic structure of bases, and totally run in space domain. The efficiency of CCIM exceeds that of the FFT counterpart remarkably in our task. Extensive experiments on six public datasets, OTB-2013, OTB-2015, NfS, VOT2018, GOT10k, and TrackingNet, show that compared to the CF trackers designed to relax the boundary effect, BACF and SRDCF, our nBEKCF achieves higher localization accuracy without tricks, in the meanwhile, runs at higher FPS.
| null |
https://arxiv.org/abs/1806.06406v5
|
https://arxiv.org/pdf/1806.06406v5.pdf
| null |
[
"Ming Tang",
"Linyu Zheng",
"Bin Yu",
"Jinqiao Wang"
] |
[
"Visual Tracking"
] | 2018-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/medgan-medical-image-translation-using-gans
|
1806.06397
| null | null |
MedGAN: Medical Image Translation using GANs
|
Image-to-image translation is considered a new frontier in the field of
medical image analysis, with numerous potential applications. However, a large
portion of recent approaches offers individualized solutions based on
specialized task-specific architectures or require refinement through
non-end-to-end training. In this paper, we propose a new framework, named
MedGAN, for medical image-to-image translation which operates on the image
level in an end-to-end manner. MedGAN builds upon recent advances in the field
of generative adversarial networks (GANs) by merging the adversarial framework
with a new combination of non-adversarial losses. We utilize a discriminator
network as a trainable feature extractor which penalizes the discrepancy
between the translated medical images and the desired modalities. Moreover,
style-transfer losses are utilized to match the textures and fine-structures of
the desired target images to the translated images. Additionally, we present a
new generator architecture, titled CasNet, which enhances the sharpness of the
translated medical outputs through progressive refinement via encoder-decoder
pairs. Without any application-specific modifications, we apply MedGAN on three
different tasks: PET-CT translation, correction of MR motion artefacts and PET
image denoising. Perceptual analysis by radiologists and quantitative
evaluations illustrate that the MedGAN outperforms other existing translation
approaches.
|
Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications.
|
http://arxiv.org/abs/1806.06397v2
|
http://arxiv.org/pdf/1806.06397v2.pdf
| null |
[
"Karim Armanious",
"Chenming Jiang",
"Marc Fischer",
"Thomas Küstner",
"Konstantin Nikolaou",
"Sergios Gatidis",
"Bin Yang"
] |
[
"Decoder",
"Denoising",
"Image Denoising",
"Image-to-Image Translation",
"Medical Image Analysis",
"Style Transfer",
"Translation"
] | 2018-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/task-relevant-object-discovery-and
|
1806.06392
| null | null |
Task-Relevant Object Discovery and Categorization for Playing First-person Shooter Games
|
We consider the problem of learning to play first-person shooter (FPS) video
games using raw screen images as observations and keyboard inputs as actions.
The high-dimensionality of the observations in this type of applications leads
to prohibitive needs of training data for model-free methods, such as the deep
Q-network (DQN), and its recurrent variant DRQN. Thus, recent works focused on
learning low-dimensional representations that may reduce the need for data.
This paper presents a new and efficient method for learning such
representations. Salient segments of consecutive frames are detected from their
optical flow, and clustered based on their feature descriptors. The clusters
typically correspond to different discovered categories of objects. Segments
detected in new frames are then classified based on their nearest clusters.
Because only a few categories are relevant to a given task, the importance of a
category is defined as the correlation between its occurrence and the agent's
performance. The result is encoded as a vector indicating objects that are in
the frame and their locations, and used as a side input to DRQN. Experiments on
the game Doom provide a good evidence for the benefit of this approach.
| null |
http://arxiv.org/abs/1806.06392v1
|
http://arxiv.org/pdf/1806.06392v1.pdf
| null |
[
"Junchi Liang",
"Abdeslam Boularias"
] |
[
"Object Discovery",
"Optical Flow Estimation"
] | 2018-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/scraping-and-preprocessing-commercial-auction
|
1806.00656
| null | null |
Scraping and Preprocessing Commercial Auction Data for Fraud Classification
|
In the last three decades, we have seen a significant increase in trading
goods and services through online auctions. However, this business created an
attractive environment for malicious moneymakers who can commit different types
of fraud activities, such as Shill Bidding (SB). The latter is predominant
across many auctions but this type of fraud is difficult to detect due to its
similarity to normal bidding behaviour. The unavailability of SB datasets makes
the development of SB detection and classification models burdensome.
Furthermore, to implement efficient SB detection models, we should produce SB
data from actual auctions of commercial sites. In this study, we first scraped
a large number of eBay auctions of a popular product. After preprocessing the
raw auction data, we build a high-quality SB dataset based on the most reliable
SB strategies. The aim of our research is to share the preprocessed auction
dataset as well as the SB training (unlabelled) dataset, thereby researchers
can apply various machine learning techniques by using authentic data of
auctions and fraud.
| null |
http://arxiv.org/abs/1806.00656v2
|
http://arxiv.org/pdf/1806.00656v2.pdf
| null |
[
"Ahmad Alzahrani",
"Samira Sadaoui"
] |
[
"Classification",
"General Classification"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multi-variable-lstm-neural-network-for
|
1806.06384
| null | null |
Multi-variable LSTM neural network for autoregressive exogenous model
|
In this paper, we propose multi-variable LSTM capable of accurate forecasting
and variable importance interpretation for time series with exogenous
variables. Current attention mechanism in recurrent neural networks mostly
focuses on the temporal aspect of data and falls short of characterizing
variable importance. To this end, the multi-variable LSTM equipped with
tensorized hidden states is developed to learn hidden states for individual
variables, which give rise to our mixture temporal and variable attention.
Based on such attention mechanism, we infer and quantify variable importance.
Extensive experiments using real datasets with Granger-causality test and the
synthetic dataset with ground truth demonstrate the prediction performance and
interpretability of multi-variable LSTM in comparison to a variety of
baselines. It exhibits the prospect of multi-variable LSTM as an end-to-end
framework for both forecasting and knowledge discovery.
| null |
http://arxiv.org/abs/1806.06384v1
|
http://arxiv.org/pdf/1806.06384v1.pdf
| null |
[
"Tian Guo",
"Tao Lin"
] |
[
"Time Series",
"Time Series Analysis"
] | 2018-06-17T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/geodesic-convex-optimization-differentiation
|
1806.06373
| null | null |
Geodesic Convex Optimization: Differentiation on Manifolds, Geodesics, and Convexity
|
Convex optimization is a vibrant and successful area due to the existence of
a variety of efficient algorithms that leverage the rich structure provided by
convexity. Convexity of a smooth set or a function in a Euclidean space is
defined by how it interacts with the standard differential structure in this
space -- the Hessian of a convex function has to be positive semi-definite
everywhere. However, in recent years, there is a growing demand to understand
non-convexity and develop computational methods to optimize non-convex
functions. Intriguingly, there is a type of non-convexity that disappears once
one introduces a suitable differentiable structure and redefines convexity with
respect to the straight lines, or {\em geodesics}, with respect to this
structure. Such convexity is referred to as {\em geodesic convexity}. Interest
in studying it arises due to recent reformulations of some non-convex problems
as geodesically convex optimization problems over geodesically convex sets.
Geodesics on manifolds have been extensively studied in various branches of
Mathematics and Physics. However, unlike convex optimization, understanding
geodesics and geodesic convexity from a computational point of view largely
remains a mystery. The goal of this exposition is to introduce the first part
of geodesic convex optimization -- geodesic convexity -- in a self-contained
manner. We first present a variety of notions from differential and Riemannian
geometry such as differentiation on manifolds, geodesics, and then introduce
geodesic convexity. We conclude by showing that certain non-convex optimization
problems such as computing the Brascamp-Lieb constant and the operator scaling
problem have geodesically convex formulations.
| null |
http://arxiv.org/abs/1806.06373v1
|
http://arxiv.org/pdf/1806.06373v1.pdf
| null |
[
"Nisheeth K. Vishnoi"
] |
[] | 2018-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-the-mathematics-of-beauty-beautiful-images
|
1705.08244
| null | null |
On the mathematics of beauty: beautiful images
|
In this paper, we will study the simplest kind of beauty which can be found in simple visual patterns. The proposed approach shows that aesthetically appealing patterns deliver higher amount of information over multiple levels in comparison with less aesthetically appealing patterns when the same amount of energy is used. The proposed approach is used to classify aesthetically appealing patterns.
| null |
https://arxiv.org/abs/1705.08244v8
|
https://arxiv.org/pdf/1705.08244v8.pdf
| null |
[
"A. M. Khalili"
] |
[] | 2017-05-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multimodal-grounding-for-language-processing
|
1806.06371
| null | null |
Multimodal Grounding for Language Processing
|
This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.
|
This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language.
|
https://arxiv.org/abs/1806.06371v2
|
https://arxiv.org/pdf/1806.06371v2.pdf
|
COLING 2018 8
|
[
"Lisa Beinborn",
"Teresa Botschen",
"Iryna Gurevych"
] |
[
"Survey"
] | 2018-06-17T00:00:00 |
https://aclanthology.org/C18-1197
|
https://aclanthology.org/C18-1197.pdf
|
multimodal-grounding-for-language-processing-1
| null |
[] |
https://paperswithcode.com/paper/how-could-polyhedral-theory-harness-deep
|
1806.06365
| null | null |
How Could Polyhedral Theory Harness Deep Learning?
|
The holy grail of deep learning is to come up with an automatic method to
design optimal architectures for different applications. In other words, how
can we effectively dimension and organize neurons along the network layers
based on the computational resources, input size, and amount of training data?
We outline promising research directions based on polyhedral theory and
mixed-integer representability that may offer an analytical approach to this
question, in contrast to the empirical techniques often employed.
| null |
http://arxiv.org/abs/1806.06365v1
|
http://arxiv.org/pdf/1806.06365v1.pdf
| null |
[
"Thiago Serra",
"Christian Tjandraatmadja",
"Srikumar Ramalingam"
] |
[
"Deep Learning"
] | 2018-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/exact-information-propagation-through-fully
|
1806.06362
| null | null |
Initialization of ReLUs for Dynamical Isometry
|
Deep learning relies on good initialization schemes and hyperparameter choices prior to training a neural network. Random weight initializations induce random network ensembles, which give rise to the trainability, training speed, and sometimes also generalization ability of an instance. In addition, such ensembles provide theoretical insights into the space of candidate models of which one is selected during training. The results obtained so far rely on mean field approximations that assume infinite layer width and that study average squared signals. We derive the joint signal output distribution exactly, without mean field assumptions, for fully-connected networks with Gaussian weights and biases, and analyze deviations from the mean field results. For rectified linear units, we further discuss limitations of the standard initialization scheme, such as its lack of dynamical isometry, and propose a simple alternative that overcomes these by initial parameter sharing.
|
Deep learning relies on good initialization schemes and hyperparameter choices prior to training a neural network.
|
https://arxiv.org/abs/1806.06362v3
|
https://arxiv.org/pdf/1806.06362v3.pdf
|
NeurIPS 2019 12
|
[
"Rebekka Burkholz",
"Alina Dubatovka"
] |
[] | 2018-06-17T00:00:00 |
http://papers.nips.cc/paper/8509-initialization-of-relus-for-dynamical-isometry
|
http://papers.nips.cc/paper/8509-initialization-of-relus-for-dynamical-isometry.pdf
|
initialization-of-relus-for-dynamical
| null |
[] |
https://paperswithcode.com/paper/incorporating-chinese-characters-of-words-for
|
1806.06349
| null | null |
Incorporating Chinese Characters of Words for Lexical Sememe Prediction
|
Sememes are minimum semantic units of concepts in human languages, such that
each word sense is composed of one or multiple sememes. Words are usually
manually annotated with their sememes by linguists, and form linguistic
common-sense knowledge bases widely used in various NLP tasks. Recently, the
lexical sememe prediction task has been introduced. It consists of
automatically recommending sememes for words, which is expected to improve
annotation efficiency and consistency. However, existing methods of lexical
sememe prediction typically rely on the external context of words to represent
the meaning, which usually fails to deal with low-frequency and
out-of-vocabulary words. To address this issue for Chinese, we propose a novel
framework to take advantage of both internal character information and external
context information of words. We experiment on HowNet, a Chinese sememe
knowledge base, and demonstrate that our framework outperforms state-of-the-art
baselines by a large margin, and maintains a robust performance even for
low-frequency words.
|
However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-of-vocabulary words.
|
http://arxiv.org/abs/1806.06349v1
|
http://arxiv.org/pdf/1806.06349v1.pdf
|
ACL 2018 7
|
[
"Huiming Jin",
"Hao Zhu",
"Zhiyuan Liu",
"Ruobing Xie",
"Maosong Sun",
"Fen Lin",
"Leyu Lin"
] |
[
"Common Sense Reasoning",
"Prediction"
] | 2018-06-17T00:00:00 |
https://aclanthology.org/P18-1227
|
https://aclanthology.org/P18-1227.pdf
|
incorporating-chinese-characters-of-words-for-1
| null |
[] |
https://paperswithcode.com/paper/neural-style-transfer-a-review
|
1705.04058
| null | null |
Neural Style Transfer: A Review
|
The seminal work of Gatys et al. demonstrated the power of Convolutional
Neural Networks (CNNs) in creating artistic imagery by separating and
recombining image content and style. This process of using CNNs to render a
content image in different styles is referred to as Neural Style Transfer
(NST). Since then, NST has become a trending topic both in academic literature
and industrial applications. It is receiving increasing attention and a variety
of approaches are proposed to either improve or extend the original NST
algorithm. In this paper, we aim to provide a comprehensive overview of the
current progress towards NST. We first propose a taxonomy of current algorithms
in the field of NST. Then, we present several evaluation methods and compare
different NST algorithms both qualitatively and quantitatively. The review
concludes with a discussion of various applications of NST and open problems
for future research. A list of papers discussed in this review, corresponding
codes, pre-trained models and more comparison results are publicly available at
https://github.com/ycjing/Neural-Style-Transfer-Papers.
|
We first propose a taxonomy of current algorithms in the field of NST.
|
http://arxiv.org/abs/1705.04058v7
|
http://arxiv.org/pdf/1705.04058v7.pdf
| null |
[
"Yongcheng Jing",
"Yezhou Yang",
"Zunlei Feng",
"Jingwen Ye",
"Yizhou Yu",
"Mingli Song"
] |
[
"Style Transfer"
] | 2017-05-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/investigating-generative-adversarial-networks
|
1803.10132
| null | null |
Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition
|
We investigate the use of generative adversarial networks (GANs) in speech
dereverberation for robust speech recognition. GANs have been recently studied
for speech enhancement to remove additive noises, but there still lacks of a
work to examine their ability in speech dereverberation and the advantages of
using GANs have not been fully established. In this paper, we provide deep
investigations in the use of GAN-based dereverberation front-end in ASR. First,
we study the effectiveness of different dereverberation networks (the generator
in GAN) and find that LSTM leads a significant improvement as compared with
feed-forward DNN and CNN in our dataset. Second, further adding residual
connections in the deep LSTMs can boost the performance as well. Finally, we
find that, for the success of GAN, it is important to update the generator and
the discriminator using the same mini-batch data during training. Moreover,
using reverberant spectrogram as a condition to discriminator, as suggested in
previous studies, may degrade the performance. In summary, our GAN-based
dereverberation front-end achieves 14%-19% relative CER reduction as compared
to the baseline DNN dereverberation network when tested on a strong
multi-condition training acoustic model.
|
First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads a significant improvement as compared with feed-forward DNN and CNN in our dataset.
|
http://arxiv.org/abs/1803.10132v3
|
http://arxiv.org/pdf/1803.10132v3.pdf
| null |
[
"Ke Wang",
"Junbo Zhang",
"Sining Sun",
"Yujun Wang",
"Fei Xiang",
"Lei Xie"
] |
[
"Robust Speech Recognition",
"Speech Dereverberation",
"Speech Enhancement",
"speech-recognition",
"Speech Recognition"
] | 2018-03-27T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "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"
},
{
"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/empirical-evaluation-of-speaker-adaptation-on
|
1803.10146
| null | null |
Empirical Evaluation of Speaker Adaptation on DNN based Acoustic Model
|
Speaker adaptation aims to estimate a speaker specific acoustic model from a
speaker independent one to minimize the mismatch between the training and
testing conditions arisen from speaker variabilities. A variety of neural
network adaptation methods have been proposed since deep learning models have
become the main stream. But there still lacks an experimental comparison
between different methods, especially when DNN-based acoustic models have been
advanced greatly. In this paper, we aim to close this gap by providing an
empirical evaluation of three typical speaker adaptation methods: LIN, LHUC and
KLD. Adaptation experiments, with different size of adaptation data, are
conducted on a strong TDNN-LSTM acoustic model. More challengingly, here, the
source and target we are concerned with are standard Mandarin speaker model and
accented Mandarin speaker model. We compare the performances of different
methods and their combinations. Speaker adaptation performance is also examined
by speaker's accent degree.
|
Speaker adaptation aims to estimate a speaker specific acoustic model from a speaker independent one to minimize the mismatch between the training and testing conditions arisen from speaker variabilities.
|
http://arxiv.org/abs/1803.10146v3
|
http://arxiv.org/pdf/1803.10146v3.pdf
| null |
[
"Ke Wang",
"Junbo Zhang",
"Yujun Wang",
"Lei Xie"
] |
[] | 2018-03-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-acoustic-word-embeddings-with-1
|
1806.03621
| null | null |
Learning Acoustic Word Embeddings with Temporal Context for Query-by-Example Speech Search
|
We propose to learn acoustic word embeddings with temporal context for
query-by-example (QbE) speech search. The temporal context includes the leading
and trailing word sequences of a word. We assume that there exist spoken word
pairs in the training database. We pad the word pairs with their original
temporal context to form fixed-length speech segment pairs. We obtain the
acoustic word embeddings through a deep convolutional neural network (CNN)
which is trained on the speech segment pairs with a triplet loss. Shifting a
fixed-length analysis window through the search content, we obtain a running
sequence of embeddings. In this way, searching for the spoken query is
equivalent to the matching of acoustic word embeddings. The experiments show
that our proposed acoustic word embeddings learned with temporal context are
effective in QbE speech search. They outperform the state-of-the-art
frame-level feature representations and reduce run-time computation since no
dynamic time warping is required in QbE speech search. We also find that it is
important to have sufficient speech segment pairs to train the deep CNN for
effective acoustic word embeddings.
