pwc-archive/papers-with-abstracts huggingface dataset:
A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model
Authors: Ev Zisselman, Jeremias Sulam, Michael Elad
Date: 2018-11-01 00:00:00
Tasks: Image Inpainting, Multi Focus Image Fusion

The Convolutional Sparse Coding (CSC) model has recently gained considerable
traction in the signal and image processing communities. By providing a global,
yet tractable, model that operates on the whole image, the CSC was shown to
overcome several limitations of the patch-based sparse model while achieving
superior performance in various applications. Contemporary methods for pursuit
and learning the CSC dictionary often rely on the Alternating Direction Method
of Multipliers (ADMM) in the Fourier domain for the computational convenience
of convolutions, while ignoring the local characterizations of the image. A
recent work by Papyan et al. suggested the SBDL algorithm for the CSC, while
operating locally on image patches. SBDL demonstrates better performance
compared to the Fourier-based methods, albeit still relying on the ADMM. In
this work we maintain the localized strategy of the SBDL, while proposing a new
and much simpler approach based on the Block Coordinate Descent algorithm -
this method is termed Local Block Coordinate Descent (LoBCoD). Furthermore, we
introduce a novel stochastic gradient descent version of LoBCoD for training
the convolutional filters. The Stochastic-LoBCoD leverages the benefits of
online learning, while being applicable to a single training image. We
demonstrate the advantages of the proposed algorithms for image inpainting and
multi-focus image fusion, achieving state-of-the-art results.
Critical initialisation for deep signal propagation in noisy rectifier neural networks
Authors: Arnu Pretorius, Elan van Biljon, Steve Kroon, Herman Kamper
Date: 2018-11-01 00:00:00
Proceeding: NeurIPS 2018 12

Stochastic regularisation is an important weapon in the arsenal of a deep
learning practitioner. However, despite recent theoretical advances, our
understanding of how noise influences signal propagation in deep neural
networks remains limited. By extending recent work based on mean field theory,
we develop a new framework for signal propagation in stochastic regularised
neural networks. Our noisy signal propagation theory can incorporate several
common noise distributions, including additive and multiplicative Gaussian
noise as well as dropout. We use this framework to investigate initialisation
strategies for noisy ReLU networks. We show that no critical initialisation
strategy exists using additive noise, with signal propagation exploding
regardless of the selected noise distribution. For multiplicative noise (e.g.
dropout), we identify alternative critical initialisation strategies that
depend on the second moment of the noise distribution. Simulations and
experiments on real-world data confirm that our proposed initialisation is able
to stably propagate signals in deep networks, while using an initialisation
disregarding noise fails to do so. Furthermore, we analyse correlation dynamics
between inputs. Stronger noise regularisation is shown to reduce the depth to
which discriminatory information about the inputs to a noisy ReLU network is
able to propagate, even when initialised at criticality. We support our
theoretical predictions for these trainable depths with simulations, as well as
with experiments on MNIST and CIFAR-10
Towards Linear Time Neural Machine Translation with Capsule Networks
Authors: Mingxuan Wang, Jun Xie, Zhixing Tan, Jinsong Su, Deyi Xiong, Lei LI
Date: 2018-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Machine Translation, NMT, Sentence, Translation

In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as \textsc{CapsNMT}. \textsc{CapsNMT} uses an aggregation mechanism to map the source sentence into a matrix with pre-determined size, and then applys a deep LSTM network to decode the target sequence from the source representation. Unlike the previous work \cite{sutskever2014sequence} to store the source sentence with a passive and bottom-up way, the dynamic routing policy encodes the source sentence with an iterative process to decide the credit attribution between nodes from lower and higher layers. \textsc{CapsNMT} has two core properties: it runs in time that is linear in the length of the sequences and provides a more flexible way to select, represent and aggregates the part-whole information of the source sentence. On WMT14 English-German task and a larger WMT14 English-French task, \textsc{CapsNMT} achieves comparable results with the state-of-the-art NMT systems. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for sequence to sequence problems.
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy
Authors: Pengcheng Yang, Fuli Luo, Shuangzhi Wu, Jingjing Xu, Dong-dong Zhang, Xu sun
Date: 2018-11-01 00:00:00
Tasks: Cross-Lingual Word Embeddings, Density Estimation, Machine Translation, Transfer Learning, Translation, Word Embeddings

