pwc-archive/papers-with-abstracts huggingface dataset:
Confusion2Vec: Towards Enriching Vector Space Word Representations with Representational Ambiguities
Authors: Prashanth Gurunath Shivakumar, Panayiotis Georgiou
Date: 2018-11-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, Word Similarity

Word vector representations are a crucial part of Natural Language Processing
(NLP) and Human Computer Interaction. In this paper, we propose a novel word
vector representation, Confusion2Vec, motivated from the human speech
production and perception that encodes representational ambiguity. Humans
employ both acoustic similarity cues and contextual cues to decode information
and we focus on a model that incorporates both sources of information. The
representational ambiguity of acoustics, which manifests itself in word
confusions, is often resolved by both humans and machines through contextual
cues. A range of representational ambiguities can emerge in various domains
further to acoustic perception, such as morphological transformations,
paraphrasing for NLP tasks like machine translation etc. In this work, we
present a case study in application to Automatic Speech Recognition (ASR),
where the word confusions are related to acoustic similarity. We present
several techniques to train an acoustic perceptual similarity representation
ambiguity. We term this Confusion2Vec and learn on unsupervised-generated data
from ASR confusion networks or lattice-like structures. Appropriate evaluations
for the Confusion2Vec are formulated for gauging acoustic similarity in
addition to semantic-syntactic and word similarity evaluations. The
Confusion2Vec is able to model word confusions efficiently, without
compromising on the semantic-syntactic word relations, thus effectively
enriching the word vector space with extra task relevant ambiguity information.
We provide an intuitive exploration of the 2-dimensional Confusion2Vec space
using Principal Component Analysis of the embedding and relate to semantic,
syntactic and acoustic relationships. The potential of Confusion2Vec in the
utilization of uncertainty present in lattices is demonstrated through small
examples relating to ASR error correction.
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning
Authors: Yanchun Xie, Jimin Xiao, Kai-Zhu Huang, Jeyarajan Thiyagalingam, Yao Zhao
Date: 2018-11-08 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Visual Tracking

Correlation filter has been proven to be an effective tool for a number of
approaches in visual tracking, particularly for seeking a good balance between
tracking accuracy and speed. However, correlation filter based models are
susceptible to wrong updates stemming from inaccurate tracking results. To
date, little effort has been devoted towards handling the correlation filter
update problem. In this paper, we propose a novel approach to address the
correlation filter update problem. In our approach, we update and maintain
multiple correlation filter models in parallel, and we use deep reinforcement
learning for the selection of an optimal correlation filter model among them.
To facilitate the decision process in an efficient manner, we propose a
decision-net to deal target appearance modeling, which is trained through
hundreds of challenging videos using proximal policy optimization and a
lightweight learning network. An exhaustive evaluation of the proposed approach
on the OTB100 and OTB2013 benchmarks show that the approach is effective enough
to achieve the average success rate of 62.3% and the average precision score of
81.2%, both exceeding the performance of traditional correlation filter based
trackers.
Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering
Authors: Konstantin Makarychev, Yury Makarychev, Ilya Razenshteyn
Date: 2018-11-08 00:00:00
Tasks: Clustering, Dimensionality Reduction

Consider an instance of Euclidean $k$-means or $k$-medians clustering. We show that the cost of the optimal solution is preserved up to a factor of $(1+\varepsilon)$ under a projection onto a random $O(\log(k / \varepsilon) / \varepsilon^2)$-dimensional subspace. Further, the cost of every clustering is preserved within $(1+\varepsilon)$. More generally, our result applies to any dimension reduction map satisfying a mild sub-Gaussian-tail condition. Our bound on the dimension is nearly optimal. Additionally, our result applies to Euclidean $k$-clustering with the distances raised to the $p$-th power for any constant $p$. For $k$-means, our result resolves an open problem posed by Cohen, Elder, Musco, Musco, and Persu (STOC 2015); for $k$-medians, it answers a question raised by Kannan.
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning
Authors: Florian Tramèr, Pascal Dupré, Gili Rusak, Giancarlo Pellegrino, Dan Boneh
Date: 2018-11-08 00:00:00
Tasks: BIG-bench Machine Learning, Blocking