| null |
http://arxiv.org/abs/1806.03621v2
|
http://arxiv.org/pdf/1806.03621v2.pdf
| null |
[
"Yougen Yuan",
"Cheung-Chi Leung",
"Lei Xie",
"Hongjie Chen",
"Bin Ma",
"Haizhou Li"
] |
[
"Dynamic Time Warping",
"Triplet",
"Word Embeddings"
] | 2018-06-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/neural-feature-learning-from-relational
|
1801.05372
| null | null |
Neural Feature Learning From Relational Database
|
Feature engineering is one of the most important but most tedious tasks in data science. This work studies automation of feature learning from relational database. We first prove theoretically that finding the optimal features from relational data for predictive tasks is NP-hard. We propose an efficient rule-based approach based on heuristics and a deep neural network to automatically learn appropriate features from relational data. We benchmark our approaches in ensembles in past Kaggle competitions. Our new approach wins late medals and beats the state-of-the-art solutions with significant margins. To the best of our knowledge, this is the first time an automated data science system could win medals in Kaggle competitions with complex relational database.
| null |
https://arxiv.org/abs/1801.05372v4
|
https://arxiv.org/pdf/1801.05372v4.pdf
| null |
[
"Hoang Thanh Lam",
"Tran Ngoc Minh",
"Mathieu Sinn",
"Beat Buesser",
"Martin Wistuba"
] |
[
"Feature Engineering"
] | 2018-01-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/extending-recurrent-neural-aligner-for
|
1806.06342
| null | null |
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin
|
End-to-end models have been showing superiority in Automatic Speech
Recognition (ASR). At the same time, the capacity of streaming recognition has
become a growing requirement for end-to-end models. Following these trends, an
encoder-decoder recurrent neural network called Recurrent Neural Aligner (RNA)
has been freshly proposed and shown its competitiveness on two English ASR
tasks. However, it is not clear if RNA can be further improved and applied to
other spoken language. In this work, we explore the applicability of RNA in
Mandarin Chinese and present four effective extensions: In the encoder, we
redesign the temporal down-sampling and introduce a powerful convolutional
structure. In the decoder, we utilize a regularizer to smooth the output
distribution and conduct joint training with a language model. On two Mandarin
Chinese conversational telephone speech recognition (MTS) datasets, our
Extended-RNA obtains promising performance. Particularly, it achieves 27.7%
character error rate (CER), which is superior to current state-of-the-art
result on the popular HKUST task.
| null |
http://arxiv.org/abs/1806.06342v2
|
http://arxiv.org/pdf/1806.06342v2.pdf
| null |
[
"Linhao Dong",
"Shiyu Zhou",
"Wei Chen",
"Bo Xu"
] |
[
"Automatic Speech Recognition",
"Automatic Speech Recognition (ASR)",
"Decoder",
"Language Modeling",
"Language Modelling",
"speech-recognition",
"Speech Recognition"
] | 2018-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/inverse-reinforcement-learning-from-summary
|
1703.09700
| null | null |
Inverse Reinforcement Learning from Summary Data
|
Inverse reinforcement learning (IRL) aims to explain observed strategic
behavior by fitting reinforcement learning models to behavioral data. However,
traditional IRL methods are only applicable when the observations are in the
form of state-action paths. This assumption may not hold in many real-world
modeling settings, where only partial or summarized observations are available.
In general, we may assume that there is a summarizing function $\sigma$, which
acts as a filter between us and the true state-action paths that constitute the
demonstration. Some initial approaches to extending IRL to such situations have
been presented, but with very specific assumptions about the structure of
$\sigma$, such as that only certain state observations are missing. This paper
instead focuses on the most general case of the problem, where no assumptions
are made about the summarizing function, except that it can be evaluated. We
demonstrate that inference is still possible. The paper presents exact and
approximate inference algorithms that allow full posterior inference, which is
particularly important for assessing parameter uncertainty in this challenging
inference situation. Empirical scalability is demonstrated to reasonably sized
problems, and practical applicability is demonstrated by estimating the
posterior for a cognitive science RL model based on an observed user's task
completion time only.
| null |
http://arxiv.org/abs/1703.09700v3
|
http://arxiv.org/pdf/1703.09700v3.pdf
| null |
[
"Antti Kangasrääsiö",
"Samuel Kaski"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2017-03-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/glomo-unsupervisedly-learned-relational
|
1806.05662
| null | null |
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
|
Modern deep transfer learning approaches have mainly focused on learning
generic feature vectors from one task that are transferable to other tasks,
such as word embeddings in language and pretrained convolutional features in
vision. However, these approaches usually transfer unary features and largely
ignore more structured graphical representations. This work explores the
possibility of learning generic latent relational graphs that capture
dependencies between pairs of data units (e.g., words or pixels) from
large-scale unlabeled data and transferring the graphs to downstream tasks. Our
proposed transfer learning framework improves performance on various tasks
including question answering, natural language inference, sentiment analysis,
and image classification. We also show that the learned graphs are generic
enough to be transferred to different embeddings on which the graphs have not
been trained (including GloVe embeddings, ELMo embeddings, and task-specific
RNN hidden unit), or embedding-free units such as image pixels.
|
We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden unit), or embedding-free units such as image pixels.
|
http://arxiv.org/abs/1806.05662v3
|
http://arxiv.org/pdf/1806.05662v3.pdf
| null |
[
"Zhilin Yang",
"Jake Zhao",
"Bhuwan Dhingra",
"Kaiming He",
"William W. Cohen",
"Ruslan Salakhutdinov",
"Yann Lecun"
] |
[
"image-classification",
"Image Classification",
"Natural Language Inference",
"Question Answering",
"Sentiment Analysis",
"Transfer Learning",
"Word Embeddings"
] | 2018-06-14T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**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"
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://www.ebi.ac.uk/intact/search?query=id:P00722*#interactor",
"description": "A **Bidirectional LSTM**, or **biLSTM**, is a sequence processing model that consists of two LSTMs: one taking the input in a forward direction, and the other in a backwards direction. BiLSTMs effectively increase the amount of information available to the network, improving the context available to the algorithm (e.g. knowing what words immediately follow *and* precede a word in a sentence).\r\n\r\nImage Source: Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks, Cornegruta et al",
"full_name": "Bidirectional LSTM",
"introduced_year": 2001,
"main_collection": {
"area": "General",
"description": "Consists of tabular data learning approaches that use deep learning architectures for learning on tabular data. According to the taxonomy in [V.Borisov et al. (2021)](https://paperswithcode.com/paper/deep-neural-networks-and-tabular-data-a), deep learning approaches for tabular data can be categorized into:\r\n\r\n- **Regularization models**\r\n- **Transformer-based models**: [TabNet](/method/tabnet), [TabTransformer](/method/tabtransformer), [SAINT](/method/saint), [ARM-Net](/method/arm-net),...\r\n- **Hybrid models** (fully differentiable and partly differentiable): [Wide&Deep](/method/wide-deep), [TabNN](/method/tabnn), [NON](/method/non), [Boost-GNN](/method/boost-gnn), [NODE](/method/node),...\r\n- **Data encoding methods** (single-dimensional encoding and multi-dimensional encoding): [VIME](/method/vime), [SCARF](/method/scarf),...",
"name": "Deep Tabular Learning",
"parent": null
},
"name": "BiLSTM",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Embeddings from Language Models**, or **ELMo**, is a type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus.\r\n\r\nA biLM combines both a forward and backward LM. ELMo jointly maximizes the log likelihood of the forward and backward directions. To add ELMo to a supervised model, we freeze the weights of the biLM and then concatenate the ELMo vector $\\textbf{ELMO}^{task}_k$ with $\\textbf{x}_k$ and pass the ELMO enhanced representation $[\\textbf{x}_k; \\textbf{ELMO}^{task}_k]$ into the task RNN. Here $\\textbf{x}_k$ is a context-independent token representation for each token position. \r\n\r\nImage Source: [here](https://medium.com/@duyanhnguyen_38925/create-a-strong-text-classification-with-the-help-from-elmo-e90809ba29da)",
"full_name": "ELMo",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Word Embeddings",
"parent": null
},
"name": "ELMo",
"source_title": "Deep contextualized word representations",
"source_url": "http://arxiv.org/abs/1802.05365v2"
}
] |
https://paperswithcode.com/paper/compnet-complementary-segmentation-network
|
1804.00521
| null | null |
CompNet: Complementary Segmentation Network for Brain MRI Extraction
|
Brain extraction is a fundamental step for most brain imaging studies. In
this paper, we investigate the problem of skull stripping and propose
complementary segmentation networks (CompNets) to accurately extract the brain
from T1-weighted MRI scans, for both normal and pathological brain images. The
proposed networks are designed in the framework of encoder-decoder networks and
have two pathways to learn features from both the brain tissue and its
complementary part located outside of the brain. The complementary pathway
extracts the features in the non-brain region and leads to a robust solution to
brain extraction from MRIs with pathologies, which do not exist in our training
dataset. We demonstrate the effectiveness of our networks by evaluating them on
the OASIS dataset, resulting in the state of the art performance under the
two-fold cross-validation setting. Moreover, the robustness of our networks is
verified by testing on images with introduced pathologies and by showing its
invariance to unseen brain pathologies. In addition, our complementary network
design is general and can be extended to address other image segmentation
problems with better generalization.
|
Brain extraction is a fundamental step for most brain imaging studies.
|
http://arxiv.org/abs/1804.00521v2
|
http://arxiv.org/pdf/1804.00521v2.pdf
| null |
[
"Raunak Dey",
"Yi Hong"
] |
[
"Decoder",
"Image Segmentation",
"Segmentation",
"Semantic Segmentation",
"Skull Stripping"
] | 2018-03-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/training-augmentation-with-adversarial
|
1806.02782
| null | null |
Training Augmentation with Adversarial Examples for Robust Speech Recognition
|
This paper explores the use of adversarial examples in training speech
recognition systems to increase robustness of deep neural network acoustic
models. During training, the fast gradient sign method is used to generate
adversarial examples augmenting the original training data. Different from
conventional data augmentation based on data transformations, the examples are
dynamically generated based on current acoustic model parameters. We assess the
impact of adversarial data augmentation in experiments on the Aurora-4 and
CHiME-4 single-channel tasks, showing improved robustness against noise and
channel variation. Further improvement is obtained when combining adversarial
examples with teacher/student training, leading to a 23% relative word error
rate reduction on Aurora-4.
| null |
http://arxiv.org/abs/1806.02782v2
|
http://arxiv.org/pdf/1806.02782v2.pdf
| null |
[
"Sining Sun",
"Ching-Feng Yeh",
"Mari Ostendorf",
"Mei-Yuh Hwang",
"Lei Xie"
] |
[
"Data Augmentation",
"Robust Speech Recognition",
"speech-recognition",
"Speech Recognition"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/approximate-submodular-functions-and
|
1806.06323
| null | null |
Approximate Submodular Functions and Performance Guarantees
|
We consider the problem of maximizing non-negative non-decreasing set
functions. Although most of the recent work focus on exploiting submodularity,
it turns out that several objectives we encounter in practice are not
submodular. Nonetheless, often we leverage the greedy algorithms used in
submodular functions to determine a solution to the non-submodular functions.
Hereafter, we propose to address the original problem by \emph{approximating}
the non-submodular function and analyze the incurred error, as well as the
performance trade-offs. To quantify the approximation error, we introduce a
novel concept of $\delta$-approximation of a function, which we used to define
the space of submodular functions that lie within an approximation error. We
provide necessary conditions on the existence of such $\delta$-approximation
functions, which might not be unique. Consequently, we characterize this
subspace which we refer to as \emph{region of submodularity}. Furthermore,
submodular functions are known to lead to different sub-optimality guarantees,
so we generalize those dependencies upon a $\delta$-approximation into the
notion of \emph{greedy curvature}. Finally, we used this latter notion to
simplify some of the existing results and efficiently (i.e., linear complexity)
determine tightened bounds on the sub-optimality guarantees using objective
functions commonly used in practical setups and validate them using real data.
| null |
http://arxiv.org/abs/1806.06323v1
|
http://arxiv.org/pdf/1806.06323v1.pdf
| null |
[
"Gaurav Gupta",
"Sergio Pequito",
"Paul Bogdan"
] |
[] | 2018-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/comparative-survey-of-visual-object
|
1806.06321
| null | null |
Comparative survey of visual object classifiers
|
Classification of Visual Object Classes represents one of the most elaborated
areas of interest in Computer Vision. It is always challenging to get one
specific detector, descriptor or classifier that provides the expected object
classification result. Consequently, it critical to compare the different
detection, descriptor and classifier methods available and chose a single or
combination of two or three to get an optimal result. In this paper, we have
presented a comparative survey of different feature descriptors and
classifiers. From feature descriptors, SIFT (Sparse & Dense) and HeuSIFT
combination colour descriptors; From classification techniques, Support Vector
Classifier, K-Nearest Neighbor, ADABOOST, and fisher are covered in comparative
practical implementation survey.
| null |
http://arxiv.org/abs/1806.06321v1
|
http://arxiv.org/pdf/1806.06321v1.pdf
| null |
[
"Hiliwi Leake Kidane"
] |
[
"Classification",
"General Classification",
"Object",
"Survey"
] | 2018-06-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-neural-nets-with-interpolating-function
|
1802.00168
| null | null |
Deep Neural Nets with Interpolating Function as Output Activation
|
We replace the output layer of deep neural nets, typically the softmax
function, by a novel interpolating function. And we propose end-to-end training
and testing algorithms for this new architecture. Compared to classical neural
nets with softmax function as output activation, the surrogate with
interpolating function as output activation combines advantages of both deep
and manifold learning. The new framework demonstrates the following major
advantages: First, it is better applicable to the case with insufficient
training data. Second, it significantly improves the generalization accuracy on
a wide variety of networks. The algorithm is implemented in PyTorch, and code
will be made publicly available.
|
We replace the output layer of deep neural nets, typically the softmax function, by a novel interpolating function.
|
http://arxiv.org/abs/1802.00168v3
|
http://arxiv.org/pdf/1802.00168v3.pdf
|
NeurIPS 2018 12
|
[
"Bao Wang",
"Xiyang Luo",
"Zhen Li",
"Wei Zhu",
"Zuoqiang Shi",
"Stanley J. Osher"
] |
[] | 2018-02-01T00:00:00 |
http://papers.nips.cc/paper/7355-deep-neural-nets-with-interpolating-function-as-output-activation
|
http://papers.nips.cc/paper/7355-deep-neural-nets-with-interpolating-function-as-output-activation.pdf
|
deep-neural-nets-with-interpolating-function-1
| null |
[
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/laplacian-smoothing-gradient-descent
|
1806.06317
| null |
By41BjA9YQ
|
Laplacian Smoothing Gradient Descent
|
We propose a class of very simple modifications of gradient descent and
stochastic gradient descent. We show that when applied to a large variety of
machine learning problems, ranging from logistic regression to deep neural
nets, the proposed surrogates can dramatically reduce the variance, allow to
take a larger step size, and improve the generalization accuracy. The methods
only involve multiplying the usual (stochastic) gradient by the inverse of a
positive definitive matrix (which can be computed efficiently by FFT) with a
low condition number coming from a one-dimensional discrete Laplacian or its
high order generalizations. It also preserves the mean and increases the
smallest component and decreases the largest component. The theory of
Hamilton-Jacobi partial differential equations demonstrates that the implicit
version of the new algorithm is almost the same as doing gradient descent on a
new function which (i) has the same global minima as the original function and
(ii) is ``more convex". Moreover, we show that optimization algorithms with
these surrogates converge uniformly in the discrete Sobolev $H_\sigma^p$ sense
and reduce the optimality gap for convex optimization problems. The code is
available at:
\url{https://github.com/BaoWangMath/LaplacianSmoothing-GradientDescent}
|
We propose a class of very simple modifications of gradient descent and stochastic gradient descent.
|
http://arxiv.org/abs/1806.06317v5
|
http://arxiv.org/pdf/1806.06317v5.pdf
| null |
[
"Stanley Osher",
"Bao Wang",
"Penghang Yin",
"Xiyang Luo",
"Farzin Barekat",
"Minh Pham",
"Alex Lin"
] |
[] | 2018-06-17T00:00:00 |
https://openreview.net/forum?id=By41BjA9YQ
|
https://openreview.net/pdf?id=By41BjA9YQ
| 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
}
] |
https://paperswithcode.com/paper/minimal-i-map-mcmc-for-scalable-structure
|
1803.05554
| null | null |
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
|
Learning a Bayesian network (BN) from data can be useful for decision-making
or discovering causal relationships. However, traditional methods often fail in
modern applications, which exhibit a larger number of observed variables than
data points. The resulting uncertainty about the underlying network as well as
the desire to incorporate prior information recommend a Bayesian approach to
learning the BN, but the highly combinatorial structure of BNs poses a striking
challenge for inference. The current state-of-the-art methods such as order
MCMC are faster than previous methods but prevent the use of many natural
structural priors and still have running time exponential in the maximum
indegree of the true directed acyclic graph (DAG) of the BN. We here propose an
alternative posterior approximation based on the observation that, if we
incorporate empirical conditional independence tests, we can focus on a
high-probability DAG associated with each order of the vertices. We show that
our method allows the desired flexibility in prior specification, removes
timing dependence on the maximum indegree and yields provably good posterior
approximations; in addition, we show that it achieves superior accuracy,
scalability, and sampler mixing on several datasets.
|
Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships.
|
http://arxiv.org/abs/1803.05554v3
|
http://arxiv.org/pdf/1803.05554v3.pdf
|
ICML 2018 7
|
[
"Raj Agrawal",
"Tamara Broderick",
"Caroline Uhler"
] |
[
"Decision Making"
] | 2018-03-15T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2472
|
http://proceedings.mlr.press/v80/agrawal18a/agrawal18a.pdf
|
minimal-i-map-mcmc-for-scalable-structure-1
| null |
[] |
https://paperswithcode.com/paper/personalized-saliency-and-its-prediction
|
1710.03011
| null | null |
Personalized Saliency and its Prediction
|
Nearly all existing visual saliency models by far have focused on predicting
a universal saliency map across all observers. Yet psychology studies suggest
that visual attention of different observers can vary significantly under
specific circumstances, especially a scene is composed of multiple salient
objects. To study such heterogenous visual attention pattern across observers,
we first construct a personalized saliency dataset and explore correlations
between visual attention, personal preferences, and image contents.
