pwc-archive/papers-with-abstracts huggingface dataset:
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
Authors: Pin-Yu Chen, huan zhang, Yash Sharma, Jin-Feng Yi, Cho-Jui Hsieh
Date: 2017-08-14 00:00:00
Tasks: Adversarial Attack, Adversarial Defense, Autonomous Driving, Dimensionality Reduction, image-classification, Image Classification

Deep neural networks (DNNs) are one of the most prominent technologies of our
time, as they achieve state-of-the-art performance in many machine learning
tasks, including but not limited to image classification, text mining, and
speech processing. However, recent research on DNNs has indicated
ever-increasing concern on the robustness to adversarial examples, especially
for security-critical tasks such as traffic sign identification for autonomous
driving. Studies have unveiled the vulnerability of a well-trained DNN by
demonstrating the ability of generating barely noticeable (to both human and
machines) adversarial images that lead to misclassification. Furthermore,
researchers have shown that these adversarial images are highly transferable by
simply training and attacking a substitute model built upon the target model,
known as a black-box attack to DNNs.
  Similar to the setting of training substitute models, in this paper we
propose an effective black-box attack that also only has access to the input
(images) and the output (confidence scores) of a targeted DNN. However,
different from leveraging attack transferability from substitute models, we
propose zeroth order optimization (ZOO) based attacks to directly estimate the
gradients of the targeted DNN for generating adversarial examples. We use
zeroth order stochastic coordinate descent along with dimension reduction,
hierarchical attack and importance sampling techniques to efficiently attack
black-box models. By exploiting zeroth order optimization, improved attacks to
the targeted DNN can be accomplished, sparing the need for training substitute
models and avoiding the loss in attack transferability. Experimental results on
MNIST, CIFAR10 and ImageNet show that the proposed ZOO attack is as effective
as the state-of-the-art white-box attack and significantly outperforms existing
black-box attacks via substitute models.
Out-of-focus Blur: Image De-blurring
Authors: Yuzhen Lu
Date: 2017-10-02 00:00:00

Image de-blurring is important in many cases of imaging a real scene or
object by a camera. This project focuses on de-blurring an image distorted by
an out-of-focus blur through a simulation study. A pseudo-inverse filter is
first explored but it fails because of severe noise amplification. Then
Tikhonov regularization methods are employed, which produce greatly improved
results compared to the pseudo-inverse filter. In Tikhonov regularization, the
choice of the regularization parameter plays a critical rule in obtaining a
high-quality image, and the regularized solutions possess a semi-convergence
property. The best result, with the relative restoration error of 8.49%, is
achieved when the prescribed discrepancy principle is used to decide an optimal
value. Furthermore, an iterative method, Conjugated Gradient, is employed for
image de-blurring, which is fast in computation and leads to an even better
result with the relative restoration error of 8.22%. The number of iteration in
CG acts as a regularization parameter, and the iterates have a semi-convergence
property as well.
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning
Authors: Stefan Elfwing, Eiji Uchibe, Kenji Doya
Date: 2017-02-10 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, neural networks have enjoyed a renaissance as function
approximators in reinforcement learning. Two decades after Tesauro's TD-Gammon
achieved near top-level human performance in backgammon, the deep reinforcement
learning algorithm DQN achieved human-level performance in many Atari 2600
games. The purpose of this study is twofold. First, we propose two activation
functions for neural network function approximation in reinforcement learning:
the sigmoid-weighted linear unit (SiLU) and its derivative function (dSiLU).
The activation of the SiLU is computed by the sigmoid function multiplied by
its input. Second, we suggest that the more traditional approach of using
on-policy learning with eligibility traces, instead of experience replay, and
softmax action selection with simple annealing can be competitive with DQN,
without the need for a separate target network. We validate our proposed
approach by, first, achieving new state-of-the-art results in both stochastic
SZ-Tetris and Tetris with a small 10$\times$10 board, using TD($\lambda$)
learning and shallow dSiLU network agents, and, then, by outperforming DQN in
the Atari 2600 domain by using a deep Sarsa($\lambda$) agent with SiLU and
dSiLU hidden units.
A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement
Authors: Zhenqiang Ying, Ge Li, Wen Gao
Date: 2017-11-02 00:00:00
Tasks: Image Enhancement, Low-Light Image Enhancement

Low-light images are not conducive to human observation and computer vision
algorithms due to their low visibility. Although many image enhancement
techniques have been proposed to solve this problem, existing methods
inevitably introduce contrast under- and over-enhancement. Inspired by human
visual system, we design a multi-exposure fusion framework for low-light image
enhancement. Based on the framework, we propose a dual-exposure fusion
algorithm to provide an accurate contrast and lightness enhancement.
Specifically, we first design the weight matrix for image fusion using
illumination estimation techniques. Then we introduce our camera response model
to synthesize multi-exposure images. Next, we find the best exposure ratio so
that the synthetic image is well-exposed in the regions where the original
image is under-exposed. Finally, the enhanced result is obtained by fusing the
input image and the synthetic image according to the weight matrix. Experiments
show that our method can obtain results with less contrast and lightness
distortion compared to that of several state-of-the-art methods.
Dilated Recurrent Neural Networks
Authors: Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael Witbrock, Mark Hasegawa-Johnson, Thomas S. Huang
Date: 2017-10-05 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Sequential Image Classification