Perceptual ad-blocking is a novel approach that detects online advertisements based on their visual content. Compared to traditional filter lists, the use of perceptual signals is believed to be less prone to an arms race with web publishers and ad networks. We demonstrate that this may not be the case. We describe attacks on multiple perceptual ad-blocking techniques, and unveil a new arms race that likely disfavors ad-blockers. Unexpectedly, perceptual ad-blocking can also introduce new vulnerabilities that let an attacker bypass web security boundaries and mount DDoS attacks. We first analyze the design space of perceptual ad-blockers and present a unified architecture that incorporates prior academic and commercial work. We then explore a variety of attacks on the ad-blocker's detection pipeline, that enable publishers or ad networks to evade or detect ad-blocking, and at times even abuse its high privilege level to bypass web security boundaries. On one hand, we show that perceptual ad-blocking must visually classify rendered web content to escape an arms race centered on obfuscation of page markup. On the other, we present a concrete set of attacks on visual ad-blockers by constructing adversarial examples in a real web page context. For seven ad-detectors, we create perturbed ads, ad-disclosure logos, and native web content that misleads perceptual ad-blocking with 100% success rates. In one of our attacks, we demonstrate how a malicious user can upload adversarial content, such as a perturbed image in a Facebook post, that fools the ad-blocker into removing another users' non-ad content. Moving beyond the Web and visual domain, we also build adversarial examples for AdblockRadio, an open source radio client that uses machine learning to detects ads in raw audio streams.
Towards Fluent Translations from Disfluent Speech
Authors: Elizabeth Salesky, Susanne Burger, Jan Niehues, Alex Waibel
Date: 2018-11-07 00:00:00
Tasks: Machine Translation, speech-recognition, Speech Recognition, Translation

When translating from speech, special consideration for conversational speech
phenomena such as disfluencies is necessary. Most machine translation training
data consists of well-formed written texts, causing issues when translating
spontaneous speech. Previous work has introduced an intermediate step between
speech recognition (ASR) and machine translation (MT) to remove disfluencies,
making the data better-matched to typical translation text and significantly
improving performance. However, with the rise of end-to-end speech translation
systems, this intermediate step must be incorporated into the
sequence-to-sequence architecture. Further, though translated speech datasets
exist, they are typically news or rehearsed speech without many disfluencies
(e.g. TED), or the disfluencies are translated into the references (e.g.
Fisher). To generate clean translations from disfluent speech, cleaned
references are necessary for evaluation. We introduce a corpus of cleaned
target data for the Fisher Spanish-English dataset for this task. We compare
how different architectures handle disfluencies and provide a baseline for
removing disfluencies in end-to-end translation.
Solving Jigsaw Puzzles By the Graph Connection Laplacian
Authors: Vahan Huroyan, Gilad Lerman, Hau-Tieng Wu
Date: 2018-11-07 00:00:00

We propose a novel mathematical framework to address the problem of automatically solving large jigsaw puzzles. This problem assumes a large image, which is cut into equal square pieces that are arbitrarily rotated and shuffled, and asks to recover the original image given the transformed pieces. The main contribution of this work is a method for recovering the rotations of the pieces when both shuffles and rotations are unknown. A major challenge of this procedure is estimating the graph connection Laplacian without the knowledge of shuffles. A careful combination of our proposed method for estimating rotations with any existing method for estimating shuffles results in a practical solution for the jigsaw puzzle problem. Our theory guarantees, in a clean setting, that our basic idea of recovering rotations is robust to some corruption of the connection graph. Numerical experiments demonstrate the competitive accuracy of this solution, its robustness to corruption and, its computational advantage for large puzzles.
How Well Generative Adversarial Networks Learn Distributions
Authors: Tengyuan Liang
Date: 2018-11-07 00:00:00
Tasks: Density Estimation, valid