Specifically, we propose to decompose a personalized saliency map (referred to
as PSM) into a universal saliency map (referred to as USM) predictable by
existing saliency detection models and a new discrepancy map across users that
characterizes personalized saliency. We then present two solutions towards
predicting such discrepancy maps, i.e., a multi-task convolutional neural
network (CNN) framework and an extended CNN with Person-specific Information
Encoded Filters (CNN-PIEF). Extensive experimental results demonstrate the
effectiveness of our models for PSM prediction as well their generalization
capability for unseen observers.
|
Specifically, we propose to decompose a personalized saliency map (referred to as PSM) into a universal saliency map (referred to as USM) predictable by existing saliency detection models and a new discrepancy map across users that characterizes personalized saliency.
|
http://arxiv.org/abs/1710.03011v2
|
http://arxiv.org/pdf/1710.03011v2.pdf
| null |
[
"Yanyu Xu",
"Shenghua Gao",
"Junru Wu",
"Nianyi Li",
"Jingyi Yu"
] |
[
"Prediction",
"Saliency Detection"
] | 2017-10-09T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/causal-generative-domain-adaptation-networks
|
1804.04333
| null | null |
Causal Generative Domain Adaptation Networks
|
An essential problem in domain adaptation is to understand and make use of
distribution changes across domains. For this purpose, we first propose a
flexible Generative Domain Adaptation Network (G-DAN) with specific latent
variables to capture changes in the generating process of features across
domains. By explicitly modeling the changes, one can even generate data in new
domains using the generating process with new values for the latent variables
in G-DAN. In practice, the process to generate all features together may
involve high-dimensional latent variables, requiring dealing with distributions
in high dimensions and making it difficult to learn domain changes from few
source domains. Interestingly, by further making use of the causal
representation of joint distributions, we then decompose the joint distribution
into separate modules, each of which involves different low-dimensional latent
variables and can be learned separately, leading to a Causal G-DAN (CG-DAN).
This improves both statistical and computational efficiency of the learning
procedure. Finally, by matching the feature distribution in the target domain,
we can recover the target-domain joint distribution and derive the learning
machine for the target domain. We demonstrate the efficacy of both G-DAN and
CG-DAN in domain generation and cross-domain prediction on both synthetic and
real data experiments.
| null |
http://arxiv.org/abs/1804.04333v3
|
http://arxiv.org/pdf/1804.04333v3.pdf
| null |
[
"Mingming Gong",
"Kun Zhang",
"Biwei Huang",
"Clark Glymour",
"DaCheng Tao",
"Kayhan Batmanghelich"
] |
[
"Computational Efficiency",
"Domain Adaptation"
] | 2018-04-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/biased-embeddings-from-wild-data-measuring
|
1806.06301
| null | null |
Biased Embeddings from Wild Data: Measuring, Understanding and Removing
|
Many modern Artificial Intelligence (AI) systems make use of data embeddings,
particularly in the domain of Natural Language Processing (NLP). These
embeddings are learnt from data that has been gathered "from the wild" and have
been found to contain unwanted biases. In this paper we make three
contributions towards measuring, understanding and removing this problem. We
present a rigorous way to measure some of these biases, based on the use of
word lists created for social psychology applications; we observe how gender
bias in occupations reflects actual gender bias in the same occupations in the
real world; and finally we demonstrate how a simple projection can
significantly reduce the effects of embedding bias. All this is part of an
ongoing effort to understand how trust can be built into AI systems.
| null |
http://arxiv.org/abs/1806.06301v1
|
http://arxiv.org/pdf/1806.06301v1.pdf
| null |
[
"Adam Sutton",
"Thomas Lansdall-Welfare",
"Nello Cristianini"
] |
[] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deformable-generator-network-unsupervised
|
1806.06298
| null | null |
Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry
|
We present a deformable generator model to disentangle the appearance and geometric information for both image and video data in a purely unsupervised manner. The appearance generator network models the information related to appearance, including color, illumination, identity or category, while the geometric generator performs geometric warping, such as rotation and stretching, through generating deformation field which is used to warp the generated appearance to obtain the final image or video sequences. Two generators take independent latent vectors as input to disentangle the appearance and geometric information from image or video sequences. For video data, a nonlinear transition model is introduced to both the appearance and geometric generators to capture the dynamics over time. The proposed scheme is general and can be easily integrated into different generative models. An extensive set of qualitative and quantitative experiments shows that the appearance and geometric information can be well disentangled, and the learned geometric generator can be conveniently transferred to other image datasets to facilitate knowledge transfer tasks.
|
We present a deformable generator model to disentangle the appearance and geometric information for both image and video data in a purely unsupervised manner.
|
https://arxiv.org/abs/1806.06298v4
|
https://arxiv.org/pdf/1806.06298v4.pdf
| null |
[
"Xianglei Xing",
"Ruiqi Gao",
"Tian Han",
"Song-Chun Zhu",
"Ying Nian Wu"
] |
[
"Disentanglement",
"Transfer Learning"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/right-for-the-right-reason-training-agnostic
|
1806.06296
| null | null |
Right for the Right Reason: Training Agnostic Networks
|
We consider the problem of a neural network being requested to classify
images (or other inputs) without making implicit use of a "protected concept",
that is a concept that should not play any role in the decision of the network.
Typically these concepts include information such as gender or race, or other
contextual information such as image backgrounds that might be implicitly
reflected in unknown correlations with other variables, making it insufficient
to simply remove them from the input features. In other words, making accurate
predictions is not good enough if those predictions rely on information that
should not be used: predictive performance is not the only important metric for
learning systems. We apply a method developed in the context of domain
adaptation to address this problem of "being right for the right reason", where
we request a classifier to make a decision in a way that is entirely 'agnostic'
to a given protected concept (e.g. gender, race, background etc.), even if this
could be implicitly reflected in other attributes via unknown correlations.
After defining the concept of an 'agnostic model', we demonstrate how the
Domain-Adversarial Neural Network can remove unwanted information from a model
using a gradient reversal layer.
| null |
http://arxiv.org/abs/1806.06296v1
|
http://arxiv.org/pdf/1806.06296v1.pdf
| null |
[
"Sen Jia",
"Thomas Lansdall-Welfare",
"Nello Cristianini"
] |
[
"Domain Adaptation"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deepmimic-example-guided-deep-reinforcement
|
1804.02717
| null | null |
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
|
A longstanding goal in character animation is to combine data-driven
specification of behavior with a system that can execute a similar behavior in
a physical simulation, thus enabling realistic responses to perturbations and
environmental variation. We show that well-known reinforcement learning (RL)
methods can be adapted to learn robust control policies capable of imitating a
broad range of example motion clips, while also learning complex recoveries,
adapting to changes in morphology, and accomplishing user-specified goals. Our
method handles keyframed motions, highly-dynamic actions such as
motion-captured flips and spins, and retargeted motions. By combining a
motion-imitation objective with a task objective, we can train characters that
react intelligently in interactive settings, e.g., by walking in a desired
direction or throwing a ball at a user-specified target. This approach thus
combines the convenience and motion quality of using motion clips to define the
desired style and appearance, with the flexibility and generality afforded by
RL methods and physics-based animation. We further explore a number of methods
for integrating multiple clips into the learning process to develop
multi-skilled agents capable of performing a rich repertoire of diverse skills.
We demonstrate results using multiple characters (human, Atlas robot, bipedal
dinosaur, dragon) and a large variety of skills, including locomotion,
acrobatics, and martial arts.
|
We further explore a number of methods for integrating multiple clips into the learning process to develop multi-skilled agents capable of performing a rich repertoire of diverse skills.
|
http://arxiv.org/abs/1804.02717v3
|
http://arxiv.org/pdf/1804.02717v3.pdf
| null |
[
"Xue Bin Peng",
"Pieter Abbeel",
"Sergey Levine",
"Michiel Van de Panne"
] |
[
"Deep Reinforcement Learning",
"Motion Synthesis",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-04-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/latent-convolutional-models
|
1806.06284
| null |
HJGciiR5Y7
|
Latent Convolutional Models
|
We present a new latent model of natural images that can be learned on
large-scale datasets. The learning process provides a latent embedding for
every image in the training dataset, as well as a deep convolutional network
that maps the latent space to the image space. After training, the new model
provides a strong and universal image prior for a variety of image restoration
tasks such as large-hole inpainting, superresolution, and colorization. To
model high-resolution natural images, our approach uses latent spaces of very
high dimensionality (one to two orders of magnitude higher than previous latent
image models). To tackle this high dimensionality, we use latent spaces with a
special manifold structure (convolutional manifolds) parameterized by a ConvNet
of a certain architecture. In the experiments, we compare the learned latent
models with latent models learned by autoencoders, advanced variants of
generative adversarial networks, and a strong baseline system using simpler
parameterization of the latent space. Our model outperforms the competing
approaches over a range of restoration tasks.
|
The learning process provides a latent embedding for every image in the training dataset, as well as a deep convolutional network that maps the latent space to the image space.
|
http://arxiv.org/abs/1806.06284v2
|
http://arxiv.org/pdf/1806.06284v2.pdf
|
ICLR 2019 5
|
[
"ShahRukh Athar",
"Evgeny Burnaev",
"Victor Lempitsky"
] |
[
"Colorization",
"Image Restoration"
] | 2018-06-16T00:00:00 |
https://openreview.net/forum?id=HJGciiR5Y7
|
https://openreview.net/pdf?id=HJGciiR5Y7
|
latent-convolutional-models-1
| null |
[] |
https://paperswithcode.com/paper/first-and-second-order-methods-for-online
|
1709.00106
| null | null |
First and Second Order Methods for Online Convolutional Dictionary Learning
|
Convolutional sparse representations are a form of sparse representation with
a structured, translation invariant dictionary. Most convolutional dictionary
learning algorithms to date operate in batch mode, requiring simultaneous
access to all training images during the learning process, which results in
very high memory usage and severely limits the training data that can be used.
Very recently, however, a number of authors have considered the design of
online convolutional dictionary learning algorithms that offer far better
scaling of memory and computational cost with training set size than batch
methods. This paper extends our prior work, improving a number of aspects of
our previous algorithm; proposing an entirely new one, with better performance,
and that supports the inclusion of a spatial mask for learning from incomplete
data; and providing a rigorous theoretical analysis of these methods.
| null |
http://arxiv.org/abs/1709.00106v3
|
http://arxiv.org/pdf/1709.00106v3.pdf
| null |
[
"Jialin Liu",
"Cristina Garcia-Cardona",
"Brendt Wohlberg",
"Wotao Yin"
] |
[
"Dictionary Learning",
"Second-order methods",
"Translation"
] | 2017-08-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/stable-prediction-across-unknown-environments
|
1806.06270
| null | null |
Stable Prediction across Unknown Environments
|
In many important machine learning applications, the training distribution
used to learn a probabilistic classifier differs from the testing distribution
on which the classifier will be used to make predictions. Traditional methods
correct the distribution shift by reweighting the training data with the ratio
of the density between test and training data. In many applications training
takes place without prior knowledge of the testing distribution on which the
algorithm will be applied in the future. Recently, methods have been proposed
to address the shift by learning causal structure, but those methods rely on
the diversity of multiple training data to a good performance, and have
complexity limitations in high dimensions. In this paper, we propose a novel
Deep Global Balancing Regression (DGBR) algorithm to jointly optimize a deep
auto-encoder model for feature selection and a global balancing model for
stable prediction across unknown environments. The global balancing model
constructs balancing weights that facilitate estimating of partial effects of
features (holding fixed all other features), a problem that is challenging in
high dimensions, and thus helps to identify stable, causal relationships
between features and outcomes. The deep auto-encoder model is designed to
reduce the dimensionality of the feature space, thus making global balancing
easier. We show, both theoretically and with empirical experiments, that our
algorithm can make stable predictions across unknown environments. Our
experiments on both synthetic and real world datasets demonstrate that our DGBR
algorithm outperforms the state-of-the-art methods for stable prediction across
unknown environments.
| null |
http://arxiv.org/abs/1806.06270v2
|
http://arxiv.org/pdf/1806.06270v2.pdf
| null |
[
"Kun Kuang",
"Ruoxuan Xiong",
"Peng Cui",
"Susan Athey",
"Bo Li"
] |
[
"feature selection",
"Prediction"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/extraction-of-technical-information-from
|
1806.02242
| null | null |
Extraction Of Technical Information From Normative Documents Using Automated Methods Based On Ontologies : Application To The Iso 15531 Mandate Standard - Methodology And First Results
|
Problems faced by international standardization bodies become more and more
crucial as the number and the size of the standards they produce increase.
Sometimes, also, the lack of coordination among the committees in charge of the
development of standards may lead to overlaps, mistakes or incompatibilities in
the documents. The aim of this study is to present a methodology enabling an
automatic extraction of the technical concepts (terms) found in normative
documents, through the use of semantic tools coming from the field of language
processing. The first part of the paper provides a description of the
standardization world, its structure, its way of working and the problems
faced; we then introduce the concepts of semantic annotation, information
extraction and the software tools available in this domain. The next section
explains the concept of ontology and its potential use in the field of
standardization. We propose here a methodology enabling the extraction of
technical information from a given normative corpus, based on a semantic
annotation process done according to reference ontologies. The application to
the ISO 15531 MANDATE corpus provides a first use case of the methodology
described in this paper. The paper ends with the description of the first
experimental results produced by this approach, and with some issues and
perspectives, notably its application to other standards and, or Technical
Committees and the possibility offered to create pre-defined technical
dictionaries of terms.
| null |
http://arxiv.org/abs/1806.02242v2
|
http://arxiv.org/pdf/1806.02242v2.pdf
| null |
[
"A. F. Cutting-Decelle",
"A. Digeon",
"R. I. Young",
"J. L. Barraud",
"P. Lamboley"
] |
[] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-strategyproof-conference-peer-review
|
1806.06266
| null | null |
On Strategyproof Conference Peer Review
|
We consider peer review in a conference setting where there is typically an overlap between the set of reviewers and the set of authors. This overlap can incentivize strategic reviews to influence the final ranking of one's own papers. In this work, we address this problem through the lens of social choice, and present a theoretical framework for strategyproof and efficient peer review. We first present and analyze an algorithm for reviewer-assignment and aggregation that guarantees strategyproofness and a natural efficiency property called unanimity, when the authorship graph satisfies a simple property. Our algorithm is based on the so-called partitioning method, and can be thought as a generalization of this method to conference peer review settings. We then empirically show that the requisite property on the authorship graph is indeed satisfied in the submission data from the ICLR conference, and further demonstrate a simple trick to make the partitioning method more practically appealing for conference peer review. Finally, we complement our positive results with negative theoretical results where we prove that under various ways of strengthening the requirements, it is impossible for any algorithm to be strategyproof and efficient.
|
We then empirically show that the requisite property on the authorship graph is indeed satisfied in the submission data from the ICLR conference, and further demonstrate a simple trick to make the partitioning method more practically appealing for conference peer review.
|
https://arxiv.org/abs/1806.06266v3
|
https://arxiv.org/pdf/1806.06266v3.pdf
| null |
[
"Yichong Xu",
"Han Zhao",
"Xiaofei Shi",
"Jeremy Zhang",
"Nihar B. Shah"
] |
[] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/evaluation-of-sentence-embeddings-in
|
1806.06259
| null | null |
Evaluation of sentence embeddings in downstream and linguistic probing tasks
|
Despite the fast developmental pace of new sentence embedding methods, it is
still challenging to find comprehensive evaluations of these different
techniques. In the past years, we saw significant improvements in the field of
sentence embeddings and especially towards the development of universal
sentence encoders that could provide inductive transfer to a wide variety of
downstream tasks. In this work, we perform a comprehensive evaluation of recent
methods using a wide variety of downstream and linguistic feature probing
tasks. We show that a simple approach using bag-of-words with a recently
introduced language model for deep context-dependent word embeddings proved to
yield better results in many tasks when compared to sentence encoders trained
on entailment datasets. We also show, however, that we are still far away from
a universal encoder that can perform consistently across several downstream
tasks.
|
Despite the fast developmental pace of new sentence embedding methods, it is still challenging to find comprehensive evaluations of these different techniques.
|
http://arxiv.org/abs/1806.06259v1
|
http://arxiv.org/pdf/1806.06259v1.pdf
| null |
[
"Christian S. Perone",
"Roberto Silveira",
"Thomas S. Paula"
] |
[
"Language Modeling",
"Language Modelling",
"Sentence",
"Sentence Embedding",
"Sentence-Embedding",
"Sentence Embeddings",
"Word Embeddings"
] | 2018-06-16T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
}
] |
https://paperswithcode.com/paper/dynmat-a-network-that-can-learn-after
|
1806.06253
| null | null |
DynMat, a network that can learn after learning
|
To survive in the dynamically-evolving world, we accumulate knowledge and
improve our skills based on experience. In the process, gaining new knowledge
does not disrupt our vigilance to external stimuli. In other words, our
learning process is 'accumulative' and 'online' without interruption. However,
despite the recent success, artificial neural networks (ANNs) must be trained
offline, and they suffer catastrophic interference between old and new
learning, indicating that ANNs' conventional learning algorithms may not be
suitable for building intelligent agents comparable to our brain. In this
study, we propose a novel neural network architecture (DynMat) consisting of
dual learning systems, inspired by the complementary learning system (CLS)
theory suggesting that the brain relies on short- and long-term learning
systems to learn continuously. Our experiments show that 1) DynMat can learn a
new class without catastrophic interference and 2) it does not strictly require
offline training.
| null |
http://arxiv.org/abs/1806.06253v2
|
http://arxiv.org/pdf/1806.06253v2.pdf
| null |
[
"Jung H. Lee"
] |
[] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/neyman-pearson-classification-parametrics-and
|
1802.02557
| null | null |
Neyman-Pearson classification: parametrics and sample size requirement
|
The Neyman-Pearson (NP) paradigm in binary classification seeks classifiers that achieve a minimal type II error while enforcing the prioritized type I error controlled under some user-specified level $\alpha$. This paradigm serves naturally in applications such as severe disease diagnosis and spam detection, where people have clear priorities among the two error types. Recently, Tong, Feng and Li (2018) proposed a nonparametric umbrella algorithm that adapts all scoring-type classification methods (e.g., logistic regression, support vector machines, random forest) to respect the given type I error upper bound $\alpha$ with high probability, without specific distributional assumptions on the features and the responses. Universal the umbrella algorithm is, it demands an explicit minimum sample size requirement on class $0$, which is often the more scarce class, such as in rare disease diagnosis applications. In this work, we employ the parametric linear discriminant analysis (LDA) model and propose a new parametric thresholding algorithm, which does not need the minimum sample size requirements on class $0$ observations and thus is suitable for small sample applications such as rare disease diagnosis. Leveraging both the existing nonparametric and the newly proposed parametric thresholding rules, we propose four LDA-based NP classifiers, for both low- and high-dimensional settings. On the theoretical front, we prove NP oracle inequalities for one proposed classifier, where the rate for excess type II error benefits from the explicit parametric model assumption. Furthermore, as NP classifiers involve a sample splitting step of class $0$ observations, we construct a new adaptive sample splitting scheme that can be applied universally to NP classifiers, and this adaptive strategy reduces the type II error of these classifiers.
| null |
https://arxiv.org/abs/1802.02557v5
|
https://arxiv.org/pdf/1802.02557v5.pdf
| null |
[
"Xin Tong",
"Lucy Xia",
"Jiacheng Wang",
"Yang Feng"
] |
[
"Binary Classification",
"Classification",
"General Classification",
"Spam detection",
"Vocal Bursts Type Prediction"
] | 2018-02-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/real-time-prediction-of-segmentation-quality
|
1806.06244
| null | null |
Real-time Prediction of Segmentation Quality
|
Recent advances in deep learning based image segmentation methods have
enabled real-time performance with human-level accuracy. However, occasionally
even the best method fails due to low image quality, artifacts or unexpected
behaviour of black box algorithms. Being able to predict segmentation quality
in the absence of ground truth is of paramount importance in clinical practice,
but also in large-scale studies to avoid the inclusion of invalid data in
subsequent analysis.