Cross-lingual word embeddings aim to capture common linguistic regularities
of different languages, which benefit various downstream tasks ranging from
machine translation to transfer learning. Recently, it has been shown that
these embeddings can be effectively learned by aligning two disjoint
monolingual vector spaces through a linear transformation (word mapping). In
this work, we focus on learning such a word mapping without any supervision
signal. Most previous work of this task adopts parametric metrics to measure
distribution differences, which typically requires a sophisticated alternate
optimization process, either in the form of \emph{minmax game} or intermediate
\emph{density estimation}. This alternate optimization process is relatively
hard and unstable. In order to avoid such sophisticated alternate optimization,
we propose to learn unsupervised word mapping by directly maximizing the mean
discrepancy between the distribution of transferred embedding and target
embedding. Extensive experimental results show that our proposed model
outperforms competitive baselines by a large margin.
Efficient Multi-Domain Dictionary Learning with GANs
Authors: Cho Ying Wu, Ulrich Neumann
Date: 2018-11-01 00:00:00
Tasks: Dictionary Learning, General Classification, Miscellaneous

In this paper, we propose the multi-domain dictionary learn- ing (MDDL) to
make dictionary learning-based classification more robust to data representing
in different domains. We use adversarial neural networks to generate data in
different styles, and collect all the generated data into a miscellaneous
dictionary. To tackle the dictionary learning with many sam- ples, we compute
the weighting matrix that compress the mis- cellaneous dictionary from
multi-sample per class to single sample per class. We show that the time
complexity solv- ing the proposed MDDL with weighting matrix is the same as
solving the dictionary with single sample per class. More- over, since the
weighting matrix could help the solver rely more on the training data, which
possibly lie in the same do- main with the testing data, the classification
could be more accurate.
Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition
Authors: Xiangbo Shu, Jinhui Tang, Guo-Jun Qi, Wei Liu, Jian Yang
Date: 2018-11-01 00:00:00
Tasks: Action Recognition, Human Interaction Recognition, Temporal Action Localization

In this paper, we aim to address the problem of human interaction recognition
in videos by exploring the long-term inter-related dynamics among multiple
persons. Recently, Long Short-Term Memory (LSTM) has become a popular choice to
model individual dynamic for single-person action recognition due to its
ability of capturing the temporal motion information in a range. However,
existing RNN models focus only on capturing the dynamics of human interaction
by simply combining all dynamics of individuals or modeling them as a whole.
Such models neglect the inter-related dynamics of how human interactions change
over time. To this end, we propose a novel Hierarchical Long Short-Term
Concurrent Memory (H-LSTCM) to model the long-term inter-related dynamics among
a group of persons for recognizing the human interactions. Specifically, we
first feed each person's static features into a Single-Person LSTM to learn the
single-person dynamic. Subsequently, the outputs of all Single-Person LSTM
units are fed into a novel Concurrent LSTM (Co-LSTM) unit, which mainly
consists of multiple sub-memory units, a new cell gate and a new co-memory
cell. In a Co-LSTM unit, each sub-memory unit stores individual motion
information, while this Co-LSTM unit selectively integrates and stores
inter-related motion information between multiple interacting persons from
multiple sub-memory units via the cell gate and co-memory cell, respectively.
Extensive experiments on four public datasets validate the effectiveness of the
proposed H-LSTCM by comparing against baseline and state-of-the-art methods.
Learning to Describe Phrases with Local and Global Contexts
Authors: Shonosuke Ishiwatari, Hiroaki Hayashi, Naoki Yoshinaga, Graham Neubig, Shoetsu Sato, Masashi Toyoda, Masaru Kitsuregawa
Date: 2018-11-01 00:00:00
Tasks: Decoder, Reading Comprehension