This paper studies the rates of convergence for learning distributions implicitly with the adversarial framework and Generative Adversarial Networks (GANs), which subsume Wasserstein, Sobolev, MMD GAN, and Generalized/Simulated Method of Moments (GMM/SMM) as special cases. We study a wide range of parametric and nonparametric target distributions under a host of objective evaluation metrics. We investigate how to obtain valid statistical guarantees for GANs through the lens of regularization. On the nonparametric end, we derive the optimal minimax rates for distribution estimation under the adversarial framework. On the parametric end, we establish a theory for general neural network classes (including deep leaky ReLU networks) that characterizes the interplay on the choice of generator and discriminator pair. We discover and isolate a new notion of regularization, called the generator-discriminator-pair regularization, that sheds light on the advantage of GANs compared to classical parametric and nonparametric approaches for explicit distribution estimation. We develop novel oracle inequalities as the main technical tools for analyzing GANs, which are of independent interest.
Automatic Thresholding of SIFT Descriptors
Authors: Matthew R. Kirchner
Date: 2018-11-07 00:00:00

We introduce a method to perform automatic thresholding of SIFT descriptors
that improves matching performance by at least 15.9% on the Oxford image
matching benchmark. The method uses a contrario methodology to determine a
unique bin magnitude threshold. This is done by building a generative uniform
background model for descriptors and determining when bin magnitudes have
reached a sufficient level. The presented method, called meaningful clamping,
contrasts from the current SIFT implementation by efficiently computing a
clamping threshold that is unique for every descriptor.
Attention Fusion Networks: Combining Behavior and E-mail Content to Improve Customer Support
Authors: Stephane Fotso, Philip Spanoudes, Benjamin C. Ponedel, Brian Reynoso, Janet Ko
Date: 2018-11-07 00:00:00

Customer support is a central objective at Square as it helps us build and
maintain great relationships with our sellers. In order to provide the best
experience, we strive to deliver the most accurate and quasi-instantaneous
responses to questions regarding our products.
  In this work, we introduce the Attention Fusion Network model which combines
signals extracted from seller interactions on the Square product ecosystem,
along with submitted email questions, to predict the most relevant solution to
a seller's inquiry. We show that the innovative combination of two very
different data sources that are rarely used together, using state-of-the-art
deep learning systems outperforms, candidate models that are trained only on a
single source.
SRP: Efficient class-aware embedding learning for large-scale data via supervised random projections
Authors: Amir-Hossein Karimi, Alexander Wong, Ali Ghodsi
Date: 2018-11-07 00:00:00
Tasks: Dimensionality Reduction, Supervised dimensionality reduction

Supervised dimensionality reduction strategies have been of great interest.
However, current supervised dimensionality reduction approaches are difficult
to scale for situations characterized by large datasets given the high
computational complexities associated with such methods. While stochastic
approximation strategies have been explored for unsupervised dimensionality
reduction to tackle this challenge, such approaches are not well-suited for
accelerating computational speed for supervised dimensionality reduction.
Motivated to tackle this challenge, in this study we explore a novel direction
of directly learning optimal class-aware embeddings in a supervised manner via
the notion of supervised random projections (SRP). The key idea behind SRP is
that, rather than performing spectral decomposition (or approximations thereof)
which are computationally prohibitive for large-scale data, we instead perform
a direct decomposition by leveraging kernel approximation theory and the
symmetry of the Hilbert-Schmidt Independence Criterion (HSIC) measure of
dependence between the embedded data and the labels. Experimental results on
five different synthetic and real-world datasets demonstrate that the proposed
SRP strategy for class-aware embedding learning can be very promising in
producing embeddings that are highly competitive with existing supervised
dimensionality reduction methods (e.g., SPCA and KSPCA) while achieving 1-2
orders of magnitude better computational performance. As such, such an
efficient approach to learning embeddings for dimensionality reduction can be a
powerful tool for large-scale data analysis and visualization.
Contrastive Explanation: A Structural-Model Approach
Authors: Tim Miller
Date: 2018-11-07 00:00:00
Tasks: Decision Making, model, Philosophy