In this work, we propose two approaches of real-time automated quality
control for cardiovascular MR segmentations using deep learning. First, we
train a neural network on 12,880 samples to predict Dice Similarity
Coefficients (DSC) on a per-case basis. We report a mean average error (MAE) of
0.03 on 1,610 test samples and 97% binary classification accuracy for
separating low and high quality segmentations. Secondly, in the scenario where
no manually annotated data is available, we train a network to predict DSC
scores from estimated quality obtained via a reverse testing strategy. We
report an MAE=0.14 and 91% binary classification accuracy for this case.
Predictions are obtained in real-time which, when combined with real-time
segmentation methods, enables instant feedback on whether an acquired scan is
analysable while the patient is still in the scanner. This further enables new
applications of optimising image acquisition towards best possible analysis
results.
| null |
http://arxiv.org/abs/1806.06244v1
|
http://arxiv.org/pdf/1806.06244v1.pdf
| null |
[
"Robert Robinson",
"Ozan Oktay",
"Wenjia Bai",
"Vanya Valindria",
"Mihir Sanghvi",
"Nay Aung",
"José Paiva",
"Filip Zemrak",
"Kenneth Fung",
"Elena Lukaschuk",
"Aaron Lee",
"Valentina Carapella",
"Young Jin Kim",
"Bernhard Kainz",
"Stefan Piechnik",
"Stefan Neubauer",
"Steffen Petersen",
"Chris Page",
"Daniel Rueckert",
"Ben Glocker"
] |
[
"Binary Classification",
"General Classification",
"Image Segmentation",
"Prediction",
"Segmentation",
"Semantic Segmentation"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/retrofitting-distributional-embeddings-to
|
1708.00112
| null | null |
Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations
|
Knowledge graphs are a versatile framework to encode richly structured data
relationships, but it can be challenging to combine these graphs with
unstructured data. Methods for retrofitting pre-trained entity representations
to the structure of a knowledge graph typically assume that entities are
embedded in a connected space and that relations imply similarity. However,
useful knowledge graphs often contain diverse entities and relations (with
potentially disjoint underlying corpora) which do not accord with these
assumptions. To overcome these limitations, we present Functional Retrofitting,
a framework that generalizes current retrofitting methods by explicitly
modeling pairwise relations. Our framework can directly incorporate a variety
of pairwise penalty functions previously developed for knowledge graph
completion. Further, it allows users to encode, learn, and extract information
about relation semantics. We present both linear and neural instantiations of
the framework. Functional Retrofitting significantly outperforms existing
retrofitting methods on complex knowledge graphs and loses no accuracy on
simpler graphs (in which relations do imply similarity). Finally, we
demonstrate the utility of the framework by predicting new drug--disease
treatment pairs in a large, complex health knowledge graph.
|
Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data.
|
http://arxiv.org/abs/1708.00112v3
|
http://arxiv.org/pdf/1708.00112v3.pdf
|
COLING 2018 8
|
[
"Benjamin J. Lengerich",
"Andrew L. Maas",
"Christopher Potts"
] |
[
"Knowledge Graph Completion",
"Knowledge Graphs"
] | 2017-08-01T00:00:00 |
https://aclanthology.org/C18-1205
|
https://aclanthology.org/C18-1205.pdf
|
retrofitting-distributional-embeddings-to-2
| null |
[] |
https://paperswithcode.com/paper/machine-translation-in-indian-languages
|
1708.07950
| null | null |
Machine Translation in Indian Languages: Challenges and Resolution
|
English to Indian language machine translation poses the challenge of
structural and morphological divergence. This paper describes English to Indian
language statistical machine translation using pre-ordering and suffix
separation. The pre-ordering uses rules to transfer the structure of the source
sentences prior to training and translation. This syntactic restructuring helps
statistical machine translation to tackle the structural divergence and hence
better translation quality. The suffix separation is used to tackle the
morphological divergence between English and highly agglutinative Indian
languages. We demonstrate that the use of pre-ordering and suffix separation
helps in improving the quality of English to Indian Language machine
translation.
| null |
http://arxiv.org/abs/1708.07950v3
|
http://arxiv.org/pdf/1708.07950v3.pdf
| null |
[
"Raj Nath Patel",
"Prakash B. Pimpale",
"M Sasikumar"
] |
[
"Machine Translation",
"Translation"
] | 2017-08-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/peerreview4all-fair-and-accurate-reviewer
|
1806.06237
| null | null |
PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review
|
We consider the problem of automated assignment of papers to reviewers in conference peer review, with a focus on fairness and statistical accuracy. Our fairness objective is to maximize the review quality of the most disadvantaged paper, in contrast to the commonly used objective of maximizing the total quality over all papers. We design an assignment algorithm based on an incremental max-flow procedure that we prove is near-optimally fair. Our statistical accuracy objective is to ensure correct recovery of the papers that should be accepted. We provide a sharp minimax analysis of the accuracy of the peer-review process for a popular objective-score model as well as for a novel subjective-score model that we propose in the paper. Our analysis proves that our proposed assignment algorithm also leads to a near-optimal statistical accuracy. Finally, we design a novel experiment that allows for an objective comparison of various assignment algorithms, and overcomes the inherent difficulty posed by the absence of a ground truth in experiments on peer-review. The results of this experiment as well as of other experiments on synthetic and real data corroborate the theoretical guarantees of our algorithm.
|
Our fairness objective is to maximize the review quality of the most disadvantaged paper, in contrast to the commonly used objective of maximizing the total quality over all papers.
|
https://arxiv.org/abs/1806.06237v2
|
https://arxiv.org/pdf/1806.06237v2.pdf
| null |
[
"Ivan Stelmakh",
"Nihar B. Shah",
"Aarti Singh"
] |
[
"Fairness"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/binary-classification-in-unstructured-space
|
1806.06232
| null | null |
Binary Classification in Unstructured Space With Hypergraph Case-Based Reasoning
|
Binary classification is one of the most common problem in machine learning.
It consists in predicting whether a given element belongs to a particular
class. In this paper, a new algorithm for binary classification is proposed
using a hypergraph representation. The method is agnostic to data
representation, can work with multiple data sources or in non-metric spaces,
and accommodates with missing values. As a result, it drastically reduces the
need for data preprocessing or feature engineering. Each element to be
classified is partitioned according to its interactions with the training set.
For each class, a seminorm over the training set partition is learnt to
represent the distribution of evidence supporting this class.
Empirical validation demonstrates its high potential on a wide range of
well-known datasets and the results are compared to the state-of-the-art. The
time complexity is given and empirically validated. Its robustness with regard
to hyperparameter sensitivity is studied and compared to standard
classification methods. Finally, the limitation of the model space is
discussed, and some potential solutions proposed.
|
For each class, a seminorm over the training set partition is learnt to represent the distribution of evidence supporting this class.
|
http://arxiv.org/abs/1806.06232v3
|
http://arxiv.org/pdf/1806.06232v3.pdf
| null |
[
"Alexandre Quemy"
] |
[
"Binary Classification",
"Classification",
"Feature Engineering",
"General Classification",
"Missing Values"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multimodal-sentiment-analysis-using
|
1806.06228
| null | null |
Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling
|
Multimodal sentiment analysis is a very actively growing field of research. A
promising area of opportunity in this field is to improve the multimodal fusion
mechanism. We present a novel feature fusion strategy that proceeds in a
hierarchical fashion, first fusing the modalities two in two and only then
fusing all three modalities. On multimodal sentiment analysis of individual
utterances, our strategy outperforms conventional concatenation of features by
1%, which amounts to 5% reduction in error rate. On utterance-level multimodal
sentiment analysis of multi-utterance video clips, for which current
state-of-the-art techniques incorporate contextual information from other
utterances of the same clip, our hierarchical fusion gives up to 2.4% (almost
10% error rate reduction) over currently used concatenation. The implementation
of our method is publicly available in the form of open-source code.
|
Multimodal sentiment analysis is a very actively growing field of research.
|
http://arxiv.org/abs/1806.06228v1
|
http://arxiv.org/pdf/1806.06228v1.pdf
| null |
[
"N. Majumder",
"D. Hazarika",
"A. Gelbukh",
"E. Cambria",
"S. Poria"
] |
[
"Multimodal Emotion Recognition",
"Multimodal Sentiment Analysis",
"Sentiment Analysis"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/gile-a-generalized-input-label-embedding-for
|
1806.06219
| null | null |
GILE: A Generalized Input-Label Embedding for Text Classification
|
Neural text classification models typically treat output labels as
categorical variables which lack description and semantics. This forces their
parametrization to be dependent on the label set size, and, hence, they are
unable to scale to large label sets and generalize to unseen ones. Existing
joint input-label text models overcome these issues by exploiting label
descriptions, but they are unable to capture complex label relationships, have
rigid parametrization, and their gains on unseen labels happen often at the
expense of weak performance on the labels seen during training. In this paper,
we propose a new input-label model which generalizes over previous such models,
addresses their limitations and does not compromise performance on seen labels.
The model consists of a joint non-linear input-label embedding with
controllable capacity and a joint-space-dependent classification unit which is
trained with cross-entropy loss to optimize classification performance. We
evaluate models on full-resource and low- or zero-resource text classification
of multilingual news and biomedical text with a large label set. Our model
outperforms monolingual and multilingual models which do not leverage label
semantics and previous joint input-label space models in both scenarios.
|
This forces their parametrization to be dependent on the label set size, and, hence, they are unable to scale to large label sets and generalize to unseen ones.
|
http://arxiv.org/abs/1806.06219v3
|
http://arxiv.org/pdf/1806.06219v3.pdf
|
TACL 2019 3
|
[
"Nikolaos Pappas",
"James Henderson"
] |
[
"Classification",
"General Classification",
"Multi-Task Learning",
"text-classification",
"Text Classification",
"Zero-Shot Learning"
] | 2018-06-16T00:00:00 |
https://aclanthology.org/Q19-1009
|
https://aclanthology.org/Q19-1009.pdf
|
gile-a-generalized-input-label-embedding-for-1
| null |
[] |
https://paperswithcode.com/paper/natasha-faster-non-convex-stochastic
|
1702.00763
| null | null |
Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter
|
Given a nonconvex function that is an average of $n$ smooth functions, we
design stochastic first-order methods to find its approximate stationary
points. The convergence of our new methods depends on the smallest (negative)
eigenvalue $-\sigma$ of the Hessian, a parameter that describes how nonconvex
the function is.
Our methods outperform known results for a range of parameter $\sigma$, and
can be used to find approximate local minima. Our result implies an interesting
dichotomy: there exists a threshold $\sigma_0$ so that the currently fastest
methods for $\sigma>\sigma_0$ and for $\sigma<\sigma_0$ have different
behaviors: the former scales with $n^{2/3}$ and the latter scales with
$n^{3/4}$.
| null |
http://arxiv.org/abs/1702.00763v5
|
http://arxiv.org/pdf/1702.00763v5.pdf
|
ICML 2017 8
|
[
"Zeyuan Allen-Zhu"
] |
[
"Stochastic Optimization"
] | 2017-02-02T00:00:00 |
https://icml.cc/Conferences/2017/Schedule?showEvent=594
|
http://proceedings.mlr.press/v70/allen-zhu17a/allen-zhu17a.pdf
|
natasha-faster-non-convex-stochastic-1
| null |
[] |
https://paperswithcode.com/paper/the-reduced-pc-algorithm-improved-causal
|
1806.06209
| null | null |
The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks
|
We consider the task of estimating a high-dimensional directed acyclic graph, given observations from a linear structural equation model with arbitrary noise distribution. By exploiting properties of common random graphs, we develop a new algorithm that requires conditioning only on small sets of variables. The proposed algorithm, which is essentially a modified version of the PC-Algorithm, offers significant gains in both computational complexity and estimation accuracy. In particular, it results in more efficient and accurate estimation in large networks containing hub nodes, which are common in biological systems. We prove the consistency of the proposed algorithm, and show that it also requires a less stringent faithfulness assumption than the PC-Algorithm. Simulations in low and high-dimensional settings are used to illustrate these findings. An application to gene expression data suggests that the proposed algorithm can identify a greater number of clinically relevant genes than current methods.
| null |
https://arxiv.org/abs/1806.06209v2
|
https://arxiv.org/pdf/1806.06209v2.pdf
| null |
[
"Arjun Sondhi",
"Ali Shojaie"
] |
[] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/offline-extraction-of-indic-regional-language
|
1806.06208
| null | null |
Offline Extraction of Indic Regional Language from Natural Scene Image using Text Segmentation and Deep Convolutional Sequence
|
Regional language extraction from a natural scene image is always a
challenging proposition due to its dependence on the text information extracted
from Image. Text Extraction on the other hand varies on different lighting
condition, arbitrary orientation, inadequate text information, heavy background
influence over text and change of text appearance. This paper presents a novel
unified method for tackling the above challenges. The proposed work uses an
image correction and segmentation technique on the existing Text Detection
Pipeline an Efficient and Accurate Scene Text Detector (EAST). EAST uses
standard PVAnet architecture to select features and non maximal suppression to
detect text from image. Text recognition is done using combined architecture of
MaxOut convolution neural network (CNN) and Bidirectional long short term
memory (LSTM) network. After recognizing text using the Deep Learning based
approach, the native Languages are translated to English and tokenized using
standard Text Tokenizers. The tokens that very likely represent a location is
used to find the Global Positioning System (GPS) coordinates of the location
and subsequently the regional languages spoken in that location is extracted.
The proposed method is tested on a self generated dataset collected from
Government of India dataset and experimented on Standard Dataset to evaluate
the performance of the proposed technique. Comparative study with a few
state-of-the-art methods on text detection, recognition and extraction of
regional language from images shows that the proposed method outperforms the
existing methods.
| null |
http://arxiv.org/abs/1806.06208v2
|
http://arxiv.org/pdf/1806.06208v2.pdf
| null |
[
"Sauradip Nag",
"Pallab Kumar Ganguly",
"Sumit Roy",
"Sourab Jha",
"Krishna Bose",
"Abhishek Jha",
"Kousik Dasgupta"
] |
[
"Text Detection",
"Text Segmentation"
] | 2018-06-16T00:00:00 | 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/meta-learning-searching-in-the-model-space
|
1806.06207
| null | null |
Meta-learning: searching in the model space
|
There is no free lunch, no single learning algorithm that will outperform
other algorithms on all data. In practice different approaches are tried and
the best algorithm selected. An alternative solution is to build new algorithms
on demand by creating a framework that accommodates many algorithms. The best
combination of parameters and procedures is searched here in the space of all
possible models belonging to the framework of Similarity-Based Methods (SBMs).
Such meta-learning approach gives a chance to find the best method in all
cases. Issues related to the meta-learning and first tests of this approach are
presented.
| null |
http://arxiv.org/abs/1806.06207v1
|
http://arxiv.org/pdf/1806.06207v1.pdf
| null |
[
"Włodzisław Duch",
"Karol Grudzińsk"
] |
[
"All",
"Meta-Learning",
"model"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/earl-joint-entity-and-relation-linking-for
|
1801.03825
| null | null |
EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs
|
Many question answering systems over knowledge graphs rely on entity and
relation linking components in order to connect the natural language input to
the underlying knowledge graph. Traditionally, entity linking and relation
linking have been performed either as dependent sequential tasks or as
independent parallel tasks. In this paper, we propose a framework called EARL,
which performs entity linking and relation linking as a joint task. EARL
implements two different solution strategies for which we provide a comparative
analysis in this paper: The first strategy is a formalisation of the joint
entity and relation linking tasks as an instance of the Generalised Travelling
Salesman Problem (GTSP). In order to be computationally feasible, we employ
approximate GTSP solvers. The second strategy uses machine learning in order to
exploit the connection density between nodes in the knowledge graph. It relies
on three base features and re-ranking steps in order to predict entities and
relations. We compare the strategies and evaluate them on a dataset with 5000
questions. Both strategies significantly outperform the current
state-of-the-art approaches for entity and relation linking.
|
Many question answering systems over knowledge graphs rely on entity and relation linking components in order to connect the natural language input to the underlying knowledge graph.
|
http://arxiv.org/abs/1801.03825v4
|
http://arxiv.org/pdf/1801.03825v4.pdf
| null |
[
"Mohnish Dubey",
"Debayan Banerjee",
"Debanjan Chaudhuri",
"Jens Lehmann"
] |
[
"Entity Linking",
"Knowledge Graphs",
"Question Answering",
"Relation",
"Relation Linking",
"Re-Ranking"
] | 2018-01-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/trquery-an-embedding-based-framework-for
|
1806.06205
| null | null |
TrQuery: An Embedding-based Framework for Recommanding SPARQL Queries
|
In this paper, we present an embedding-based framework (TrQuery) for
recommending solutions of a SPARQL query, including approximate solutions when
exact querying solutions are not available due to incompleteness or
inconsistencies of real-world RDF data. Within this framework, embedding is
applied to score solutions together with edit distance so that we could obtain
more fine-grained recommendations than those recommendations via edit distance.
For instance, graphs of two querying solutions with a similar structure can be
distinguished in our proposed framework while the edit distance depending on
structural difference becomes unable. To this end, we propose a novel score
model built on vector space generated in embedding system to compute the
similarity between an approximate subgraph matching and a whole graph matching.
