pwc-archive/papers-with-abstracts huggingface dataset:
One pixel attack for fooling deep neural networks
Authors: Jiawei Su, Danilo Vasconcellos Vargas, Sakurai Kouichi
Date: 2017-10-24 00:00:00
Tasks: BIG-bench Machine Learning

Recent research has revealed that the output of Deep Neural Networks (DNN) can be easily altered by adding relatively small perturbations to the input vector. In this paper, we analyze an attack in an extremely limited scenario where only one pixel can be modified. For that we propose a novel method for generating one-pixel adversarial perturbations based on differential evolution (DE). It requires less adversarial information (a black-box attack) and can fool more types of networks due to the inherent features of DE. The results show that 67.97% of the natural images in Kaggle CIFAR-10 test dataset and 16.04% of the ImageNet (ILSVRC 2012) test images can be perturbed to at least one target class by modifying just one pixel with 74.03% and 22.91% confidence on average. We also show the same vulnerability on the original CIFAR-10 dataset. Thus, the proposed attack explores a different take on adversarial machine learning in an extreme limited scenario, showing that current DNNs are also vulnerable to such low dimension attacks. Besides, we also illustrate an important application of DE (or broadly speaking, evolutionary computation) in the domain of adversarial machine learning: creating tools that can effectively generate low-cost adversarial attacks against neural networks for evaluating robustness.
Actigraphy-based Sleep/Wake Pattern Detection using Convolutional Neural Networks
Authors: Lena Granovsky, Gabi Shalev, Nancy Yacovzada, Yotam Frank, Shai Fine
Date: 2018-02-22 00:00:00
Tasks: Management, Multi-Task Learning, Sleep Quality, slot-filling, Slot Filling

Common medical conditions are often associated with sleep abnormalities.
Patients with medical disorders often suffer from poor sleep quality compared
to healthy individuals, which in turn may worsen the symptoms of the disorder.
Accurate detection of sleep/wake patterns is important in developing
personalized digital markers, which can be used for objective measurements and
efficient disease management. Big Data technologies and advanced analytics
methods hold the promise to revolutionize clinical research processes, enabling
the effective blending of digital data into clinical trials. Actigraphy, a
non-invasive activity monitoring method is heavily used to detect and evaluate
activities and movement disorders, and assess sleep/wake behavior. In order to
study the connection between sleep/wake patterns and a cluster headache
disorder, activity data was collected using a wearable device in the course of
a clinical trial. This study presents two novel modeling schemes that utilize
Deep Convolutional Neural Networks (CNN) to identify sleep/wake states. The
proposed methods are a sequential CNN, reminiscent of the bi-directional CNN
for slot filling, and a Multi-Task Learning (MTL) based model. Furthermore, we
expand standard "Sleep" and "Wake" activity states space by adding the "Falling
asleep" and "Siesta" states. We show that the proposed methods provide
promising results in accurate detection of the expanded sleep/wake states.
Finally, we explore the relations between the detected sleep/wake patterns and
onset of cluster headache attacks, and present preliminary observations.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
Authors: Jun Wang, Lantao Yu, Wei-Nan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, Dell Zhang
Date: 2017-05-30 00:00:00
Tasks: Ad-Hoc Information Retrieval, Document Ranking, Information Retrieval, Question Answering, Retrieval

This paper provides a unified account of two schools of thinking in
information retrieval modelling: the generative retrieval focusing on
predicting relevant documents given a query, and the discriminative retrieval
focusing on predicting relevancy given a query-document pair. We propose a game
theoretical minimax game to iteratively optimise both models. On one hand, the
discriminative model, aiming to mine signals from labelled and unlabelled data,
provides guidance to train the generative model towards fitting the underlying
relevance distribution over documents given the query. On the other hand, the
generative model, acting as an attacker to the current discriminative model,
generates difficult examples for the discriminative model in an adversarial way
by minimising its discrimination objective. With the competition between these
two models, we show that the unified framework takes advantage of both schools
of thinking: (i) the generative model learns to fit the relevance distribution
over documents via the signals from the discriminative model, and (ii) the
discriminative model is able to exploit the unlabelled data selected by the
generative model to achieve a better estimation for document ranking. Our
experimental results have demonstrated significant performance gains as much as
23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of
applications including web search, item recommendation, and question answering.
Training L1-Regularized Models with Orthant-Wise Passive Descent Algorithms
Authors: Jianqiao Wangni
Date: 2017-04-26 00:00:00
Tasks: regression