When reading a text, it is common to become stuck on unfamiliar words and
phrases, such as polysemous words with novel senses, rarely used idioms,
internet slang, or emerging entities. If we humans cannot figure out the
meaning of those expressions from the immediate local context, we consult
dictionaries for definitions or search documents or the web to find other
global context to help in interpretation. Can machines help us do this work?
Which type of context is more important for machines to solve the problem? To
answer these questions, we undertake a task of describing a given phrase in
natural language based on its local and global contexts. To solve this task, we
propose a neural description model that consists of two context encoders and a
description decoder. In contrast to the existing methods for non-standard
English explanation [Ni+ 2017] and definition generation [Noraset+ 2017;
Gadetsky+ 2018], our model appropriately takes important clues from both local
and global contexts. Experimental results on three existing datasets (including
WordNet, Oxford and Urban Dictionaries) and a dataset newly created from
Wikipedia demonstrate the effectiveness of our method over previous work.
ATM:Adversarial-neural Topic Model
Authors: Rui Wang, Deyu Zhou, Yulan He
Date: 2018-11-01 00:00:00
Tasks: Articles, Event Extraction, model, Topic Models

Topic models are widely used for thematic structure discovery in text. But traditional topic models often require dedicated inference procedures for specific tasks at hand. Also, they are not designed to generate word-level semantic representations. To address these limitations, we propose a topic modeling approach based on Generative Adversarial Nets (GANs), called Adversarial-neural Topic Model (ATM). The proposed ATM models topics with Dirichlet prior and employs a generator network to capture the semantic patterns among latent topics. Meanwhile, the generator could also produce word-level semantic representations. To illustrate the feasibility of porting ATM to tasks other than topic modeling, we apply ATM for open domain event extraction. Our experimental results on the two public corpora show that ATM generates more coherence topics, outperforming a number of competitive baselines. Moreover, ATM is able to extract meaningful events from news articles.
Multiple Kernel $k$-Means Clustering by Selecting Representative Kernels
Authors: Yaqiang Yao, Huanhuan Chen
Date: 2018-11-01 00:00:00
Tasks: Clustering

To cluster data that are not linearly separable in the original feature
space, $k$-means clustering was extended to the kernel version. However, the
performance of kernel $k$-means clustering largely depends on the choice of
kernel function. To mitigate this problem, multiple kernel learning has been
introduced into the $k$-means clustering to obtain an optimal kernel
combination for clustering. Despite the success of multiple kernel $k$-means
clustering in various scenarios, few of the existing work update the
combination coefficients based on the diversity of kernels, which leads to the
result that the selected kernels contain high redundancy and would degrade the
clustering performance and efficiency. In this paper, we propose a simple but
efficient strategy that selects a diverse subset from the pre-specified kernels
as the representative kernels, and then incorporate the subset selection
process into the framework of multiple $k$-means clustering. The representative
kernels can be indicated as the significant combination weights. Due to the
non-convexity of the obtained objective function, we develop an alternating
minimization method to optimize the combination coefficients of the selected
kernels and the cluster membership alternatively. We evaluate the proposed
approach on several benchmark and real-world datasets. The experimental results
demonstrate the competitiveness of our approach in comparison with the
state-of-the-art methods.
Horizon: Facebook's Open Source Applied Reinforcement Learning Platform
Authors: Jason Gauci, Edoardo Conti, Yitao Liang, Kittipat Virochsiri, Yuchen He, Zachary Kaden, Vivek Narayanan, Xiaohui Ye, Zhengxing Chen, Scott Fujimoto
Date: 2018-11-01 00:00:00
Tasks: counterfactual, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we present Horizon, Facebook's open source applied reinforcement learning (RL) platform. Horizon is an end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don't run in a simulator. Unlike other RL platforms, which are often designed for fast prototyping and experimentation, Horizon is designed with production use cases as top of mind. The platform contains workflows to train popular deep RL algorithms and includes data preprocessing, feature transformation, distributed training, counterfactual policy evaluation, optimized serving, and a model-based data understanding tool. We also showcase and describe real examples where reinforcement learning models trained with Horizon significantly outperformed and replaced supervised learning systems at Facebook.
Language-Independent Representor for Neural Machine Translation
Authors: Long Zhou, Yuchen Liu, Jiajun Zhang, Cheng-qing Zong, Guoping Huang
Date: 2018-11-01 00:00:00
Tasks: Decoder, Machine Translation, Multi-Task Learning, NMT, Sentence, Translation