Finally, we evaluate our approach on large RDF datasets DBpedia and YAGO, and
experimental results show that TrQuery exhibits an excellent behavior in terms
of both effectiveness and efficiency.
| null |
http://arxiv.org/abs/1806.06205v1
|
http://arxiv.org/pdf/1806.06205v1.pdf
| null |
[
"Lijing Zhang",
"Xiaowang Zhang",
"Zhiyong Feng"
] |
[
"Graph Matching"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-towards-minimum-hyperspherical
|
1805.09298
| null | null |
Learning towards Minimum Hyperspherical Energy
|
Neural networks are a powerful class of nonlinear functions that can be trained end-to-end on various applications. While the over-parametrization nature in many neural networks renders the ability to fit complex functions and the strong representation power to handle challenging tasks, it also leads to highly correlated neurons that can hurt the generalization ability and incur unnecessary computation cost. As a result, how to regularize the network to avoid undesired representation redundancy becomes an important issue. To this end, we draw inspiration from a well-known problem in physics -- Thomson problem, where one seeks to find a state that distributes N electrons on a unit sphere as evenly as possible with minimum potential energy. In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks. We also propose a few novel variants of MHE, and provide some insights from a theoretical point of view. Finally, we apply neural networks with MHE regularization to several challenging tasks. Extensive experiments demonstrate the effectiveness of our intuition, by showing the superior performance with MHE regularization.
|
In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks.
|
https://arxiv.org/abs/1805.09298v9
|
https://arxiv.org/pdf/1805.09298v9.pdf
|
NeurIPS 2018 12
|
[
"Weiyang Liu",
"Rongmei Lin",
"Zhen Liu",
"Lixin Liu",
"Zhiding Yu",
"Bo Dai",
"Le Song"
] |
[] | 2018-05-23T00:00:00 |
http://papers.nips.cc/paper/7860-learning-towards-minimum-hyperspherical-energy
|
http://papers.nips.cc/paper/7860-learning-towards-minimum-hyperspherical-energy.pdf
|
learning-towards-minimum-hyperspherical-1
| null |
[] |
https://paperswithcode.com/paper/study-of-semi-supervised-approaches-to
|
1806.06200
| null | null |
Study of Semi-supervised Approaches to Improving English-Mandarin Code-Switching Speech Recognition
|
In this paper, we present our overall efforts to improve the performance of a
code-switching speech recognition system using semi-supervised training methods
from lexicon learning to acoustic modeling, on the South East Asian
Mandarin-English (SEAME) data. We first investigate semi-supervised lexicon
learning approach to adapt the canonical lexicon, which is meant to alleviate
the heavily accented pronunciation issue within the code-switching conversation
of the local area. As a result, the learned lexicon yields improved
performance. Furthermore, we attempt to use semi-supervised training to deal
with those transcriptions that are highly mismatched between human transcribers
and ASR system. Specifically, we conduct semi-supervised training assuming
those poorly transcribed data as unsupervised data. We found the
semi-supervised acoustic modeling can lead to improved results. Finally, to
make up for the limitation of the conventional n-gram language models due to
data sparsity issue, we perform lattice rescoring using neural network language
models, and significant WER reduction is obtained.
| null |
http://arxiv.org/abs/1806.06200v1
|
http://arxiv.org/pdf/1806.06200v1.pdf
| null |
[
"Pengcheng Guo",
"Hai-Hua Xu",
"Lei Xie",
"Eng Siong Chng"
] |
[
"speech-recognition",
"Speech Recognition"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/part-aware-fine-grained-object-categorization
|
1806.06198
| null | null |
Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Network
|
Fine-grained object categorization aims for distinguishing objects of subordinate categories that belong to the same entry-level object category. The task is challenging due to the facts that (1) training images with ground-truth labels are difficult to obtain, and (2) variations among different subordinate categories are subtle. It is well established that characterizing features of different subordinate categories are located on local parts of object instances. In fact, careful part annotations are available in many fine-grained categorization datasets. However, manually annotating object parts requires expertise, which is also difficult to generalize to new fine-grained categorization tasks. In this work, we propose a Weakly Supervised Part Detection Network (PartNet) that is able to detect discriminative local parts for use of fine-grained categorization. A vanilla PartNet builds on top of a base subnetwork two parallel streams of upper network layers, which respectively compute scores of classification probabilities (over subordinate categories) and detection probabilities (over a specified number of discriminative part detectors) for local regions of interest (RoIs). The image-level prediction is obtained by aggregating element-wise products of these region-level probabilities. To generate a diverse set of RoIs as inputs of PartNet, we propose a simple Discretized Part Proposals module (DPP) that directly targets for proposing candidates of discriminative local parts, with no bridging via object-level proposals. Experiments on the benchmark CUB-200-2011 and Oxford Flower 102 datasets show the efficacy of our proposed method for both discriminative part detection and fine-grained categorization. In particular, we achieve the new state-of-the-art performance on CUB-200-2011 dataset when ground-truth part annotations are not available.
|
In this work, we propose a Weakly Supervised Part Detection Network (PartNet) that is able to detect discriminative local parts for use of fine-grained categorization.
|
https://arxiv.org/abs/1806.06198v2
|
https://arxiv.org/pdf/1806.06198v2.pdf
| null |
[
"Yabin Zhang",
"Kui Jia",
"Zhixin Wang"
] |
[
"Object",
"Object Categorization"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/show-attend-and-translate-unsupervised-image
|
1806.06195
| null | null |
Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention
|
Image translation between two domains is a class of problems aiming to learn mapping from an input image in the source domain to an output image in the target domain. It has been applied to numerous domains, such as data augmentation, domain adaptation, and unsupervised training. When paired training data is not accessible, image translation becomes an ill-posed problem. We constrain the problem with the assumption that the translated image needs to be perceptually similar to the original image and also appears to be drawn from the new domain, and propose a simple yet effective image translation model consisting of a single generator trained with a self-regularization term and an adversarial term. We further notice that existing image translation techniques are agnostic to the subjects of interest and often introduce unwanted changes or artifacts to the input. Thus we propose to add an attention module to predict an attention map to guide the image translation process. The module learns to attend to key parts of the image while keeping everything else unaltered, essentially avoiding undesired artifacts or changes. The predicted attention map also opens door to applications such as unsupervised segmentation and saliency detection. Extensive experiments and evaluations show that our model while being simpler, achieves significantly better performance than existing image translation methods.
| null |
https://arxiv.org/abs/1806.06195v3
|
https://arxiv.org/pdf/1806.06195v3.pdf
| null |
[
"Chao Yang",
"Taehwan Kim",
"Ruizhe Wang",
"Hao Peng",
"C. -C. Jay Kuo"
] |
[
"Data Augmentation",
"Domain Adaptation",
"Saliency Detection",
"Translation"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/large-scale-fine-grained-categorization-and
|
1806.06193
| null | null |
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
|
Transferring the knowledge learned from large scale datasets (e.g., ImageNet)
via fine-tuning offers an effective solution for domain-specific fine-grained
visual categorization (FGVC) tasks (e.g., recognizing bird species or car make
and model). In such scenarios, data annotation often calls for specialized
domain knowledge and thus is difficult to scale. In this work, we first tackle
a problem in large scale FGVC. Our method won first place in iNaturalist 2017
large scale species classification challenge. Central to the success of our
approach is a training scheme that uses higher image resolution and deals with
the long-tailed distribution of training data. Next, we study transfer learning
via fine-tuning from large scale datasets to small scale, domain-specific FGVC
datasets. We propose a measure to estimate domain similarity via Earth Mover's
Distance and demonstrate that transfer learning benefits from pre-training on a
source domain that is similar to the target domain by this measure. Our
proposed transfer learning outperforms ImageNet pre-training and obtains
state-of-the-art results on multiple commonly used FGVC datasets.
|
We propose a measure to estimate domain similarity via Earth Mover's Distance and demonstrate that transfer learning benefits from pre-training on a source domain that is similar to the target domain by this measure.
|
http://arxiv.org/abs/1806.06193v1
|
http://arxiv.org/pdf/1806.06193v1.pdf
|
CVPR 2018 6
|
[
"Yin Cui",
"Yang song",
"Chen Sun",
"Andrew Howard",
"Serge Belongie"
] |
[
"Fine-Grained Image Classification",
"Fine-Grained Visual Categorization",
"Transfer Learning"
] | 2018-06-16T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Cui_Large_Scale_Fine-Grained_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Cui_Large_Scale_Fine-Grained_CVPR_2018_paper.pdf
|
large-scale-fine-grained-categorization-and-1
| null |
[] |
https://paperswithcode.com/paper/statistics-of-deep-generated-images
|
1708.02688
| null | null |
Statistics of Deep Generated Images
|
Here, we explore the low-level statistics of images generated by state-of-the-art deep generative models. First, Variational auto-encoder (VAE~\cite{kingma2013auto}), Wasserstein generative adversarial network (WGAN~\cite{arjovsky2017wasserstein}) and deep convolutional generative adversarial network (DCGAN~\cite{radford2015unsupervised}) are trained on the ImageNet dataset and a large set of cartoon frames from animations. Then, for images generated by these models as well as natural scenes and cartoons, statistics including mean power spectrum, the number of connected components in a given image area, distribution of random filter responses, and contrast distribution are computed. Our analyses on training images support current findings on scale invariance, non-Gaussianity, and Weibull contrast distribution of natural scenes. We find that although similar results hold over cartoon images, there is still a significant difference between statistics of natural scenes and images generated by VAE, DCGAN and WGAN models. In particular, generated images do not have scale invariant mean power spectrum magnitude, which indicates existence of extra structures in these images. Inspecting how well the statistics of deep generated images match the known statistical properties of natural images, such as scale invariance, non-Gaussianity, and Weibull contrast distribution, can a) reveal the degree to which deep learning models capture the essence of the natural scenes, b) provide a new dimension to evaluate models, and c) allow possible improvement of image generative models (e.g., via defining new loss functions).
|
Here, we explore the low-level statistics of images generated by state-of-the-art deep generative models.
|
https://arxiv.org/abs/1708.02688v5
|
https://arxiv.org/pdf/1708.02688v5.pdf
| null |
[
"Yu Zeng",
"Huchuan Lu",
"Ali Borji"
] |
[
"Generative Adversarial Network"
] | 2017-08-09T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "How do I get a human at Expedia?\r\nHow Do I Get a Human at Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Real-Time Help & Exclusive Travel Deals!Want to speak with a real person at Expedia? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now for immediate support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Skip the wait, get fast answers, and enjoy limited-time offers that make your next journey more affordable and stress-free. Call today and save!\r\n\r\nHow do I get a human at Expedia?\r\nHow Do I Get a Human at Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Real-Time Help & Exclusive Travel Deals!Want to speak with a real person at Expedia? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now for immediate support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Skip the wait, get fast answers, and enjoy limited-time offers that make your next journey more affordable and stress-free. Call today and save!",
"full_name": "HuMan(Expedia)||How do I get a human at Expedia?",
"introduced_year": 2014,
"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": "HuMan(Expedia)||How do I get a human at Expedia?",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": "https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/dcgan/dcgan.py",
"description": "**DCGAN**, or **Deep Convolutional GAN**, is a generative adversarial network architecture. It uses a couple of guidelines, in particular:\r\n\r\n- Replacing any pooling layers with strided convolutions (discriminator) and fractional-strided convolutions (generator).\r\n- Using batchnorm in both the generator and the discriminator.\r\n- Removing fully connected hidden layers for deeper architectures.\r\n- Using [ReLU](https://paperswithcode.com/method/relu) activation in generator for all layers except for the output, which uses tanh.\r\n- Using LeakyReLU activation in the discriminator for all layer.",
"full_name": "Deep Convolutional GAN",
"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": "DCGAN",
"source_title": "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks",
"source_url": "http://arxiv.org/abs/1511.06434v2"
},
{
"code_snippet_url": "",
"description": "In today’s digital age, USD Coin 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 USD Coin transaction not confirmed, your USD Coin wallet not showing balance, or you're trying to recover a lost USD Coin wallet, knowing where to get help is essential. That’s why the USD Coin 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 USD Coin Customer Support Number +1-833-534-1729\r\nUSD Coin 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. USD Coin Transaction Not Confirmed\r\nOne of the most common concerns is when a USD Coin 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. USD Coin 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 USD Coin 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 USD Coin 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 USD Coin 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. USD Coin Deposit Not Received\r\nIf someone has sent you USD Coin 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 USD Coin 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. USD Coin Transaction Stuck or Pending\r\nSometimes your USD Coin 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. USD Coin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word USD Coin 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 USD Coin 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 USD Coin tech.\r\n\r\n24/7 Availability: USD Coin 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 USD Coin Support and Wallet Issues\r\nQ1: Can USD Coin support help me recover stolen BTC?\r\nA: While USD Coin 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: USD Coin 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 USD Coin’s official number (USD Coin is decentralized), it connects you to trained professionals experienced in resolving all major USD Coin issues.\r\n\r\nFinal Thoughts\r\nUSD Coin 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 USD Coin transaction not confirmed, your USD Coin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the USD Coin 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": "USD Coin 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": "USD Coin Customer Service Number +1-833-534-1729",
"source_title": "Auto-Encoding Variational Bayes",
"source_url": "http://arxiv.org/abs/1312.6114v10"
},
{
"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/daheyinyin/wgan",
"description": "**Wasserstein GAN**, or **WGAN**, is a type of generative adversarial network that minimizes an approximation of the Earth-Mover's distance (EM) rather than the Jensen-Shannon divergence as in the original [GAN](https://paperswithcode.com/method/gan) formulation. It leads to more stable training than original GANs with less evidence of mode collapse, as well as meaningful curves that can be used for debugging and searching hyperparameters.",
"full_name": "Wasserstein GAN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Adversarial Networks (GANs)** are a type of generative model that use two networks, a generator to generate images and a discriminator to discriminate between real and fake, to train a model that approximates the distribution of the data. Below you can find a continuously updating list of GANs.",
"name": "Generative Adversarial Networks",
"parent": "Generative Models"
},
"name": "WGAN",
"source_title": "Wasserstein GAN",
"source_url": "http://arxiv.org/abs/1701.07875v3"
}
] |
https://paperswithcode.com/paper/handling-cold-start-collaborative-filtering
|
1806.06192
| null | null |
Handling Cold-Start Collaborative Filtering with Reinforcement Learning
|
A major challenge in recommender systems is handling new users, whom are also
called $\textit{cold-start}$ users. In this paper, we propose a novel approach
for learning an optimal series of questions with which to interview cold-start
users for movie recommender systems. We propose learning interview questions
using Deep Q Networks to create user profiles to make better recommendations to
cold-start users. While our proposed system is trained using a movie
recommender system, our Deep Q Network model should generalize across various
types of recommender systems.
| null |
http://arxiv.org/abs/1806.06192v1
|
http://arxiv.org/pdf/1806.06192v1.pdf
| null |
[
"Hima Varsha Dureddy",
"Zachary Kaden"
] |
[
"Collaborative Filtering",
"Recommendation Systems",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/accurate-spectral-super-resolution-from
|
1806.03575
| null | null |
Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN
|
Different from traditional hyperspectral super-resolution approaches that
focus on improving the spatial resolution, spectral super-resolution aims at
producing a high-resolution hyperspectral image from the RGB observation with
super-resolution in spectral domain. However, it is challenging to accurately
reconstruct a high-dimensional continuous spectrum from three discrete
intensity values at each pixel, since too much information is lost during the
procedure where the latent hyperspectral image is downsampled (e.g., with x10
scaling factor) in spectral domain to produce an RGB observation. To address
this problem, we present a multi-scale deep convolutional neural network (CNN)
to explicitly map the input RGB image into a hyperspectral image. Through
symmetrically downsampling and upsampling the intermediate feature maps in a
cascading paradigm, the local and non-local image information can be jointly
encoded for spectral representation, ultimately improving the spectral
reconstruction accuracy. Extensive experiments on a large hyperspectral dataset
demonstrate the effectiveness of the proposed method.
| null |
http://arxiv.org/abs/1806.03575v3
|
http://arxiv.org/pdf/1806.03575v3.pdf
| null |
[
"Yiqi Yan",
"Lei Zhang",
"Jun Li",
"Wei Wei",
"Yanning Zhang"
] |
[
"Spectral Reconstruction",
"Spectral Super-Resolution",
"Super-Resolution"
] | 2018-06-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/scheduled-policy-optimization-for-natural
|
1806.06187
| null | null |
Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents
|
We investigate the task of learning to follow natural language instructions
by jointly reasoning with visual observations and language inputs. In contrast
to existing methods which start with learning from demonstrations (LfD) and
then use reinforcement learning (RL) to fine-tune the model parameters, we
propose a novel policy optimization algorithm which dynamically schedules
demonstration learning and RL. The proposed training paradigm provides
efficient exploration and better generalization beyond existing methods.
Comparing to existing ensemble models, the best single model based on our
proposed method tremendously decreases the execution error by over 50% on a
block-world environment. To further illustrate the exploration strategy of our
RL algorithm, We also include systematic studies on the evolution of policy
entropy during training.
|
We investigate the task of learning to follow natural language instructions by jointly reasoning with visual observations and language inputs.
|
http://arxiv.org/abs/1806.06187v2
|
http://arxiv.org/pdf/1806.06187v2.pdf
| null |
[
"Wenhan Xiong",
"Xiaoxiao Guo",
"Mo Yu",
"Shiyu Chang",
"Bo-Wen Zhou",
"William Yang Wang"
] |
[
"Efficient Exploration",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/orthogonal-machine-learning-power-and
|
1711.00342
| null | null |
Orthogonal Machine Learning: Power and Limitations
|
Double machine learning provides $\sqrt{n}$-consistent estimates of
parameters of interest even when high-dimensional or nonparametric nuisance
parameters are estimated at an $n^{-1/4}$ rate. The key is to employ
Neyman-orthogonal moment equations which are first-order insensitive to
perturbations in the nuisance parameters. We show that the $n^{-1/4}$
requirement can be improved to $n^{-1/(2k+2)}$ by employing a $k$-th order
notion of orthogonality that grants robustness to more complex or
higher-dimensional nuisance parameters. In the partially linear regression
setting popular in causal inference, we show that we can construct second-order
orthogonal moments if and only if the treatment residual is not normally
distributed. Our proof relies on Stein's lemma and may be of independent
interest. We conclude by demonstrating the robustness benefits of an explicit
doubly-orthogonal estimation procedure for treatment effect.
|
Double machine learning provides $\sqrt{n}$-consistent estimates of parameters of interest even when high-dimensional or nonparametric nuisance parameters are estimated at an $n^{-1/4}$ rate.
|
http://arxiv.org/abs/1711.00342v6
|
http://arxiv.org/pdf/1711.00342v6.pdf
|
ICML 2018 7
|
[
"Lester Mackey",
"Vasilis Syrgkanis",
"Ilias Zadik"
] |
[
"2k",
"BIG-bench Machine Learning",
"Causal Inference",
"LEMMA"
] | 2017-11-01T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2409
|
http://proceedings.mlr.press/v80/mackey18a/mackey18a.pdf
|
orthogonal-machine-learning-power-and-1
| null |
[] |
https://paperswithcode.com/paper/formal-security-analysis-of-neural-networks
|
1804.10829
| null | null |
Formal Security Analysis of Neural Networks using Symbolic Intervals
|
Due to the increasing deployment of Deep Neural Networks (DNNs) in real-world
security-critical domains including autonomous vehicles and collision avoidance
systems, formally checking security properties of DNNs, especially under
different attacker capabilities, is becoming crucial. Most existing security
testing techniques for DNNs try to find adversarial examples without providing
any formal security guarantees about the non-existence of such adversarial
examples. Recently, several projects have used different types of
Satisfiability Modulo Theory (SMT) solvers to formally check security
properties of DNNs. However, all of these approaches are limited by the high
overhead caused by the solver.