The $L_1$-regularized models are widely used for sparse regression or
classification tasks. In this paper, we propose the orthant-wise passive
descent algorithm (OPDA) for optimizing $L_1$-regularized models, as an
improved substitute of proximal algorithms, which are the standard tools for
optimizing the models nowadays. OPDA uses a stochastic variance-reduced
gradient (SVRG) to initialize the descent direction, then apply a novel
alignment operator to encourage each element keeping the same sign after one
iteration of update, so the parameter remains in the same orthant as before. It
also explicitly suppresses the magnitude of each element to impose sparsity.
The quasi-Newton update can be utilized to incorporate curvature information
and accelerate the speed. We prove a linear convergence rate for OPDA on
general smooth and strongly-convex loss functions. By conducting experiments on
$L_1$-regularized logistic regression and convolutional neural networks, we
show that OPDA outperforms state-of-the-art stochastic proximal algorithms,
implying a wide range of applications in training sparse models.
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning
Authors: Arunselvan Ramaswamy, Shalabh Bhatnagar, Daniel E. Quevedo
Date: 2018-02-22 00:00:00
Tasks: Multi-agent Reinforcement Learning, Policy Gradient Methods

Asynchronous stochastic approximations (SAs) are an important class of
model-free algorithms, tools and techniques that are popular in multi-agent and
distributed control scenarios. To counter Bellman's curse of dimensionality,
such algorithms are coupled with function approximations. Although the
learning/ control problem becomes more tractable, function approximations
affect stability and convergence. In this paper, we present verifiable
sufficient conditions for stability and convergence of asynchronous SAs with
biased approximation errors. The theory developed herein is used to analyze
Policy Gradient methods and noisy Value Iteration schemes. Specifically, we
analyze the asynchronous approximate counterparts of the policy gradient (A2PG)
and value iteration (A2VI) schemes. It is shown that the stability of these
algorithms is unaffected by biased approximation errors, provided they are
asymptotically bounded. With respect to convergence (of A2VI and A2PG), a
relationship between the limiting set and the approximation errors is
established. Finally, experimental results are presented that support the
theory.
Adversarial Learning for Semi-Supervised Semantic Segmentation
Authors: Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou, Yen-Yu Lin, Ming-Hsuan Yang
Date: 2018-02-22 00:00:00
Proceeding: ICLR 2018 1
Tasks: Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation

We propose a method for semi-supervised semantic segmentation using an
adversarial network. While most existing discriminators are trained to classify
input images as real or fake on the image level, we design a discriminator in a
fully convolutional manner to differentiate the predicted probability maps from
the ground truth segmentation distribution with the consideration of the
spatial resolution. We show that the proposed discriminator can be used to
improve semantic segmentation accuracy by coupling the adversarial loss with
the standard cross entropy loss of the proposed model. In addition, the fully
convolutional discriminator enables semi-supervised learning through
discovering the trustworthy regions in predicted results of unlabeled images,
thereby providing additional supervisory signals. In contrast to existing
methods that utilize weakly-labeled images, our method leverages unlabeled
images to enhance the segmentation model. Experimental results on the PASCAL
VOC 2012 and Cityscapes datasets demonstrate the effectiveness of the proposed
algorithm.
Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks
Authors: Gaurav Goswami, Nalini Ratha, Akshay Agarwal, Richa Singh, Mayank Vatsa
Date: 2018-02-22 00:00:00
Tasks: Deep Learning, Face Recognition