Learning with recurrent neural networks (RNNs) on long sequences is a
notoriously difficult task. There are three major challenges: 1) complex
dependencies, 2) vanishing and exploding gradients, and 3) efficient
parallelization. In this paper, we introduce a simple yet effective RNN
connection structure, the DilatedRNN, which simultaneously tackles all of these
challenges. The proposed architecture is characterized by multi-resolution
dilated recurrent skip connections and can be combined flexibly with diverse
RNN cells. Moreover, the DilatedRNN reduces the number of parameters needed and
enhances training efficiency significantly, while matching state-of-the-art
performance (even with standard RNN cells) in tasks involving very long-term
dependencies. To provide a theory-based quantification of the architecture's
advantages, we introduce a memory capacity measure, the mean recurrent length,
which is more suitable for RNNs with long skip connections than existing
measures. We rigorously prove the advantages of the DilatedRNN over other
recurrent neural architectures. The code for our method is publicly available
at https://github.com/code-terminator/DilatedRNN
Deep Learning from Noisy Image Labels with Quality Embedding
Authors: Jiangchao Yao, Jiajie Wang, Ivor Tsang, Ya zhang, Jun Sun, Chengqi Zhang, Rui Zhang
Date: 2017-11-02 00:00:00
Tasks: Deep Learning

There is an emerging trend to leverage noisy image datasets in many visual
recognition tasks. However, the label noise among the datasets severely
degenerates the \mbox{performance of deep} learning approaches. Recently, one
mainstream is to introduce the latent label to handle label noise, which has
shown promising improvement in the network designs. Nevertheless, the mismatch
between latent labels and noisy labels still affects the predictions in such
methods. To address this issue, we propose a quality embedding model, which
explicitly introduces a quality variable to represent the trustworthiness of
noisy labels. Our key idea is to identify the mismatch between the latent and
noisy labels by embedding the quality variables into different subspaces, which
effectively minimizes the noise effect. At the same time, the high-quality
labels is still able to be applied for training. To instantiate the model, we
further propose a Contrastive-Additive Noise network (CAN), which consists of
two important layers: (1) the contrastive layer estimates the quality variable
in the embedding space to reduce noise effect; and (2) the additive layer
aggregates the prior predictions and noisy labels as the posterior to train the
classifier. Moreover, to tackle the optimization difficulty, we deduce an SGD
algorithm with the reparameterization tricks, which makes our method scalable
to big data. We conduct the experimental evaluation of the proposed method over
a range of noisy image datasets. Comprehensive results have demonstrated CAN
outperforms the state-of-the-art deep learning approaches.
Random Subspace Two-dimensional LDA for Face Recognition
Authors: Garrett Bingham
Date: 2017-11-02 00:00:00
Tasks: Face Recognition, MORPH, Vocal Bursts Valence Prediction

In this paper, a novel technique named random subspace two-dimensional LDA
(RS-2DLDA) is developed for face recognition. This approach offers a number of
improvements over the random subspace two-dimensional PCA (RS2DPCA) framework
introduced by Nguyen et al. [5]. Firstly, the eigenvectors from 2DLDA have more
discriminative power than those from 2DPCA, resulting in higher accuracy for
the RS-2DLDA method over RS-2DPCA. Various distance metrics are evaluated, and
a weighting scheme is developed to further boost accuracy. A series of
experiments on the MORPH-II and ORL datasets are conducted to demonstrate the
effectiveness of this approach.
Anomaly Detection on Graph Time Series
Authors: Daniel Hsu
Date: 2017-08-09 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis, Variational Inference

In this paper, we use variational recurrent neural network to investigate the anomaly detection problem on graph time series. The temporal correlation is modeled by the combination of recurrent neural network (RNN) and variational inference (VI), while the spatial information is captured by the graph convolutional network. In order to incorporate external factors, we use feature extractor to augment the transition of latent variables, which can learn the influence of external factors. With the target function as accumulative ELBO, it is easy to extend this model to on-line method. The experimental study on traffic flow data shows the detection capability of the proposed method.
Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications
Authors: Shubham Jain, Marco Gruteser
Date: 2017-11-01 00:00:00
Tasks: Material Recognition, object-detection, Object Detection