In this paper, we present a new direction for formally checking security
properties of DNNs without using SMT solvers. Instead, we leverage interval
arithmetic to compute rigorous bounds on the DNN outputs. Our approach, unlike
existing solver-based approaches, is easily parallelizable. We further present
symbolic interval analysis along with several other optimizations to minimize
overestimations of output bounds.
We design, implement, and evaluate our approach as part of ReluVal, a system
for formally checking security properties of Relu-based DNNs. Our extensive
empirical results show that ReluVal outperforms Reluplex, a state-of-the-art
solver-based system, by 200 times on average. On a single 8-core machine
without GPUs, within 4 hours, ReluVal is able to verify a security property
that Reluplex deemed inconclusive due to timeout after running for more than 5
days. Our experiments demonstrate that symbolic interval analysis is a
promising new direction towards rigorously analyzing different security
properties of DNNs.
|
In this paper, we present a new direction for formally checking security properties of DNNs without using SMT solvers.
|
http://arxiv.org/abs/1804.10829v3
|
http://arxiv.org/pdf/1804.10829v3.pdf
| null |
[
"Shiqi Wang",
"Kexin Pei",
"Justin Whitehouse",
"Junfeng Yang",
"Suman Jana"
] |
[
"Autonomous Vehicles",
"Collision Avoidance"
] | 2018-04-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-neural-painter-multi-turn-image
|
1806.06183
| null | null |
The Neural Painter: Multi-Turn Image Generation
|
In this work we combine two research threads from Vision/ Graphics and
Natural Language Processing to formulate an image generation task conditioned
on attributes in a multi-turn setting. By multiturn, we mean the image is
generated in a series of steps of user-specified conditioning information. Our
proposed approach is practically useful and offers insights into neural
interpretability. We introduce a framework that includes a novel training
algorithm as well as model improvements built for the multi-turn setting. We
demonstrate that this framework generates a sequence of images that match the
given conditioning information and that this task is useful for more detailed
benchmarking and analysis of conditional image generation methods.
| null |
http://arxiv.org/abs/1806.06183v1
|
http://arxiv.org/pdf/1806.06183v1.pdf
| null |
[
"Ryan Y. Benmalek",
"Claire Cardie",
"Serge Belongie",
"Xiadong He",
"Jianfeng Gao"
] |
[
"Benchmarking",
"Conditional Image Generation",
"Image Generation"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/component-spd-matrices-a-lower-dimensional
|
1806.06178
| null | null |
Component SPD Matrices: A lower-dimensional discriminative data descriptor for image set classification
|
In the domain of pattern recognition, using the SPD (Symmetric Positive
Definite) matrices to represent data and taking the metrics of resulting
Riemannian manifold into account have been widely used for the task of image
set classification. In this paper, we propose a new data representation
framework for image sets named CSPD (Component Symmetric Positive Definite).
Firstly, we obtain sub-image sets by dividing the image set into square blocks
with the same size, and use traditional SPD model to describe them. Then, we
use the results of the Riemannian kernel on SPD matrices as similarities of
corresponding sub-image sets. Finally, the CSPD matrix appears in the form of
the kernel matrix for all the sub-image sets, and CSPDi,j denotes the
similarity between i-th sub-image set and j-th sub-image set. Here, the
Riemannian kernel is shown to satisfy the Mercer's theorem, so our proposed
CSPD matrix is symmetric and positive definite and also lies on a Riemannian
manifold. On three benchmark datasets, experimental results show that CSPD is a
lower-dimensional and more discriminative data descriptor for the task of image
set classification.
| null |
http://arxiv.org/abs/1806.06178v1
|
http://arxiv.org/pdf/1806.06178v1.pdf
| null |
[
"Kai-Xuan Chen",
"Xiao-Jun Wu"
] |
[
"General Classification"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/riemannian-kernel-based-nystrom-method-for
|
1806.06177
| null | null |
Riemannian kernel based Nyström method for approximate infinite-dimensional covariance descriptors with application to image set classification
|
In the domain of pattern recognition, using the CovDs (Covariance Descriptors) to represent data and taking the metrics of the resulting Riemannian manifold into account have been widely adopted for the task of image set classification. Recently, it has been proven that infinite-dimensional CovDs are more discriminative than their low-dimensional counterparts. However, the form of infinite-dimensional CovDs is implicit and the computational load is high. We propose a novel framework for representing image sets by approximating infinite-dimensional CovDs in the paradigm of the Nystr\"om method based on a Riemannian kernel. We start by modeling the images via CovDs, which lie on the Riemannian manifold spanned by SPD (Symmetric Positive Definite) matrices. We then extend the Nystr\"om method to the SPD manifold and obtain the approximations of CovDs in RKHS (Reproducing Kernel Hilbert Space). Finally, we approximate infinite-dimensional CovDs via these approximations. Empirically, we apply our framework to the task of image set classification. The experimental results obtained on three benchmark datasets show that our proposed approximate infinite-dimensional CovDs outperform the original CovDs.
|
We propose a novel framework for representing image sets by approximating infinite-dimensional CovDs in the paradigm of the Nystr\"om method based on a Riemannian kernel.
|
https://arxiv.org/abs/1806.06177v2
|
https://arxiv.org/pdf/1806.06177v2.pdf
| null |
[
"Kai-Xuan Chen",
"Xiao-Jun Wu",
"Rui Wang",
"Josef Kittler"
] |
[
"General Classification"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-factorized-multimodal
|
1806.06176
| null |
rygqqsA9KX
|
Learning Factorized Multimodal Representations
|
Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information. Although the presence of multiple modalities provides additional valuable information, there are two key challenges to address when learning from multimodal data: 1) models must learn the complex intra-modal and cross-modal interactions for prediction and 2) models must be robust to unexpected missing or noisy modalities during testing. In this paper, we propose to optimize for a joint generative-discriminative objective across multimodal data and labels. We introduce a model that factorizes representations into two sets of independent factors: multimodal discriminative and modality-specific generative factors. Multimodal discriminative factors are shared across all modalities and contain joint multimodal features required for discriminative tasks such as sentiment prediction. Modality-specific generative factors are unique for each modality and contain the information required for generating data. Experimental results show that our model is able to learn meaningful multimodal representations that achieve state-of-the-art or competitive performance on six multimodal datasets. Our model demonstrates flexible generative capabilities by conditioning on independent factors and can reconstruct missing modalities without significantly impacting performance. Lastly, we interpret our factorized representations to understand the interactions that influence multimodal learning.
|
Multimodal discriminative factors are shared across all modalities and contain joint multimodal features required for discriminative tasks such as sentiment prediction.
|
https://arxiv.org/abs/1806.06176v3
|
https://arxiv.org/pdf/1806.06176v3.pdf
|
ICLR 2019 5
|
[
"Yao-Hung Hubert Tsai",
"Paul Pu Liang",
"Amir Zadeh",
"Louis-Philippe Morency",
"Ruslan Salakhutdinov"
] |
[
"Representation Learning"
] | 2018-06-16T00:00:00 |
https://openreview.net/forum?id=rygqqsA9KX
|
https://openreview.net/pdf?id=rygqqsA9KX
|
learning-factorized-multimodal-1
| null |
[] |
https://paperswithcode.com/paper/ensemble-pruning-based-on-objection
|
1806.04899
| null | null |
Ensemble Pruning based on Objection Maximization with a General Distributed Framework
|
Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space. Accuracy and diversity serve as two crucial factors while they usually conflict with each other. To balance both of them, we formalize the ensemble pruning problem as an objection maximization problem based on information entropy. Then we propose an ensemble pruning method including a centralized version and a distributed version, in which the latter is to speed up the former. At last, we extract a general distributed framework for ensemble pruning, which can be widely suitable for most of the existing ensemble pruning methods and achieve less time consuming without much accuracy degradation. Experimental results validate the efficiency of our framework and methods, particularly concerning a remarkable improvement of the execution speed, accompanied by gratifying accuracy performance.
|
Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space.
|
https://arxiv.org/abs/1806.04899v3
|
https://arxiv.org/pdf/1806.04899v3.pdf
| null |
[
"Yijun Bian",
"Yijun Wang",
"Yaqiang Yao",
"Huanhuan Chen"
] |
[
"Diversity",
"Ensemble Learning",
"Ensemble Pruning"
] | 2018-06-13T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Pruning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Model Compression",
"parent": null
},
"name": "Pruning",
"source_title": "Pruning Filters for Efficient ConvNets",
"source_url": "http://arxiv.org/abs/1608.08710v3"
},
{
"code_snippet_url": "https://github.com/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/revisiting-deep-intrinsic-image
|
1701.02965
| null | null |
Revisiting Deep Intrinsic Image Decompositions
|
While invaluable for many computer vision applications, decomposing a natural
image into intrinsic reflectance and shading layers represents a challenging,
underdetermined inverse problem. As opposed to strict reliance on conventional
optimization or filtering solutions with strong prior assumptions, deep
learning based approaches have also been proposed to compute intrinsic image
decompositions when granted access to sufficient labeled training data. The
downside is that current data sources are quite limited, and broadly speaking
fall into one of two categories: either dense fully-labeled images in
synthetic/narrow settings, or weakly-labeled data from relatively diverse
natural scenes. In contrast to many previous learning-based approaches, which
are often tailored to the structure of a particular dataset (and may not work
well on others), we adopt core network structures that universally reflect
loose prior knowledge regarding the intrinsic image formation process and can
be largely shared across datasets. We then apply flexibly supervised loss
layers that are customized for each source of ground truth labels. The
resulting deep architecture achieves state-of-the-art results on all of the
major intrinsic image benchmarks, and runs considerably faster than most at
test time.
| null |
http://arxiv.org/abs/1701.02965v8
|
http://arxiv.org/pdf/1701.02965v8.pdf
|
CVPR 2018 6
|
[
"Qingnan Fan",
"Jiaolong Yang",
"Gang Hua",
"Baoquan Chen",
"David Wipf"
] |
[] | 2017-01-11T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Fan_Revisiting_Deep_Intrinsic_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Fan_Revisiting_Deep_Intrinsic_CVPR_2018_paper.pdf
|
revisiting-deep-intrinsic-image-1
| null |
[] |
https://paperswithcode.com/paper/on-the-complexity-of-detecting-convexity-over
|
1806.06173
| null | null |
On the Complexity of Detecting Convexity over a Box
|
It has recently been shown that the problem of testing global convexity of
polynomials of degree four is {strongly} NP-hard, answering an open question of
N.Z. Shor. This result is minimal in the degree of the polynomial when global
convexity is of concern. In a number of applications however, one is interested
in testing convexity only over a compact region, most commonly a box (i.e.,
hyper-rectangle). In this paper, we show that this problem is also strongly
NP-hard, in fact for polynomials of degree as low as three. This result is
minimal in the degree of the polynomial and in some sense justifies why
convexity detection in nonlinear optimization solvers is limited to quadratic
functions or functions with special structure. As a byproduct, our proof shows
that the problem of testing whether all matrices in an interval family are
positive semidefinite is strongly NP-hard. This problem, which was previously
shown to be (weakly) NP-hard by Nemirovski, is of independent interest in the
theory of robust control.
| null |
http://arxiv.org/abs/1806.06173v2
|
http://arxiv.org/pdf/1806.06173v2.pdf
| null |
[
"Amir Ali Ahmadi",
"Georgina Hall"
] |
[
"Open-Ended Question Answering"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/random-forest-for-label-ranking
|
1608.07710
| null | null |
Random Forest for Label Ranking
|
Label ranking aims to learn a mapping from instances to rankings over a
finite number of predefined labels. Random forest is a powerful and one of the
most successful general-purpose machine learning algorithms of modern times. In
this paper, we present a powerful random forest label ranking method which uses
random decision trees to retrieve nearest neighbors. We have developed a novel
two-step rank aggregation strategy to effectively aggregate neighboring
rankings discovered by the random forest into a final predicted ranking.
Compared with existing methods, the new random forest method has many
advantages including its intrinsically scalable tree data structure, highly
parallel-able computational architecture and much superior performance. We
present extensive experimental results to demonstrate that our new method
achieves the highly competitive performance compared with state-of-the-art
methods for datasets with complete ranking and datasets with only partial
ranking information.
| null |
http://arxiv.org/abs/1608.07710v3
|
http://arxiv.org/pdf/1608.07710v3.pdf
| null |
[
"Yangming Zhou",
"Guoping Qiu"
] |
[] | 2016-08-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/discrete-sequential-prediction-of-continuous
|
1705.05035
| null |
r1SuFjkRW
|
Discrete Sequential Prediction of Continuous Actions for Deep RL
|
It has long been assumed that high dimensional continuous control problems cannot be solved effectively by discretizing individual dimensions of the action space due to the exponentially large number of bins over which policies would have to be learned. In this paper, we draw inspiration from the recent success of sequence-to-sequence models for structured prediction problems to develop policies over discretized spaces. Central to this method is the realization that complex functions over high dimensional spaces can be modeled by neural networks that predict one dimension at a time. Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions. With this parameterization, it is possible to both leverage the compositional structure of action spaces during learning, as well as compute maxima over action spaces (approximately). On a simple example task we demonstrate empirically that our method can perform global search, which effectively gets around the local optimization issues that plague DDPG. We apply the technique to off-policy (Q-learning) methods and show that our method can achieve the state-of-the-art for off-policy methods on several continuous control tasks.
| null |
https://arxiv.org/abs/1705.05035v3
|
https://arxiv.org/pdf/1705.05035v3.pdf
|
ICLR 2018 1
|
[
"Luke Metz",
"Julian Ibarz",
"Navdeep Jaitly",
"James Davidson"
] |
[
"continuous-control",
"Continuous Control",
"Prediction",
"Q-Learning",
"Structured Prediction"
] | 2017-05-14T00:00:00 |
https://openreview.net/forum?id=r1SuFjkRW
|
https://openreview.net/pdf?id=r1SuFjkRW
|
discrete-sequential-prediction-of-continuous-1
| null |
[
{
"code_snippet_url": "",
"description": "**Weight Decay**, or **$L_{2}$ Regularization**, is a regularization technique applied to the weights of a neural network. We minimize a loss function compromising both the primary loss function and a penalty on the $L\\_{2}$ Norm of the weights:\r\n\r\n$$L\\_{new}\\left(w\\right) = L\\_{original}\\left(w\\right) + \\lambda{w^{T}w}$$\r\n\r\nwhere $\\lambda$ is a value determining the strength of the penalty (encouraging smaller weights). \r\n\r\nWeight decay can be incorporated directly into the weight update rule, rather than just implicitly by defining it through to objective function. Often weight decay refers to the implementation where we specify it directly in the weight update rule (whereas L2 regularization is usually the implementation which is specified in the objective function).\r\n\r\nImage Source: Deep Learning, Goodfellow et al",
"full_name": "Weight Decay",
"introduced_year": 1943,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Weight Decay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "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/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": "",
"description": "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": "**Experience Replay** is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, $e\\_{t} = \\left(s\\_{t}, a\\_{t}, r\\_{t}, s\\_{t+1}\\right)$ in a data-set $D = e\\_{1}, \\cdots, e\\_{N}$ , pooled over many episodes into a replay memory. We then usually sample the memory randomly for a minibatch of experience, and use this to learn off-policy, as with Deep Q-Networks. This tackles the problem of autocorrelation leading to unstable training, by making the problem more like a supervised learning problem.\r\n\r\nImage Credit: [Hands-On Reinforcement Learning with Python, Sudharsan Ravichandiran](https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781788836524)",
"full_name": "Experience Replay",
"introduced_year": 1993,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Replay Memory",
"parent": null
},
"name": "Experience Replay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**DDPG**, or **Deep Deterministic Policy Gradient**, is an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. It combines the actor-critic approach with insights from [DQNs](https://paperswithcode.com/method/dqn): in particular, the insights that 1) the network is trained off-policy with samples from a replay buffer to minimize correlations between samples, and 2) the network is trained with a target Q network to give consistent targets during temporal difference backups. DDPG makes use of the same ideas along with [batch normalization](https://paperswithcode.com/method/batch-normalization).",
"full_name": "Deep Deterministic Policy Gradient",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "**Policy Gradient Methods** try to optimize the policy function directly in reinforcement learning. This contrasts with, for example, Q-Learning, where the policy manifests itself as maximizing a value function. Below you can find a continuously updating catalog of policy gradient methods.",
"name": "Policy Gradient Methods",
"parent": null
},
"name": "DDPG",
"source_title": "Continuous control with deep reinforcement learning",
"source_url": "https://arxiv.org/abs/1509.02971v6"
}
] |
https://paperswithcode.com/paper/semantic-video-segmentation-a-review-on
|
1806.06172
| null | null |
Semantic Video Segmentation: A Review on Recent Approaches
|
This paper gives an overview on semantic segmentation consists of an
explanation of this field, it's status and relation with other vision
fundamental tasks, different datasets and common evaluation parameters that
have been used by researchers. This survey also includes an overall review on a
variety of recent approaches (RDF, MRF, CRF, etc.) and their advantages and
challenges and shows the superiority of CNN-based semantic segmentation systems
on CamVid and NYUDv2 datasets. In addition, some areas that is ideal for future
work have mentioned.
| null |
http://arxiv.org/abs/1806.06172v1
|
http://arxiv.org/pdf/1806.06172v1.pdf
| null |
[
"Mohammad Hajizadeh Saffar",
"Mohsen Fayyaz",
"Mohammad Sabokrou",
"Mahmood Fathy"
] |
[
"Segmentation",
"Semantic Segmentation",
"Video Segmentation",
"Video Semantic Segmentation"
] | 2018-06-16T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Conditional Random Fields** or **CRFs** are a type of probabilistic graph model that take neighboring sample context into account for tasks like classification. Prediction is modeled as a graphical model, which implements dependencies between the predictions. Graph choice depends on the application, for example linear chain CRFs are popular in natural language processing, whereas in image-based tasks, the graph would connect to neighboring locations in an image to enforce that they have similar predictions.\r\n\r\nImage Credit: [Charles Sutton and Andrew McCallum, An Introduction to Conditional Random Fields](https://homepages.inf.ed.ac.uk/csutton/publications/crftut-fnt.pdf)",
"full_name": "Conditional Random Field",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Structured Prediction** methods deal with structured outputs with multiple interdependent outputs. Below you can find a continuously updating list of structured prediction methods.",
"name": "Structured Prediction",
"parent": null
},
"name": "CRF",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/barc-backward-reachability-curriculum-for
|
1806.06161
| null | null |
BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning
|
Model-free Reinforcement Learning (RL) offers an attractive approach to learn
control policies for high-dimensional systems, but its relatively poor sample
complexity often forces training in simulated environments. Even in simulation,
goal-directed tasks whose natural reward function is sparse remain intractable
for state-of-the-art model-free algorithms for continuous control. The
bottleneck in these tasks is the prohibitive amount of exploration required to
obtain a learning signal from the initial state of the system. In this work, we
leverage physical priors in the form of an approximate system dynamics model to
design a curriculum scheme for a model-free policy optimization algorithm. Our
Backward Reachability Curriculum (BaRC) begins policy training from states that
require a small number of actions to accomplish the task, and expands the
initial state distribution backwards in a dynamically-consistent manner once
the policy optimization algorithm demonstrates sufficient performance. BaRC is
general, in that it can accelerate training of any model-free RL algorithm on a
broad class of goal-directed continuous control MDPs. Its curriculum strategy
is physically intuitive, easy-to-tune, and allows incorporating physical priors
to accelerate training without hindering the performance, flexibility, and
applicability of the model-free RL algorithm. We evaluate our approach on two
representative dynamic robotic learning problems and find substantial
performance improvement relative to previous curriculum generation techniques
and naive exploration strategies.
|
Our Backward Reachability Curriculum (BaRC) begins policy training from states that require a small number of actions to accomplish the task, and expands the initial state distribution backwards in a dynamically-consistent manner once the policy optimization algorithm demonstrates sufficient performance.
|
http://arxiv.org/abs/1806.06161v2
|
http://arxiv.org/pdf/1806.06161v2.pdf
| null |
[
"Boris Ivanovic",
"James Harrison",
"Apoorva Sharma",
"Mo Chen",
"Marco Pavone"
] |
[
"continuous-control",
"Continuous Control",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/object-level-visual-reasoning-in-videos
|
1806.06157
| null | null |
Object Level Visual Reasoning in Videos
|
Human activity recognition is typically addressed by detecting key concepts
like global and local motion, features related to object classes present in the
scene, as well as features related to the global context. The next open
challenges in activity recognition require a level of understanding that pushes
beyond this and call for models with capabilities for fine distinction and
detailed comprehension of interactions between actors and objects in a scene.