Deep neural network (DNN) architecture based models have high expressive
power and learning capacity. However, they are essentially a black box method
since it is not easy to mathematically formulate the functions that are learned
within its many layers of representation. Realizing this, many researchers have
started to design methods to exploit the drawbacks of deep learning based
algorithms questioning their robustness and exposing their singularities. In
this paper, we attempt to unravel three aspects related to the robustness of
DNNs for face recognition: (i) assessing the impact of deep architectures for
face recognition in terms of vulnerabilities to attacks inspired by commonly
observed distortions in the real world that are well handled by shallow
learning methods along with learning based adversaries; (ii) detecting the
singularities by characterizing abnormal filter response behavior in the hidden
layers of deep networks; and (iii) making corrections to the processing
pipeline to alleviate the problem. Our experimental evaluation using multiple
open-source DNN-based face recognition networks, including OpenFace and
VGG-Face, and two publicly available databases (MEDS and PaSC) demonstrates
that the performance of deep learning based face recognition algorithms can
suffer greatly in the presence of such distortions. The proposed method is also
compared with existing detection algorithms and the results show that it is
able to detect the attacks with very high accuracy by suitably designing a
classifier using the response of the hidden layers in the network. Finally, we
present several effective countermeasures to mitigate the impact of adversarial
attacks and improve the overall robustness of DNN-based face recognition.
Where's YOUR focus: Personalized Attention
Authors: Sikun Lin, Pan Hui
Date: 2018-02-22 00:00:00
Tasks: Prediction, Saliency Detection, Saliency Prediction

Human visual attention is subjective and biased according to the personal
preference of the viewer, however, current works of saliency detection are
general and objective, without counting the factor of the observer. This will
make the attention prediction for a particular person not accurate enough. In
this work, we present the novel idea of personalized attention prediction and
develop Personalized Attention Network (PANet), a convolutional network that
predicts saliency in images with personal preference. The model consists of two
streams which share common feature extraction layers, and one stream is
responsible for saliency prediction, while the other is adapted from the
detection model and used to fit user preference. We automatically collect user
preference from their albums and leaves them freedom to define what and how
many categories their preferences are divided into. To train PANet, we
dynamically generate ground truth saliency maps upon existing detection labels
and saliency labels, and the generation parameters are based upon our collected
datasets consists of 1k images. We evaluate the model with saliency prediction
metrics and test the trained model on different preference vectors. The results
have shown that our system is much better than general models in personalized
saliency prediction and is efficient to use for different preferences.
Construction of a Japanese Word Similarity Dataset
Authors: Yuya Sakaizawa, Mamoru Komachi
Date: 2017-03-17 00:00:00
Proceeding: LREC 2018 5
Tasks: Word Similarity

An evaluation of distributed word representation is generally conducted using
a word similarity task and/or a word analogy task. There are many datasets
readily available for these tasks in English. However, evaluating distributed
representation in languages that do not have such resources (e.g., Japanese) is
difficult. Therefore, as a first step toward evaluating distributed
representations in Japanese, we constructed a Japanese word similarity dataset.
To the best of our knowledge, our dataset is the first resource that can be
used to evaluate distributed representations in Japanese. Moreover, our dataset
contains various parts of speech and includes rare words in addition to common
words.
Graph-Based Blind Image Deblurring From a Single Photograph
Authors: Yuanchao Bai, Gene Cheung, Xian-Ming Liu, Wen Gao
Date: 2018-02-22 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring

Blind image deblurring, i.e., deblurring without knowledge of the blur
kernel, is a highly ill-posed problem. The problem can be solved in two parts:
i) estimate a blur kernel from the blurry image, and ii) given estimated blur
kernel, de-convolve blurry input to restore the target image. In this paper, we
propose a graph-based blind image deblurring algorithm by interpreting an image
patch as a signal on a weighted graph. Specifically, we first argue that a
skeleton image---a proxy that retains the strong gradients of the target but
smooths out the details---can be used to accurately estimate the blur kernel
and has a unique bi-modal edge weight distribution. Then, we design a
reweighted graph total variation (RGTV) prior that can efficiently promote a
bi-modal edge weight distribution given a blurry patch. Further, to analyze
RGTV in the graph frequency domain, we introduce a new weight function to
represent RGTV as a graph $l_1$-Laplacian regularizer. This leads to a graph
spectral filtering interpretation of the prior with desirable properties,
including robustness to noise and blur, strong piecewise smooth (PWS) filtering
and sharpness promotion. Minimizing a blind image deblurring objective with
RGTV results in a non-convex non-differentiable optimization problem. We
leverage the new graph spectral interpretation for RGTV to design an efficient
algorithm that solves for the skeleton image and the blur kernel alternately.
Specifically for Gaussian blur, we propose a further speedup strategy for blind
Gaussian deblurring using accelerated graph spectral filtering. Finally, with
the computed blur kernel, recent non-blind image deblurring algorithms can be
applied to restore the target image. Experimental results demonstrate that our
algorithm successfully restores latent sharp images and outperforms
state-of-the-art methods quantitatively and qualitatively.
Asynchronous Byzantine Machine Learning (the case of SGD)
Authors: Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Rhicheek Patra, Mahsa Taziki
Date: 2018-02-22 00:00:00
Proceeding: ICML 2018 7
Tasks: BIG-bench Machine Learning

Asynchronous distributed machine learning solutions have proven very
effective so far, but always assuming perfectly functioning workers. In
practice, some of the workers can however exhibit Byzantine behavior, caused by
hardware failures, software bugs, corrupt data, or even malicious attacks. We
introduce \emph{Kardam}, the first distributed asynchronous stochastic gradient
descent (SGD) algorithm that copes with Byzantine workers. Kardam consists of
two complementary components: a filtering and a dampening component. The first
is scalar-based and ensures resilience against $\frac{1}{3}$ Byzantine workers.
Essentially, this filter leverages the Lipschitzness of cost functions and acts
as a self-stabilizer against Byzantine workers that would attempt to corrupt
the progress of SGD. The dampening component bounds the convergence rate by
adjusting to stale information through a generic gradient weighting scheme. We
prove that Kardam guarantees almost sure convergence in the presence of
asynchrony and Byzantine behavior, and we derive its convergence rate. We
evaluate Kardam on the CIFAR-100 and EMNIST datasets and measure its overhead
with respect to non Byzantine-resilient solutions. We empirically show that
Kardam does not introduce additional noise to the learning procedure but does
induce a slowdown (the cost of Byzantine resilience) that we both theoretically
and empirically show to be less than $f/n$, where $f$ is the number of
Byzantine failures tolerated and $n$ the total number of workers.
Interestingly, we also empirically observe that the dampening component is
interesting in its own right for it enables to build an SGD algorithm that
outperforms alternative staleness-aware asynchronous competitors in
environments with honest workers.
The Hidden Vulnerability of Distributed Learning in Byzantium
Authors: El Mahdi El Mhamdi, Rachid Guerraoui, Sébastien Rouault
Date: 2018-02-22 00:00:00
Proceeding: ICML 2018 7

While machine learning is going through an era of celebrated success,
concerns have been raised about the vulnerability of its backbone: stochastic
gradient descent (SGD). Recent approaches have been proposed to ensure the
robustness of distributed SGD against adversarial (Byzantine) workers sending
poisoned gradients during the training phase. Some of these approaches have
been proven Byzantine-resilient: they ensure the convergence of SGD despite the
presence of a minority of adversarial workers.
  We show in this paper that convergence is not enough. In high dimension $d
\gg 1$, an adver\-sary can build on the loss function's non-convexity to make
SGD converge to ineffective models. More precisely, we bring to light that
existing Byzantine-resilient schemes leave a margin of poisoning of
$\Omega\left(f(d)\right)$, where $f(d)$ increases at least like $\sqrt{d~}$.
Based on this leeway, we build a simple attack, and experimentally show its
strong to utmost effectivity on CIFAR-10 and MNIST.
  We introduce Bulyan, and prove it significantly reduces the attackers leeway
to a narrow $O( \frac{1}{\sqrt{d~}})$ bound. We empirically show that Bulyan
does not suffer the fragility of existing aggregation rules and, at a
reasonable cost in terms of required batch size, achieves convergence as if
only non-Byzantine gradients had been used to update the model.
Graph Convolutional Networks for Classification with a Structured Label Space
Authors: Meihao Chen, Zhuoru Lin, Kyunghyun Cho
Date: 2017-10-12 00:00:00
Tasks: Classification, Document Classification, General Classification, Multi-class Classification, Object Recognition