This paper presents a model of contrastive explanation using structural casual models. The topic of causal explanation in artificial intelligence has gathered interest in recent years as researchers and practitioners aim to increase trust and understanding of intelligent decision-making. While different sub-fields of artificial intelligence have looked into this problem with a sub-field-specific view, there are few models that aim to capture explanation more generally. One general model is based on structural causal models. It defines an explanation as a fact that, if found to be true, would constitute an actual cause of a specific event. However, research in philosophy and social sciences shows that explanations are contrastive: that is, when people ask for an explanation of an event -- the fact -- they (sometimes implicitly) are asking for an explanation relative to some contrast case; that is, "Why P rather than Q?". In this paper, we extend the structural causal model approach to define two complementary notions of contrastive explanation, and demonstrate them on two classical problems in artificial intelligence: classification and planning. We believe that this model can help researchers in subfields of artificial intelligence to better understand contrastive explanation.
Forensic Discrimination between Traditional and Compressive Imaging Systems
Authors: Ali Taimori, Farokh Marvasti
Date: 2018-11-07 00:00:00
Tasks: Compressive Sensing, Image Forensics

Compressive sensing is a new technology for modern computational imaging
systems. In comparison to widespread conventional image sensing, the
compressive imaging paradigm requires specific forensic analysis techniques and
tools. In this regards, one of basic scenarios in image forensics is to
distinguish traditionally sensed images from sophisticated compressively sensed
ones. To do this, we first mathematically and systematically model the imaging
system based on compressive sensing technology. Afterwards, a simplified
version of the whole model is presented, which is appropriate for forensic
investigation applications. We estimate the nonlinear system of compressive
sensing with a linear model. Then, we model the imaging pipeline as an inverse
problem and demonstrate that different imagers have discriminative degradation
kernels. Hence, blur kernels of various imaging systems have utilized as
footprints for discriminating image acquisition sources. In order to accomplish
the identification cycle, we have utilized the state-of-the-art Convolutional
Neural Network (CNN) and Support Vector Machine (SVM) approaches to learn a
classification system from estimated blur kernels. Numerical experiments show
promising identification results. Simulation codes are available for research
and development purposes.
Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
Authors: Maryam Fatemi, Karl Granström, Lennart Svensson, Francisco J. R. Ruiz, Lars Hammarstrand
Date: 2018-11-07 00:00:00

This paper addresses the mapping problem. Using a conjugate prior form, we
derive the exact theoretical batch multi-object posterior density of the map
given a set of measurements. The landmarks in the map are modeled as extended
objects, and the measurements are described as a Poisson process, conditioned
on the map. We use a Poisson process prior on the map and prove that the
posterior distribution is a hybrid Poisson, multi-Bernoulli mixture
distribution. We devise a Gibbs sampling algorithm to sample from the batch
multi-object posterior. The proposed method can handle uncertainties in the
data associations and the cardinality of the set of landmarks, and is
parallelizable, making it suitable for large-scale problems. The performance of
the proposed method is evaluated on synthetic data and is shown to outperform a
state-of-the-art method.
DragonPaint: Rule based bootstrapping for small data with an application to cartoon coloring
Authors: K. Gretchen Greene
Date: 2018-11-07 00:00:00
Tasks: Image-to-Image Translation, Small Data Image Classification, Translation

In this paper, we confront the problem of deep learning's big labeled data
requirements, offer a rule based strategy for extreme augmentation of small
data sets and apply that strategy with the image to image translation model by
Isola et al. (2016) to automate cel style cartoon coloring with very limited
training data. While our experimental results using geometric rules and
transformations demonstrate the performance of our methods on an image
translation task with industry applications in art, design and animation, we
also propose the use of rules on partial data sets as a generalizable small
data strategy, potentially applicable across data types and domains.
Time Series Classification to Improve Poultry Welfare
Authors: Alireza Abdoli, Amy C. Murillo, Chin-Chia M. Yeh, Alec C. Gerry, Eamonn J. Keogh
Date: 2018-11-07 00:00:00
Tasks: Classification, Dictionary Learning, General Classification, Time Series, Time Series Analysis, Time Series Classification