We propose a model capable of learning to reason about semantically meaningful
spatiotemporal interactions in videos. The key to our approach is a choice of
performing this reasoning at the object level through the integration of state
of the art object detection networks. This allows the model to learn detailed
spatial interactions that exist at a semantic, object-interaction relevant
level. We evaluate our method on three standard datasets (Twenty-BN
Something-Something, VLOG and EPIC Kitchens) and achieve state of the art
results on all of them. Finally, we show visualizations of the interactions
learned by the model, which illustrate object classes and their interactions
corresponding to different activity classes.
|
Human activity recognition is typically addressed by detecting key concepts like global and local motion, features related to object classes present in the scene, as well as features related to the global context.
|
http://arxiv.org/abs/1806.06157v3
|
http://arxiv.org/pdf/1806.06157v3.pdf
|
ECCV 2018 9
|
[
"Fabien Baradel",
"Natalia Neverova",
"Christian Wolf",
"Julien Mille",
"Greg Mori"
] |
[
"Activity Recognition",
"Human Activity Recognition",
"Object",
"object-detection",
"Object Detection",
"Visual Reasoning"
] | 2018-06-16T00:00:00 |
http://openaccess.thecvf.com/content_ECCV_2018/html/Fabien_Baradel_Object_Level_Visual_ECCV_2018_paper.html
|
http://openaccess.thecvf.com/content_ECCV_2018/papers/Fabien_Baradel_Object_Level_Visual_ECCV_2018_paper.pdf
|
object-level-visual-reasoning-in-videos-1
| null |
[] |
https://paperswithcode.com/paper/knowledge-enriched-two-layered-attention
|
1805.07819
| null | null |
Knowledge-enriched Two-layered Attention Network for Sentiment Analysis
|
We propose a novel two-layered attention network based on Bidirectional Long
Short-Term Memory for sentiment analysis. The novel two-layered attention
network takes advantage of the external knowledge bases to improve the
sentiment prediction. It uses the Knowledge Graph Embedding generated using the
WordNet. We build our model by combining the two-layered attention network with
the supervised model based on Support Vector Regression using a Multilayer
Perceptron network for sentiment analysis. We evaluate our model on the
benchmark dataset of SemEval 2017 Task 5. Experimental results show that the
proposed model surpasses the top system of SemEval 2017 Task 5. The model
performs significantly better by improving the state-of-the-art system at
SemEval 2017 Task 5 by 1.7 and 3.7 points for sub-tracks 1 and 2 respectively.
| null |
http://arxiv.org/abs/1805.07819v4
|
http://arxiv.org/pdf/1805.07819v4.pdf
|
NAACL 2018 6
|
[
"Abhishek Kumar",
"Daisuke Kawahara",
"Sadao Kurohashi"
] |
[
"Graph Embedding",
"Knowledge Graph Embedding",
"Sentiment Analysis",
"Vocal Bursts Valence Prediction"
] | 2018-05-20T00:00:00 |
https://aclanthology.org/N18-2041
|
https://aclanthology.org/N18-2041.pdf
|
knowledge-enriched-two-layered-attention-1
| null |
[] |
https://paperswithcode.com/paper/new-techniques-for-preserving-global
|
1805.03383
| null | null |
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution
|
This work identifies and addresses two important technical challenges in
single-image super-resolution: (1) how to upsample an image without magnifying
noise and (2) how to preserve large scale structure when upsampling. We
summarize the techniques we developed for our second place entry in Track 1
(Bicubic Downsampling), seventh place entry in Track 2 (Realistic Adverse
Conditions), and seventh place entry in Track 3 (Realistic difficult) in the
2018 NTIRE Super-Resolution Challenge. Furthermore, we present new neural
network architectures that specifically address the two challenges listed
above: denoising and preservation of large-scale structure.
|
This work identifies and addresses two important technical challenges in single-image super-resolution: (1) how to upsample an image without magnifying noise and (2) how to preserve large scale structure when upsampling.
|
http://arxiv.org/abs/1805.03383v2
|
http://arxiv.org/pdf/1805.03383v2.pdf
| null |
[
"Yijie Bei",
"Alex Damian",
"Shijia Hu",
"Sachit Menon",
"Nikhil Ravi",
"Cynthia Rudin"
] |
[
"Denoising",
"Image Super-Resolution",
"Super-Resolution"
] | 2018-05-09T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/anticipation-in-human-robot-cooperation-a
|
1802.10503
| null | null |
Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction
|
Close human-robot cooperation is a key enabler for new developments in
advanced manufacturing and assistive applications. Close cooperation require
robots that can predict human actions and intent, and understand human
non-verbal cues. Recent approaches based on neural networks have led to
encouraging results in the human action prediction problem both in continuous
and discrete spaces. Our approach extends the research in this direction. Our
contributions are three-fold. First, we validate the use of gaze and body pose
cues as a means of predicting human action through a feature selection method.
Next, we address two shortcomings of existing literature: predicting multiple
and variable-length action sequences. This is achieved by introducing an
encoder-decoder recurrent neural network topology in the discrete action
prediction problem. In addition, we theoretically demonstrate the importance of
predicting multiple action sequences as a means of estimating the stochastic
reward in a human robot cooperation scenario. Finally, we show the ability to
effectively train the prediction model on a action prediction dataset,
involving human motion data, and explore the influence of the model's
parameters on its performance. Source code repository:
https://github.com/pschydlo/ActionAnticipation
|
Recent approaches based on neural networks have led to encouraging results in the human action prediction problem both in continuous and discrete spaces.
|
http://arxiv.org/abs/1802.10503v3
|
http://arxiv.org/pdf/1802.10503v3.pdf
| null |
[
"Paul Schydlo",
"Mirko Rakovic",
"Lorenzo Jamone",
"José Santos-Victor"
] |
[
"Decoder",
"feature selection",
"Prediction"
] | 2018-02-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/image-transformer
|
1802.05751
| null |
r16Vyf-0-
|
Image Transformer
|
Image generation has been successfully cast as an autoregressive sequence
generation or transformation problem. Recent work has shown that self-attention
is an effective way of modeling textual sequences. In this work, we generalize
a recently proposed model architecture based on self-attention, the
Transformer, to a sequence modeling formulation of image generation with a
tractable likelihood. By restricting the self-attention mechanism to attend to
local neighborhoods we significantly increase the size of images the model can
process in practice, despite maintaining significantly larger receptive fields
per layer than typical convolutional neural networks. While conceptually
simple, our generative models significantly outperform the current state of the
art in image generation on ImageNet, improving the best published negative
log-likelihood on ImageNet from 3.83 to 3.77. We also present results on image
super-resolution with a large magnification ratio, applying an encoder-decoder
configuration of our architecture. In a human evaluation study, we find that
images generated by our super-resolution model fool human observers three times
more often than the previous state of the art.
| null |
http://arxiv.org/abs/1802.05751v3
|
http://arxiv.org/pdf/1802.05751v3.pdf
| null |
[
"Niki Parmar",
"Ashish Vaswani",
"Jakob Uszkoreit",
"Łukasz Kaiser",
"Noam Shazeer",
"Alexander Ku",
"Dustin Tran"
] |
[
"Decoder",
"Density Estimation",
"Image Generation",
"Image Super-Resolution",
"Super-Resolution"
] | 2018-02-15T00:00:00 |
https://openreview.net/forum?id=r16Vyf-0-
|
https://openreview.net/pdf?id=r16Vyf-0-
|
image-transformer-2
| null |
[] |
https://paperswithcode.com/paper/characterizing-departures-from-linearity-in
|
1806.04508
| null | null |
Characterizing Departures from Linearity in Word Translation
|
We investigate the behavior of maps learned by machine translation methods.
The maps translate words by projecting between word embedding spaces of
different languages. We locally approximate these maps using linear maps, and
find that they vary across the word embedding space. This demonstrates that the
underlying maps are non-linear. Importantly, we show that the locally linear
maps vary by an amount that is tightly correlated with the distance between the
neighborhoods on which they are trained. Our results can be used to test
non-linear methods, and to drive the design of more accurate maps for word
translation.
| null |
http://arxiv.org/abs/1806.04508v2
|
http://arxiv.org/pdf/1806.04508v2.pdf
|
ACL 2018 7
|
[
"Ndapa Nakashole",
"Raphael Flauger"
] |
[
"Machine Translation",
"Translation",
"Word Translation"
] | 2018-06-07T00:00:00 |
https://aclanthology.org/P18-2036
|
https://aclanthology.org/P18-2036.pdf
|
characterizing-departures-from-linearity-in-1
| null |
[] |
https://paperswithcode.com/paper/learning-what-information-to-give-in
|
1805.08263
| null | null |
Learning What Information to Give in Partially Observed Domains
|
In many robotic applications, an autonomous agent must act within and explore
a partially observed environment that is unobserved by its human teammate. We
consider such a setting in which the agent can, while acting, transmit
declarative information to the human that helps them understand aspects of this
unseen environment. In this work, we address the algorithmic question of how
the agent should plan out what actions to take and what information to
transmit. Naturally, one would expect the human to have preferences, which we
model information-theoretically by scoring transmitted information based on the
change it induces in weighted entropy of the human's belief state. We formulate
this setting as a belief MDP and give a tractable algorithm for solving it
approximately. Then, we give an algorithm that allows the agent to learn the
human's preferences online, through exploration. We validate our approach
experimentally in simulated discrete and continuous partially observed
search-and-recover domains. Visit http://tinyurl.com/chitnis-corl-18 for a
supplementary video.
| null |
http://arxiv.org/abs/1805.08263v4
|
http://arxiv.org/pdf/1805.08263v4.pdf
| null |
[
"Rohan Chitnis",
"Leslie Pack Kaelbling",
"Tomás Lozano-Pérez"
] |
[] | 2018-05-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/kernel-machines-that-adapt-to-gpus-for
|
1806.06144
| null | null |
Kernel machines that adapt to GPUs for effective large batch training
|
Modern machine learning models are typically trained using Stochastic
Gradient Descent (SGD) on massively parallel computing resources such as GPUs.
Increasing mini-batch size is a simple and direct way to utilize the parallel
computing capacity. For small batch an increase in batch size results in the
proportional reduction in the training time, a phenomenon known as linear
scaling. However, increasing batch size beyond a certain value leads to no
further improvement in training time. In this paper we develop the first
analytical framework that extends linear scaling to match the parallel
computing capacity of a resource. The framework is designed for a class of
classical kernel machines. It automatically modifies a standard kernel machine
to output a mathematically equivalent prediction function, yet allowing for
extended linear scaling, i.e., higher effective parallelization and faster
training time on given hardware.
The resulting algorithms are accurate, principled and very fast. For example,
using a single Titan Xp GPU, training on ImageNet with $1.3\times 10^6$ data
points and $1000$ labels takes under an hour, while smaller datasets, such as
MNIST, take seconds. As the parameters are chosen analytically, based on the
theoretical bounds, little tuning beyond selecting the kernel and the kernel
parameter is needed, further facilitating the practical use of these methods.
|
In this paper we develop the first analytical framework that extends linear scaling to match the parallel computing capacity of a resource.
|
http://arxiv.org/abs/1806.06144v3
|
http://arxiv.org/pdf/1806.06144v3.pdf
| null |
[
"Siyuan Ma",
"Mikhail Belkin"
] |
[
"GPU"
] | 2018-06-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/sparse-pseudo-input-local-kriging-for-large
|
1508.01248
| null | null |
Sparse Pseudo-input Local Kriging for Large Spatial Datasets with Exogenous Variables
|
We study large-scale spatial systems that contain exogenous variables, e.g. environmental factors that are significant predictors in spatial processes. Building predictive models for such processes is challenging because the large numbers of observations present makes it inefficient to apply full Kriging. In order to reduce computational complexity, this paper proposes Sparse Pseudo-input Local Kriging (SPLK), which utilizes hyperplanes to partition a domain into smaller subdomains and then applies a sparse approximation of the full Kriging to each subdomain. We also develop an optimization procedure to find the desired hyperplanes. To alleviate the problem of discontinuity in the global predictor, we impose continuity constraints on the boundaries of the neighboring subdomains. Furthermore, partitioning the domain into smaller subdomains makes it possible to use different parameter values for the covariance function in each region and, therefore, the heterogeneity in the data structure can be effectively captured. Numerical experiments demonstrate that SPLK outperforms, or is comparable to, the algorithms commonly applied to spatial datasets.
| null |
https://arxiv.org/abs/1508.01248v4
|
https://arxiv.org/pdf/1508.01248v4.pdf
| null |
[
"Babak Farmanesh",
"Arash Pourhabib"
] |
[] | 2015-08-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/morse-theory-and-an-impossibility-theorem-for
|
1806.06142
| null | null |
Possibility results for graph clustering: A novel consistency axiom
|
Kleinberg introduced three natural clustering properties, or axioms, and showed they cannot be simultaneously satisfied by any clustering algorithm. We present a new clustering property, Monotonic Consistency, which avoids the well-known problematic behaviour of Kleinberg's Consistency axiom, and the impossibility result. Namely, we describe a clustering algorithm, Morse Clustering, inspired by Morse Theory in Differential Topology, which satisfies Kleinberg's original axioms with Consistency replaced by Monotonic Consistency. Morse clustering uncovers the underlying flow structure on a set or graph and returns a partition into trees representing basins of attraction of critical vertices. We also generalise Kleinberg's axiomatic approach to sparse graphs, showing an impossibility result for Consistency, and a possibility result for Monotonic Consistency and Morse clustering.
| null |
https://arxiv.org/abs/1806.06142v6
|
https://arxiv.org/pdf/1806.06142v6.pdf
| null |
[
"Fabio Strazzeri",
"Rubén J. Sánchez-García"
] |
[
"Clustering",
"Graph Clustering"
] | 2018-06-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/decomposition-of-uncertainty-in-bayesian-deep
|
1710.07283
| null | null |
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
|
Bayesian neural networks with latent variables are scalable and flexible
probabilistic models: They account for uncertainty in the estimation of the
network weights and, by making use of latent variables, can capture complex
noise patterns in the data. We show how to extract and decompose uncertainty
into epistemic and aleatoric components for decision-making purposes. This
allows us to successfully identify informative points for active learning of
functions with heteroscedastic and bimodal noise. Using the decomposition we
further define a novel risk-sensitive criterion for reinforcement learning to
identify policies that balance expected cost, model-bias and noise aversion.
|
Bayesian neural networks with latent variables are scalable and flexible probabilistic models: They account for uncertainty in the estimation of the network weights and, by making use of latent variables, can capture complex noise patterns in the data.
|
http://arxiv.org/abs/1710.07283v4
|
http://arxiv.org/pdf/1710.07283v4.pdf
|
ICML 2018 7
|
[
"Stefan Depeweg",
"José Miguel Hernández-Lobato",
"Finale Doshi-Velez",
"Steffen Udluft"
] |
[
"Active Learning",
"Decision Making",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2017-10-19T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2262
|
http://proceedings.mlr.press/v80/depeweg18a/depeweg18a.pdf
|
decomposition-of-uncertainty-in-bayesian-deep-1
| null |
[] |
https://paperswithcode.com/paper/high-dimensional-data-enrichment
|
1806.04047
| null | null |
High Dimensional Data Enrichment: Interpretable, Fast, and Data-Efficient
|
We consider the problem of multi-task learning in the high dimensional setting. In particular, we introduce an estimator and investigate its statistical and computational properties for the problem of multiple connected linear regressions known as Data Enrichment/Sharing. The between-tasks connections are captured by a cross-tasks \emph{common parameter}, which gets refined by per-task \emph{individual parameters}. Any convex function, e.g., norm, can characterize the structure of both common and individual parameters. We delineate the sample complexity of our estimator and provide a high probability non-asymptotic bound for estimation error of all parameters under a geometric condition. We show that the recovery of the common parameter benefits from \emph{all} of the pooled samples. We propose an iterative estimation algorithm with a geometric convergence rate and supplement our theoretical analysis with experiments on synthetic data. Overall, we present a first thorough statistical and computational analysis of inference in the data-sharing model.
| null |
https://arxiv.org/abs/1806.04047v4
|
https://arxiv.org/pdf/1806.04047v4.pdf
| null |
[
"Amir Asiaee",
"Samet Oymak",
"Kevin R. Coombes",
"Arindam Banerjee"
] |
[
"Multi-Task Learning",
"Vocal Bursts Intensity Prediction"
] | 2018-06-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/near-lossless-deep-feature-compression-for
|
1804.09963
| null | null |
Near-Lossless Deep Feature Compression for Collaborative Intelligence
|
Collaborative intelligence is a new paradigm for efficient deployment of deep
neural networks across the mobile-cloud infrastructure. By dividing the network
between the mobile and the cloud, it is possible to distribute the
computational workload such that the overall energy and/or latency of the
system is minimized. However, this necessitates sending deep feature data from
the mobile to the cloud in order to perform inference. In this work, we examine
the differences between the deep feature data and natural image data, and
propose a simple and effective near-lossless deep feature compressor. The
proposed method achieves up to 5% bit rate reduction compared to HEVC-Intra and
even more against other popular image codecs. Finally, we suggest an approach
for reconstructing the input image from compressed deep features in the cloud,
that could serve to supplement the inference performed by the deep model.