As smartphone rooted distractions become commonplace, the lack of compelling
safety measures has led to a rise in the number of injuries to distracted
walkers. Various solutions address this problem by sensing a pedestrian's
walking environment. Existing camera-based approaches have been largely limited
to obstacle detection and other forms of object detection. Instead, we present
TerraFirma, an approach that performs material recognition on the pedestrian's
walking surface. We explore, first, how well commercial off-the-shelf
smartphone cameras can learn texture to distinguish among paving materials in
uncontrolled outdoor urban settings. Second, we aim at identifying when a
distracted user is about to enter the street, which can be used to support
safety functions such as warning the user to be cautious. To this end, we
gather a unique dataset of street/sidewalk imagery from a pedestrian's
perspective, that spans major cities like New York, Paris, and London. We
demonstrate that modern phone cameras can be enabled to distinguish materials
of walking surfaces in urban areas with more than 90% accuracy, and accurately
identify when pedestrians transition from sidewalk to street.
Alternating minimization and alternating descent over nonconvex sets
Authors: Wooseok Ha, Rina Foygel Barber
Date: 2017-09-13 00:00:00

We analyze the performance of alternating minimization for loss functions
optimized over two variables, where each variable may be restricted to lie in
some potentially nonconvex constraint set. This type of setting arises
naturally in high-dimensional statistics and signal processing, where the
variables often reflect different structures or components within the signals
being considered. Our analysis relies on the notion of local concavity
coefficients, which has been proposed in Barber and Ha to measure and quantify
the concavity of a general nonconvex set. Our results further reveal important
distinctions between alternating and non-alternating methods. Since computing
the alternating minimization steps may not be tractable for some problems, we
also consider an inexact version of the algorithm and provide a set of
sufficient conditions to ensure fast convergence of the inexact algorithms. We
demonstrate our framework on several examples, including low rank + sparse
decomposition and multitask regression, and provide numerical experiments to
validate our theoretical results.
Convergence Analysis of Two-layer Neural Networks with ReLU Activation
Authors: Yuanzhi Li, Yang Yuan
Date: 2017-05-28 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Vocal Bursts Valence Prediction

In recent years, stochastic gradient descent (SGD) based techniques has
become the standard tools for training neural networks. However, formal
theoretical understanding of why SGD can train neural networks in practice is
largely missing.
  In this paper, we make progress on understanding this mystery by providing a
convergence analysis for SGD on a rich subset of two-layer feedforward networks
with ReLU activations. This subset is characterized by a special structure
called "identity mapping". We prove that, if input follows from Gaussian
distribution, with standard $O(1/\sqrt{d})$ initialization of the weights, SGD
converges to the global minimum in polynomial number of steps. Unlike normal
vanilla networks, the "identity mapping" makes our network asymmetric and thus
the global minimum is unique. To complement our theory, we are also able to
show experimentally that multi-layer networks with this mapping have better
performance compared with normal vanilla networks.
  Our convergence theorem differs from traditional non-convex optimization
techniques. We show that SGD converges to optimal in "two phases": In phase I,
the gradient points to the wrong direction, however, a potential function $g$
gradually decreases. Then in phase II, SGD enters a nice one point convex
region and converges. We also show that the identity mapping is necessary for
convergence, as it moves the initial point to a better place for optimization.
Experiment verifies our claims.
Beautiful and damned. Combined effect of content quality and social ties on user engagement
Authors: Luca M. Aiello, Rossano Schifanella, Miriam Redi, Stacey Svetlichnaya, Frank Liu, Simon Osindero
Date: 2017-11-01 00:00:00
Tasks: Recommendation Systems

User participation in online communities is driven by the intertwinement of
the social network structure with the crowd-generated content that flows along
its links. These aspects are rarely explored jointly and at scale. By looking
at how users generate and access pictures of varying beauty on Flickr, we
investigate how the production of quality impacts the dynamics of online social
systems. We develop a deep learning computer vision model to score images
according to their aesthetic value and we validate its output through
crowdsourcing. By applying it to over 15B Flickr photos, we study for the first
time how image beauty is distributed over a large-scale social system.
Beautiful images are evenly distributed in the network, although only a small
core of people get social recognition for them. To study the impact of exposure
to quality on user engagement, we set up matching experiments aimed at
detecting causality from observational data. Exposure to beauty is
double-edged: following people who produce high-quality content increases one's
probability of uploading better photos; however, an excessive imbalance between
the quality generated by a user and the user's neighbors leads to a decline in
engagement. Our analysis has practical implications for improving link
recommender systems.
An Adaptive Genetic Algorithm for Solving N-Queens Problem
Authors: Uddalok Sarkar, Sayan Nag
Date: 2017-11-01 00:00:00