Poultry farms are an important contributor to the human food chain.
Worldwide, humankind keeps an enormous number of domesticated birds (e.g.
chickens) for their eggs and their meat, providing rich sources of low-fat
protein. However, around the world, there have been growing concerns about the
quality of life for the livestock in poultry farms; and increasingly vocal
demands for improved standards of animal welfare. Recent advances in sensing
technologies and machine learning allow the possibility of automatically
assessing the health of some individual birds, and employing the lessons
learned to improve the welfare for all birds. This task superficially appears
to be easy, given the dramatic progress in recent years in classifying human
behaviors, and given that human behaviors are presumably more complex. However,
as we shall demonstrate, classifying chicken behaviors poses several unique
challenges, chief among which is creating a generalizable dictionary of
behaviors from sparse and noisy data. In this work we introduce a novel time
series dictionary learning algorithm that can robustly learn from weakly
labeled data sources.
Global Optimality in Distributed Low-rank Matrix Factorization
Authors: Zhihui Zhu, Qiuwei Li, Xinshuo Yang, Gongguo Tang, Michael B. Wakin
Date: 2018-11-07 00:00:00

We study the convergence of a variant of distributed gradient descent (DGD)
on a distributed low-rank matrix approximation problem wherein some
optimization variables are used for consensus (as in classical DGD) and some
optimization variables appear only locally at a single node in the network. We
term the resulting algorithm DGD+LOCAL. Using algorithmic connections to
gradient descent and geometric connections to the well-behaved landscape of the
centralized low-rank matrix approximation problem, we identify sufficient
conditions where DGD+LOCAL is guaranteed to converge with exact consensus to a
global minimizer of the original centralized problem. For the distributed
low-rank matrix approximation problem, these guarantees are stronger---in terms
of consensus and optimality---than what appear in the literature for classical
DGD and more general problems.
ColorUNet: A convolutional classification approach to colorization
Authors: Vincent Billaut, Matthieu de Rochemonteix, Marc Thibault
Date: 2018-11-07 00:00:00
Tasks: Classification, Colorization, Data Augmentation, General Classification

This paper tackles the challenge of colorizing grayscale images. We take a
deep convolutional neural network approach, and choose to take the angle of
classification, working on a finite set of possible colors. Similarly to a
recent paper, we implement a loss and a prediction function that favor
realistic, colorful images rather than "true" ones.
  We show that a rather lightweight architecture inspired by the U-Net, and
trained on a reasonable amount of pictures of landscapes, achieves satisfactory
results on this specific subset of pictures. We show that data augmentation
significantly improves the performance and robustness of the model, and provide
visual analysis of the prediction confidence.
  We show an application of our model, extending the task to video
colorization. We suggest a way to smooth color predictions across frames,
without the need to train a recurrent network designed for sequential inputs.
On the Complexity of Reconnaissance Blind Chess
Authors: Jared Markowitz, Ryan W. Gardner, Ashley J. Llorens
Date: 2018-11-07 00:00:00
Tasks: Decision Making

This paper provides a complexity analysis for the game of reconnaissance
blind chess (RBC), a recently-introduced variant of chess where each player
does not know the positions of the opponent's pieces a priori but may reveal a
subset of them through chosen, private sensing actions. In contrast to many
commonly studied imperfect information games like poker, an RBC player does not
know what the opponent knows or has chosen to learn, exponentially expanding
the size of the game's information sets (i.e., the number of possible game
states that are consistent with what a player has observed). Effective RBC
sensing and moving strategies must account for the uncertainty of both players,
an essential element of many real-world decision-making problems. Here we
evaluate RBC from a game theoretic perspective, tracking the proliferation of
information sets from the perspective of selected canonical bot players in
tournament play. We show that, even for effective sensing strategies, the game
sizes of RBC compare to those of Go while the average size of a player's
information set throughout an RBC game is much greater than that of a player in
Heads-up Limit Hold 'Em. We compare these measures of complexity among
different playing algorithms and provide cursory assessments of the various
sensing and moving strategies.