Current Neural Machine Translation (NMT) employs a language-specific encoder
to represent the source sentence and adopts a language-specific decoder to
generate target translation. This language-dependent design leads to
large-scale network parameters and makes the duality of the parallel data
underutilized. To address the problem, we propose in this paper a
language-independent representor to replace the encoder and decoder by using
weight sharing. This shared representor can not only reduce large portion of
network parameters, but also facilitate us to fully explore the language
duality by jointly training source-to-target, target-to-source, left-to-right
and right-to-left translations within a multi-task learning framework.
Experiments show that our proposed framework can obtain significant
improvements over conventional NMT models on resource-rich and low-resource
translation tasks with only a quarter of parameters.
Skeleton-based Activity Recognition with Local Order Preserving Match of Linear Patches
Authors: Yaqiang Yao, Yan Liu, Huanhuan Chen
Date: 2018-11-01 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Human activity recognition has drawn considerable attention recently in the
field of computer vision due to the development of commodity depth cameras, by
which the human activity is represented as a sequence of 3D skeleton postures.
Assuming human body 3D joint locations of an activity lie on a manifold, the
problem of recognizing human activity is formulated as the computation of
activity manifold-manifold distance (AMMD). In this paper, we first design an
efficient division method to decompose a manifold into ordered continuous
maximal linear patches (CMLPs) that denote meaningful action snippets of the
action sequence. Then the CMLP is represented by its position (average value of
points) and the first principal component, which specify the major posture and
main evolving direction of an action snippet, respectively. Finally, we compute
the distance between CMLPs by taking both the posture and direction into
consideration. Based on these preparations, an intuitive distance measure that
preserves the local order of action snippets is proposed to compute AMMD. The
performance on two benchmark datasets demonstrates the effectiveness of the
proposed approach.
HMLasso: Lasso with High Missing Rate
Authors: Masaaki Takada, Hironori Fujisawa, Takeichiro Nishikawa
Date: 2018-11-01 00:00:00
Tasks: Missing Values, regression, Vocal Bursts Intensity Prediction

Sparse regression such as the Lasso has achieved great success in handling high-dimensional data. However, one of the biggest practical problems is that high-dimensional data often contain large amounts of missing values. Convex Conditioned Lasso (CoCoLasso) has been proposed for dealing with high-dimensional data with missing values, but it performs poorly when there are many missing values, so that the high missing rate problem has not been resolved. In this paper, we propose a novel Lasso-type regression method for high-dimensional data with high missing rates. We effectively incorporate mean imputed covariance, overcoming its inherent estimation bias. The result is an optimally weighted modification of CoCoLasso according to missing ratios. We theoretically and experimentally show that our proposed method is highly effective even when there are many missing values.
Hybrid Self-Attention Network for Machine Translation
Authors: Kaitao Song, Xu Tan, Furong Peng, Jianfeng Lu
Date: 2018-11-01 00:00:00
Tasks: Decoder, Machine Translation, NMT, Translation