In this paper a Metaheuristic approach for solving the N-Queens Problem is
introduced to find the best possible solution in a reasonable amount of time.
Genetic Algorithm is used with a novel fitness function as the Metaheuristic.
The aim of N-Queens Problem is to place N queens on an N x N chessboard, in a
way so that no queen is in conflict with the others. Chromosome representation
and genetic operations like Mutation and Crossover are described in detail.
Results show that this approach yields promising and satisfactory results in
less time compared to that obtained from the previous approaches for several
large values of N.
Uncovering Latent Style Factors for Expressive Speech Synthesis
Authors: Yuxuan Wang, RJ Skerry-Ryan, Ying Xiao, Daisy Stanton, Joel Shor, Eric Battenberg, Rob Clark, Rif A. Saurous
Date: 2017-11-01 00:00:00
Tasks: Expressive Speech Synthesis, Speech Synthesis

Prosodic modeling is a core problem in speech synthesis. The key challenge is
producing desirable prosody from textual input containing only phonetic
information. In this preliminary study, we introduce the concept of "style
tokens" in Tacotron, a recently proposed end-to-end neural speech synthesis
model. Using style tokens, we aim to extract independent prosodic styles from
training data. We show that without annotation data or an explicit supervision
signal, our approach can automatically learn a variety of prosodic variations
in a purely data-driven way. Importantly, each style token corresponds to a
fixed style factor regardless of the given text sequence. As a result, we can
control the prosodic style of synthetic speech in a somewhat predictable and
globally consistent way.
Learning Hard Alignments with Variational Inference
Authors: Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly
Date: 2017-05-16 00:00:00
Tasks: Hard Attention, Image Captioning, Object Recognition, Phoneme Recognition, Q-Learning, speech-recognition, Speech Recognition, Variational Inference

There has recently been significant interest in hard attention models for
tasks such as object recognition, visual captioning and speech recognition.
Hard attention can offer benefits over soft attention such as decreased
computational cost, but training hard attention models can be difficult because
of the discrete latent variables they introduce. Previous work used REINFORCE
and Q-learning to approach these issues, but those methods can provide
high-variance gradient estimates and be slow to train. In this paper, we tackle
the problem of learning hard attention for a sequential task using variational
inference methods, specifically the recently introduced VIMCO and NVIL.
Furthermore, we propose a novel baseline that adapts VIMCO to this setting. We
demonstrate our method on a phoneme recognition task in clean and noisy
environments and show that our method outperforms REINFORCE, with the
difference being greater for a more complicated task.
Widening siamese architectures for stereo matching
Authors: Patrick Brandao, Evangelos Mazomenos, Danail Stoyanov
Date: 2017-11-01 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

Computational stereo is one of the classical problems in computer vision.
Numerous algorithms and solutions have been reported in recent years focusing
on developing methods for computing similarity, aggregating it to obtain
spatial support and finally optimizing an energy function to find the final
disparity. In this paper, we focus on the feature extraction component of
stereo matching architecture and we show standard CNNs operation can be used to
improve the quality of the features used to find point correspondences.
Furthermore, we propose a simple space aggregation that hugely simplifies the
correlation learning problem. Our results on benchmark data are compelling and
show promising potential even without refining the solution.
Learning Non-Discriminatory Predictors
Authors: Blake Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro
Date: 2017-02-20 00:00:00
Tasks: Attribute

We consider learning a predictor which is non-discriminatory with respect to
a "protected attribute" according to the notion of "equalized odds" proposed by
Hardt et al. [2016]. We study the problem of learning such a non-discriminatory
predictor from a finite training set, both statistically and computationally.
We show that a post-hoc correction approach, as suggested by Hardt et al, can
be highly suboptimal, present a nearly-optimal statistical procedure, argue
that the associated computational problem is intractable, and suggest a second
moment relaxation of the non-discrimination definition for which learning is
tractable.
Tensor Valued Common and Individual Feature Extraction: Multi-dimensional Perspective
Authors: Ilia Kisil, Giuseppe G. Calvi, Danilo P. Mandic
Date: 2017-11-01 00:00:00
Tasks: General Classification, Multi-class Classification

A novel method for common and individual feature analysis from exceedingly
large-scale data is proposed, in order to ensure the tractability of both the
computation and storage and thus mitigate the curse of dimensionality, a major
bottleneck in modern data science. This is achieved by making use of the
inherent redundancy in so-called multi-block data structures, which represent
multiple observations of the same phenomenon taken at different times, angles
or recording conditions. Upon providing an intrinsic link between the
properties of the outer vector product and extracted features in tensor
decompositions (TDs), the proposed common and individual information extraction
from multi-block data is performed through imposing physical meaning to
otherwise unconstrained factorisation approaches. This is shown to dramatically
reduce the dimensionality of search spaces for subsequent classification
procedures and to yield greatly enhanced accuracy. Simulations on a multi-class
classification task of large-scale extraction of individual features from a
collection of partially related real-world images demonstrate the advantages of
the "blessing of dimensionality" associated with TDs.