It is a usual practice to ignore any structural information underlying
classes in multi-class classification. In this paper, we propose a graph
convolutional network (GCN) augmented neural network classifier to exploit a
known, underlying graph structure of labels. The proposed approach resembles an
(approximate) inference procedure in, for instance, a conditional random field
(CRF). We evaluate the proposed approach on document classification and object
recognition and report both accuracies and graph-theoretic metrics that
correspond to the consistency of the model's prediction. The experiment results
reveal that the proposed model outperforms a baseline method which ignores the
graph structures of a label space in terms of graph-theoretic metrics.
Video Person Re-identification by Temporal Residual Learning
Authors: Ju Dai, Pingping Zhang, Huchuan Lu, Hongyu Wang
Date: 2018-02-22 00:00:00
Tasks: Person Re-Identification, Video-Based Person Re-Identification

In this paper, we propose a novel feature learning framework for video person
re-identification (re-ID). The proposed framework largely aims to exploit the
adequate temporal information of video sequences and tackle the poor spatial
alignment of moving pedestrians. More specifically, for exploiting the temporal
information, we design a temporal residual learning (TRL) module to
simultaneously extract the generic and specific features of consecutive frames.
The TRL module is equipped with two bi-directional LSTM (BiLSTM), which are
respectively responsible to describe a moving person in different aspects,
providing complementary information for better feature representations. To deal
with the poor spatial alignment in video re-ID datasets, we propose a
spatial-temporal transformer network (ST^2N) module. Transformation parameters
in the ST^2N module are learned by leveraging the high-level semantic
information of the current frame as well as the temporal context knowledge from
other frames. The proposed ST^2N module with less learnable parameters allows
effective person alignments under significant appearance changes. Extensive
experimental results on the large-scale MARS, PRID2011, ILIDS-VID and SDU-VID
datasets demonstrate that the proposed method achieves consistently superior
performance and outperforms most of the very recent state-of-the-art methods.
Regional Multi-Armed Bandits
Authors: Zhiyang Wang, Ruida Zhou, Cong Shen
Date: 2018-02-22 00:00:00
Tasks: Multi-Armed Bandits

We consider a variant of the classic multi-armed bandit problem where the
expected reward of each arm is a function of an unknown parameter. The arms are
divided into different groups, each of which has a common parameter. Therefore,
when the player selects an arm at each time slot, information of other arms in
the same group is also revealed. This regional bandit model naturally bridges
the non-informative bandit setting where the player can only learn the chosen
arm, and the global bandit model where sampling one arms reveals information of
all arms. We propose an efficient algorithm, UCB-g, that solves the regional
bandit problem by combining the Upper Confidence Bound (UCB) and greedy
principles. Both parameter-dependent and parameter-free regret upper bounds are
derived. We also establish a matching lower bound, which proves the
order-optimality of UCB-g. Moreover, we propose SW-UCB-g, which is an extension
of UCB-g for a non-stationary environment where the parameters slowly vary over
time.
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
Authors: Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan O. Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, John Miller
Date: 2017-10-20 00:00:00
Proceeding: ICLR 2018 1
Tasks: GPU, Speech Synthesis, text-to-speech, Text to Speech

We present Deep Voice 3, a fully-convolutional attention-based neural
text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural
speech synthesis systems in naturalness while training ten times faster. We
scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more
than eight hundred hours of audio from over two thousand speakers. In addition,
we identify common error modes of attention-based speech synthesis networks,
demonstrate how to mitigate them, and compare several different waveform
synthesis methods. We also describe how to scale inference to ten million
queries per day on one single-GPU server.