The encoder-decoder is the typical framework for Neural Machine Translation
(NMT), and different structures have been developed for improving the
translation performance. Transformer is one of the most promising structures,
which can leverage the self-attention mechanism to capture the semantic
dependency from global view. However, it cannot distinguish the relative
position of different tokens very well, such as the tokens located at the left
or right of the current token, and cannot focus on the local information around
the current token either. To alleviate these problems, we propose a novel
attention mechanism named Hybrid Self-Attention Network (HySAN) which
accommodates some specific-designed masks for self-attention network to extract
various semantic, such as the global/local information, the left/right part
context. Finally, a squeeze gate is introduced to combine different kinds of
SANs for fusion. Experimental results on three machine translation tasks show
that our proposed framework outperforms the Transformer baseline significantly
and achieves superior results over state-of-the-art NMT systems.
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
Authors: Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, Yi Yang
Date: 2018-11-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: image-classification, Image Classification

Previous works utilized ''smaller-norm-less-important'' criterion to prune filters with smaller norm values in a convolutional neural network. In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small. To solve this problem, we propose a novel filter pruning method, namely Filter Pruning via Geometric Median (FPGM), to compress the model regardless of those two requirements. Unlike previous methods, FPGM compresses CNN models by pruning filters with redundancy, rather than those with ''relatively less'' importance. When applied to two image classification benchmarks, our method validates its usefulness and strengths. Notably, on CIFAR-10, FPGM reduces more than 52% FLOPs on ResNet-110 with even 2.69% relative accuracy improvement. Moreover, on ILSVRC-2012, FPGM reduces more than 42% FLOPs on ResNet-101 without top-5 accuracy drop, which has advanced the state-of-the-art. Code is publicly available on GitHub: https://github.com/he-y/filter-pruning-geometric-median
Examining Performance of Sketch-to-Image Translation Models with Multiclass Automatically Generated Paired Training Data
Authors: Dichao Hu
Date: 2018-11-01 00:00:00
Tasks: Decoder, Sketch-to-Image Translation, Translation

Image translation is a computer vision task that involves translating one
representation of the scene into another. Various approaches have been proposed
and achieved highly desirable results. Nevertheless, its accomplishment
requires abundant paired training data which are expensive to acquire.
Therefore, models for translation are usually trained on a set of paired
training data which are carefully and laboriously designed. Our work is focused
on learning through automatically generated paired data. We propose a method to
generate fake sketches from images using an adversarial network and then pair
the images with corresponding fake sketches to form large-scale multi-class
paired training data for training a sketch-to-image translation model. Our
model is an encoder-decoder architecture where the encoder generates fake
sketches from images and the decoder performs sketch-to-image translation.
Qualitative results show that the encoder can be used for generating
large-scale multi-class paired data under low supervision. Our current dataset
now contains 61255 image and (fake) sketch pairs from 256 different categories.
These figures can be greatly increased in the future thanks to our weak
reliance on manually labeled data.
FNNC: Achieving Fairness through Neural Networks
Authors: Padala Manisha, Sujit Gujar
Date: 2018-11-01 00:00:00
Tasks: Classification, Decision Making, Fairness, General Classification

In classification models fairness can be ensured by solving a constrained optimization problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and Equalized Odds, which are non-decomposable and non-convex. Researchers define convex surrogates of the constraints and then apply convex optimization frameworks to obtain fair classifiers. Surrogates serve only as an upper bound to the actual constraints, and convexifying fairness constraints might be challenging. We propose a neural network-based framework, \emph{FNNC}, to achieve fairness while maintaining high accuracy in classification. The above fairness constraints are included in the loss using Lagrangian multipliers. We prove bounds on generalization errors for the constrained losses which asymptotically go to zero. The network is optimized using two-step mini-batch stochastic gradient descent. Our experiments show that FNNC performs as good as the state of the art, if not better. The experimental evidence supplements our theoretical guarantees. In summary, we have an automated solution to achieve fairness in classification, which is easily extendable to many fairness constraints.