| null |
http://arxiv.org/abs/1804.09963v2
|
http://arxiv.org/pdf/1804.09963v2.pdf
| null |
[
"Hyomin Choi",
"Ivan V. Bajic"
] |
[
"Feature Compression"
] | 2018-04-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/teaching-machines-to-code-neural-markup
|
1802.05415
| null | null |
Teaching Machines to Code: Neural Markup Generation with Visual Attention
|
We present a neural transducer model with visual attention that learns to
generate LaTeX markup of a real-world math formula given its image. Applying
sequence modeling and transduction techniques that have been very successful
across modalities such as natural language, image, handwriting, speech and
audio; we construct an image-to-markup model that learns to produce
syntactically and semantically correct LaTeX markup code over 150 words long
and achieves a BLEU score of 89%; improving upon the previous state-of-art for
the Im2Latex problem. We also demonstrate with heat-map visualization how
attention helps in interpreting the model and can pinpoint (detect and
localize) symbols on the image accurately despite having been trained without
any bounding box data.
|
We present a neural transducer model with visual attention that learns to generate LaTeX markup of a real-world math formula given its image.
|
http://arxiv.org/abs/1802.05415v2
|
http://arxiv.org/pdf/1802.05415v2.pdf
| null |
[
"Sumeet S. Singh"
] |
[
"Math",
"Optical Character Recognition (OCR)"
] | 2018-02-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/supervised-fuzzy-partitioning
|
1806.06124
| null | null |
Supervised Fuzzy Partitioning
|
Centroid-based methods including k-means and fuzzy c-means are known as effective and easy-to-implement approaches to clustering purposes in many applications. However, these algorithms cannot be directly applied to supervised tasks. This paper thus presents a generative model extending the centroid-based clustering approach to be applicable to classification and regression tasks. Given an arbitrary loss function, the proposed approach, termed Supervised Fuzzy Partitioning (SFP), incorporates labels information into its objective function through a surrogate term penalizing the empirical risk. Entropy-based regularization is also employed to fuzzify the partition and to weight features, enabling the method to capture more complex patterns, identify significant features, and yield better performance facing high-dimensional data. An iterative algorithm based on block coordinate descent scheme is formulated to efficiently find a local optimum. Extensive classification experiments on synthetic, real-world, and high-dimensional datasets demonstrate that the predictive performance of SFP is competitive with state-of-the-art algorithms such as SVM and random forest. SFP has a major advantage over such methods, in that it not only leads to a flexible, nonlinear model but also can exploit any convex loss function in the training phase without compromising computational efficiency.
| null |
https://arxiv.org/abs/1806.06124v5
|
https://arxiv.org/pdf/1806.06124v5.pdf
| null |
[
"Pooya Ashtari",
"Fateme Nateghi Haredasht",
"Hamid Beigy"
] |
[
"Clustering",
"Computational Efficiency",
"General Classification"
] | 2018-06-15T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **Support Vector Machine**, or **SVM**, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs to high-dimensional feature spaces. SVMs construct a hyper-plane or set of hyper-planes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyper-plane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. The figure to the right shows the decision function for a linearly separable problem, with three samples on the margin boundaries, called “support vectors”. \r\n\r\nSource: [scikit-learn](https://scikit-learn.org/stable/modules/svm.html)",
"full_name": "Support Vector Machine",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "SVM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/on-the-relationship-between-data-efficiency
|
1806.06123
| null | null |
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
|
While active learning offers potential cost savings, the actual data
efficiency---the reduction in amount of labeled data needed to obtain the same
error rate---observed in practice is mixed. This paper poses a basic question:
when is active learning actually helpful? We provide an answer for logistic
regression with the popular active learning algorithm, uncertainty sampling.
Empirically, on 21 datasets from OpenML, we find a strong inverse correlation
between data efficiency and the error rate of the final classifier.
Theoretically, we show that for a variant of uncertainty sampling, the
asymptotic data efficiency is within a constant factor of the inverse error
rate of the limiting classifier.
|
While active learning offers potential cost savings, the actual data efficiency---the reduction in amount of labeled data needed to obtain the same error rate---observed in practice is mixed.
|
http://arxiv.org/abs/1806.06123v1
|
http://arxiv.org/pdf/1806.06123v1.pdf
|
ICML 2018 7
|
[
"Stephen Mussmann",
"Percy Liang"
] |
[
"Active Learning",
"regression"
] | 2018-06-15T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2390
|
http://proceedings.mlr.press/v80/mussmann18a/mussmann18a.pdf
|
on-the-relationship-between-data-efficiency-1
| null |
[] |
https://paperswithcode.com/paper/fairness-under-composition
|
1806.06122
| null | null |
Fairness Under Composition
|
Algorithmic fairness, and in particular the fairness of scoring and
classification algorithms, has become a topic of increasing social concern and
has recently witnessed an explosion of research in theoretical computer
science, machine learning, statistics, the social sciences, and law. Much of
the literature considers the case of a single classifier (or scoring function)
used once, in isolation. In this work, we initiate the study of the fairness
properties of systems composed of algorithms that are fair in isolation; that
is, we study fairness under composition. We identify pitfalls of naive
composition and give general constructions for fair composition, demonstrating
both that classifiers that are fair in isolation do not necessarily compose
into fair systems and also that seemingly unfair components may be carefully
combined to construct fair systems. We focus primarily on the individual
fairness setting proposed in [Dwork, Hardt, Pitassi, Reingold, Zemel, 2011],
but also extend our results to a large class of group fairness definitions
popular in the recent literature, exhibiting several cases in which group
fairness definitions give misleading signals under composition.
| null |
http://arxiv.org/abs/1806.06122v2
|
http://arxiv.org/pdf/1806.06122v2.pdf
| null |
[
"Cynthia Dwork",
"Christina Ilvento"
] |
[
"Fairness"
] | 2018-06-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/stochastic-wavenet-a-generative-latent
|
1806.06116
| null | null |
Stochastic WaveNet: A Generative Latent Variable Model for Sequential Data
|
How to model distribution of sequential data, including but not limited to
speech and human motions, is an important ongoing research problem. It has been
demonstrated that model capacity can be significantly enhanced by introducing
stochastic latent variables in the hidden states of recurrent neural networks.
Simultaneously, WaveNet, equipped with dilated convolutions, achieves
astonishing empirical performance in natural speech generation task. In this
paper, we combine the ideas from both stochastic latent variables and dilated
convolutions, and propose a new architecture to model sequential data, termed
as Stochastic WaveNet, where stochastic latent variables are injected into the
WaveNet structure. We argue that Stochastic WaveNet enjoys powerful
distribution modeling capacity and the advantage of parallel training from
dilated convolutions. In order to efficiently infer the posterior distribution
of the latent variables, a novel inference network structure is designed based
on the characteristics of WaveNet architecture. State-of-the-art performances
on benchmark datasets are obtained by Stochastic WaveNet on natural speech
modeling and high quality human handwriting samples can be generated as well.
|
In this paper, we combine the ideas from both stochastic latent variables and dilated convolutions, and propose a new architecture to model sequential data, termed as Stochastic WaveNet, where stochastic latent variables are injected into the WaveNet structure.
|
http://arxiv.org/abs/1806.06116v1
|
http://arxiv.org/pdf/1806.06116v1.pdf
| null |
[
"Guokun Lai",
"Bohan Li",
"Guoqing Zheng",
"Yiming Yang"
] |
[] | 2018-06-15T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Mixture of Logistic Distributions (MoL)** is a type of output function, and an alternative to a [softmax](https://paperswithcode.com/method/softmax) layer. Discretized logistic mixture likelihood is used in [PixelCNN](https://paperswithcode.com/method/pixelcnn)++ and [WaveNet](https://paperswithcode.com/method/wavenet) to predict discrete values.\r\n\r\nImage Credit: [Hao Gao](https://medium.com/@smallfishbigsea/an-explanation-of-discretized-logistic-mixture-likelihood-bdfe531751f0)",
"full_name": "Mixture of Logistic Distributions",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Mixture of Logistic Distributions",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "A **Dilated Causal Convolution** is a [causal convolution](https://paperswithcode.com/method/causal-convolution) where the filter is applied over an area larger than its length by skipping input values with a certain step. A dilated causal [convolution](https://paperswithcode.com/method/convolution) effectively allows the network to have very large receptive fields with just a few layers.",
"full_name": "Dilated Causal Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Temporal Convolutions",
"parent": null
},
"name": "Dilated Causal Convolution",
"source_title": "WaveNet: A Generative Model for Raw Audio",
"source_url": "http://arxiv.org/abs/1609.03499v2"
},
{
"code_snippet_url": null,
"description": "**WaveNet** is an audio generative model based on the [PixelCNN](https://paperswithcode.com/method/pixelcnn) architecture. In order to deal with long-range temporal dependencies needed for raw audio generation, architectures are developed based on dilated causal convolutions, which exhibit very large receptive fields.\r\n\r\nThe joint probability of a waveform $\\vec{x} = \\{ x_1, \\dots, x_T \\}$ is factorised as a product of conditional probabilities as follows:\r\n\r\n$$p\\left(\\vec{x}\\right) = \\prod_{t=1}^{T} p\\left(x_t \\mid x_1, \\dots ,x_{t-1}\\right)$$\r\n\r\nEach audio sample $x_t$ is therefore conditioned on the samples at all previous timesteps.",
"full_name": "WaveNet",
"introduced_year": 2000,
"main_collection": {
"area": "Audio",
"description": "",
"name": "Generative Audio Models",
"parent": null
},
"name": "WaveNet",
"source_title": "WaveNet: A Generative Model for Raw Audio",
"source_url": "http://arxiv.org/abs/1609.03499v2"
}
] |
https://paperswithcode.com/paper/mv-yolo-motion-vector-aided-tracking-by
|
1805.00107
| null | null |
MV-YOLO: Motion Vector-aided Tracking by Semantic Object Detection
|
Object tracking is the cornerstone of many visual analytics systems. While
considerable progress has been made in this area in recent years, robust,
efficient, and accurate tracking in real-world video remains a challenge. In
this paper, we present a hybrid tracker that leverages motion information from
the compressed video stream and a general-purpose semantic object detector
acting on decoded frames to construct a fast and efficient tracking engine. The
proposed approach is compared with several well-known recent trackers on the
OTB tracking dataset. The results indicate advantages of the proposed method in
terms of speed and/or accuracy.Other desirable features of the proposed method
are its simplicity and deployment efficiency, which stems from the fact that it
reuses the resources and information that may already exist in the system for
other reasons.
| null |
http://arxiv.org/abs/1805.00107v2
|
http://arxiv.org/pdf/1805.00107v2.pdf
| null |
[
"Saeed Ranjbar Alvar",
"Ivan V. Bajić"
] |
[
"Object",
"object-detection",
"Object Detection",
"Object Tracking"
] | 2018-04-30T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/non-negative-networks-against-adversarial
|
1806.06108
| null | null |
Non-Negative Networks Against Adversarial Attacks
|
Adversarial attacks against neural networks are a problem of considerable
importance, for which effective defenses are not yet readily available. We make
progress toward this problem by showing that non-negative weight constraints
can be used to improve resistance in specific scenarios. In particular, we show
that they can provide an effective defense for binary classification problems
with asymmetric cost, such as malware or spam detection. We also show the
potential for non-negativity to be helpful to non-binary problems by applying
it to image classification.
|
Adversarial attacks against neural networks are a problem of considerable importance, for which effective defenses are not yet readily available.
|
http://arxiv.org/abs/1806.06108v2
|
http://arxiv.org/pdf/1806.06108v2.pdf
| null |
[
"William Fleshman",
"Edward Raff",
"Jared Sylvester",
"Steven Forsyth",
"Mark McLean"
] |
[
"Binary Classification",
"Classification",
"General Classification",
"image-classification",
"Image Classification",
"Spam detection"
] | 2018-06-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-limits-of-post-selection-generalization
|
1806.06100
| null | null |
The Limits of Post-Selection Generalization
|
While statistics and machine learning offers numerous methods for ensuring
generalization, these methods often fail in the presence of adaptivity---the
common practice in which the choice of analysis depends on previous
interactions with the same dataset. A recent line of work has introduced
powerful, general purpose algorithms that ensure post hoc generalization (also
called robust or post-selection generalization), which says that, given the
output of the algorithm, it is hard to find any statistic for which the data
differs significantly from the population it came from.
In this work we show several limitations on the power of algorithms
satisfying post hoc generalization. First, we show a tight lower bound on the
error of any algorithm that satisfies post hoc generalization and answers
adaptively chosen statistical queries, showing a strong barrier to progress in
post selection data analysis. Second, we show that post hoc generalization is
not closed under composition, despite many examples of such algorithms
exhibiting strong composition properties.
| null |
http://arxiv.org/abs/1806.06100v1
|
http://arxiv.org/pdf/1806.06100v1.pdf
|
NeurIPS 2018 12
|
[
"Kobbi Nissim",
"Adam Smith",
"Thomas Steinke",
"Uri Stemmer",
"Jonathan Ullman"
] |
[] | 2018-06-15T00:00:00 |
http://papers.nips.cc/paper/7876-the-limits-of-post-selection-generalization
|
http://papers.nips.cc/paper/7876-the-limits-of-post-selection-generalization.pdf
|
the-limits-of-post-selection-generalization-1
| null |
[] |
https://paperswithcode.com/paper/unsupervised-training-for-3d-morphable-model
|
1806.06098
| null | null |
Unsupervised Training for 3D Morphable Model Regression
|
We present a method for training a regression network from image pixels to 3D
morphable model coordinates using only unlabeled photographs. The training loss
is based on features from a facial recognition network, computed on-the-fly by
rendering the predicted faces with a differentiable renderer. To make training
from features feasible and avoid network fooling effects, we introduce three
objectives: a batch distribution loss that encourages the output distribution
to match the distribution of the morphable model, a loopback loss that ensures
the network can correctly reinterpret its own output, and a multi-view identity
loss that compares the features of the predicted 3D face and the input
photograph from multiple viewing angles. We train a regression network using
these objectives, a set of unlabeled photographs, and the morphable model
itself, and demonstrate state-of-the-art results.
|
We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results.
|
http://arxiv.org/abs/1806.06098v1
|
http://arxiv.org/pdf/1806.06098v1.pdf
|
CVPR 2018 6
|
[
"Kyle Genova",
"Forrester Cole",
"Aaron Maschinot",
"Aaron Sarna",
"Daniel Vlasic",
"William T. Freeman"
] |
[
"3D Face Reconstruction",
"model",
"regression"
] | 2018-06-15T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Genova_Unsupervised_Training_for_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Genova_Unsupervised_Training_for_CVPR_2018_paper.pdf
|
unsupervised-training-for-3d-morphable-model-1
| null |
[] |
https://paperswithcode.com/paper/integrating-human-provided-information-into
|
1803.00119
| null | null |
Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization
|
In partially observed environments, it can be useful for a human to provide
the robot with declarative information that represents probabilistic relational
constraints on properties of objects in the world, augmenting the robot's
sensory observations. For instance, a robot tasked with a search-and-rescue
mission may be informed by the human that two victims are probably in the same
room. An important question arises: how should we represent the robot's
internal knowledge so that this information is correctly processed and combined
with raw sensory information? In this paper, we provide an efficient belief
state representation that dynamically selects an appropriate factoring,
combining aspects of the belief when they are correlated through information
and separating them when they are not. This strategy works in open domains, in
which the set of possible objects is not known in advance, and provides
significant improvements in inference time over a static factoring, leading to
more efficient planning for complex partially observed tasks. We validate our
approach experimentally in two open-domain planning problems: a 2D discrete
gridworld task and a 3D continuous cooking task. A supplementary video can be
found at http://tinyurl.com/chitnis-iros-18.
| null |
http://arxiv.org/abs/1803.00119v4
|
http://arxiv.org/pdf/1803.00119v4.pdf
| null |
[
"Rohan Chitnis",
"Leslie Pack Kaelbling",
"Tomás Lozano-Pérez"
] |
[] | 2018-02-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/crime-event-embedding-with-unsupervised
|
1806.06095
| null | null |
Crime Event Embedding with Unsupervised Feature Selection
|
We present a novel event embedding algorithm for crime data that can jointly
capture time, location, and the complex free-text component of each event. The
embedding is achieved by regularized Restricted Boltzmann Machines (RBMs), and
we introduce a new way to regularize by imposing a $\ell_1$ penalty on the
conditional distributions of the observed variables of RBMs. This choice of
regularization performs feature selection and it also leads to efficient
computation since the gradient can be computed in a closed form. The feature
selection forces embedding to be based on the most important keywords, which
captures the common modus operandi (M. O.) in crime series. Using numerical
experiments on a large-scale crime dataset, we show that our regularized RBMs
can achieve better event embedding and the selected features are highly
interpretable from human understanding.
| null |
http://arxiv.org/abs/1806.06095v4
|
http://arxiv.org/pdf/1806.06095v4.pdf
| null |
[
"Shixiang Zhu",
"Yao Xie"
] |
[
"feature selection"
] | 2018-06-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/training-vaes-under-structured-residuals
|
1804.01050
| null | null |
Training VAEs Under Structured Residuals
|
Variational auto-encoders (VAEs) are a popular and powerful deep generative
model. Previous works on VAEs have assumed a factorized likelihood model,
whereby the output uncertainty of each pixel is assumed to be independent. This
approximation is clearly limited as demonstrated by observing a residual image
from a VAE reconstruction, which often possess a high level of structure. This
paper demonstrates a novel scheme to incorporate a structured Gaussian
likelihood prediction network within the VAE that allows the residual
correlations to be modeled. Our novel architecture, with minimal increase in
complexity, incorporates the covariance matrix prediction within the VAE. We
also propose a new mechanism for allowing structured uncertainty on color
images. Furthermore, we provide a scheme for effectively training this model,
and include some suggestions for improving performance in terms of efficiency
or modeling longer range correlations.
|
This paper demonstrates a novel scheme to incorporate a structured Gaussian likelihood prediction network within the VAE that allows the residual correlations to be modeled.
|
http://arxiv.org/abs/1804.01050v3
|
http://arxiv.org/pdf/1804.01050v3.pdf
| null |
[
"Garoe Dorta",
"Sara Vicente",
"Lourdes Agapito",
"Neill D. F. Campbell",
"Ivor Simpson"
] |
[] | 2018-04-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
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"full_name": "USD Coin 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": "USD Coin Customer Service Number +1-833-534-1729",
"source_title": "Auto-Encoding Variational Bayes",
"source_url": "http://arxiv.org/abs/1312.6114v10"
}
] |
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