pwc-archive/papers-with-abstracts huggingface dataset:
Generating Steganographic Text with LSTMs
Authors: Tina Fang, Martin Jaggi, Katerina Argyraki
Date: 2017-05-30 00:00:00
Proceeding: ACL 2017 7

Motivated by concerns for user privacy, we design a steganographic system
("stegosystem") that enables two users to exchange encrypted messages without
an adversary detecting that such an exchange is taking place. We propose a new
linguistic stegosystem based on a Long Short-Term Memory (LSTM) neural network.
We demonstrate our approach on the Twitter and Enron email datasets and show
that it yields high-quality steganographic text while significantly improving
capacity (encrypted bits per word) relative to the state-of-the-art.
Efficient Decentralized Visual Place Recognition From Full-Image Descriptors
Authors: Titus Cieslewski, Davide Scaramuzza
Date: 2017-05-30 00:00:00
Tasks: Clustering, Image Description, Visual Place Recognition

In this paper, we discuss the adaptation of our decentralized place
recognition method described in [1] to full image descriptors. As we had shown,
the key to making a scalable decentralized visual place recognition lies in
exploting deterministic key assignment in a distributed key-value map. Through
this, it is possible to reduce bandwidth by up to a factor of n, the robot
count, by casting visual place recognition to a key-value lookup problem. In
[1], we exploited this for the bag-of-words method [3], [4]. Our method of
casting bag-of-words, however, results in a complex decentralized system, which
has inherently worse recall than its centralized counterpart. In this paper, we
instead start from the recent full-image description method NetVLAD [5]. As we
show, casting this to a key-value lookup problem can be achieved with k-means
clustering, and results in a much simpler system than [1]. The resulting system
still has some flaws, albeit of a completely different nature: it suffers when
the environment seen during deployment lies in a different distribution in
feature space than the environment seen during training.
Fast Regression with an $\ell_\infty$ Guarantee
Authors: Eric Price, Zhao Song, David P. Woodruff
Date: 2017-05-30 00:00:00
Tasks: regression

Sketching has emerged as a powerful technique for speeding up problems in
numerical linear algebra, such as regression. In the overconstrained regression
problem, one is given an $n \times d$ matrix $A$, with $n \gg d$, as well as an
$n \times 1$ vector $b$, and one wants to find a vector $\hat{x}$ so as to
minimize the residual error $\|Ax-b\|_2$. Using the sketch and solve paradigm,
one first computes $S \cdot A$ and $S \cdot b$ for a randomly chosen matrix
$S$, then outputs $x' = (SA)^{\dagger} Sb$ so as to minimize $\|SAx' - Sb\|_2$.
  The sketch-and-solve paradigm gives a bound on $\|x'-x^*\|_2$ when $A$ is
well-conditioned. Our main result is that, when $S$ is the subsampled
randomized Fourier/Hadamard transform, the error $x' - x^*$ behaves as if it
lies in a "random" direction within this bound: for any fixed direction $a\in
\mathbb{R}^d$, we have with $1 - d^{-c}$ probability that
  \[
  \langle a, x'-x^*\rangle \lesssim
\frac{\|a\|_2\|x'-x^*\|_2}{d^{\frac{1}{2}-\gamma}}, \quad (1)
  \]
  where $c, \gamma > 0$ are arbitrary constants.
  This implies $\|x'-x^*\|_{\infty}$ is a factor $d^{\frac{1}{2}-\gamma}$
smaller than $\|x'-x^*\|_2$. It also gives a better bound on the generalization
of $x'$ to new examples: if rows of $A$ correspond to examples and columns to
features, then our result gives a better bound for the error introduced by
sketch-and-solve when classifying fresh examples. We show that not all
oblivious subspace embeddings $S$ satisfy these properties. In particular, we
give counterexamples showing that matrices based on Count-Sketch or leverage
score sampling do not satisfy these properties.
  We also provide lower bounds, both on how small $\|x'-x^*\|_2$ can be, and
for our new guarantee (1), showing that the subsampled randomized
Fourier/Hadamard transform is nearly optimal.
Low Impact Artificial Intelligences
Authors: Stuart Armstrong, Benjamin Levinstein
Date: 2017-05-30 00:00:00

There are many goals for an AI that could become dangerous if the AI becomes
superintelligent or otherwise powerful. Much work on the AI control problem has
been focused on constructing AI goals that are safe even for such AIs. This
paper looks at an alternative approach: defining a general concept of `low
impact'. The aim is to ensure that a powerful AI which implements low impact
will not modify the world extensively, even if it is given a simple or
dangerous goal. The paper proposes various ways of defining and grounding low
impact, and discusses methods for ensuring that the AI can still be allowed to
have a (desired) impact despite the restriction. The end of the paper addresses
known issues with this approach and avenues for future research.
Addressing Ambiguity in Multi-target Tracking by Hierarchical Strategy
Authors: Ali Taalimi, Liu Liu, Hairong Qi
Date: 2017-05-30 00:00:00

This paper presents a novel hierarchical approach for the simultaneous
tracking of multiple targets in a video. We use a network flow approach to link
detections in low-level and tracklets in high-level. At each step of the
hierarchy, the confidence of candidates is measured by using a new scoring
system, ConfRank, that considers the quality and the quantity of its
neighborhood. The output of the first stage is a collection of safe tracklets
and unlinked high-confidence detections. For each individual detection, we
determine if it belongs to an existing or is a new tracklet. We show the effect
of our framework to recover missed detections and reduce switch identity. The
proposed tracker is referred to as TVOD for multi-target tracking using the
visual tracker and generic object detector. We achieve competitive results with
lower identity switches on several datasets comparing to state-of-the-art.
Multi-Labelled Value Networks for Computer Go
Authors: Ti-Rong Wu, I-Chen Wu, Guan-Wun Chen, Ting-Han Wei, Tung-Yi Lai, Hung-Chun Wu, Li-Cheng Lan
Date: 2017-05-30 00:00:00

This paper proposes a new approach to a novel value network architecture for
the game Go, called a multi-labelled (ML) value network. In the ML value
network, different values (win rates) are trained simultaneously for different
settings of komi, a compensation given to balance the initiative of playing
first. The ML value network has three advantages, (a) it outputs values for
different komi, (b) it supports dynamic komi, and (c) it lowers the mean
squared error (MSE). This paper also proposes a new dynamic komi method to
improve game-playing strength. This paper also performs experiments to
demonstrate the merits of the architecture. First, the MSE of the ML value
network is generally lower than the value network alone. Second, the program
based on the ML value network wins by a rate of 67.6% against the program based
on the value network alone. Third, the program with the proposed dynamic komi
method significantly improves the playing strength over the baseline that does
not use dynamic komi, especially for handicap games. To our knowledge, up to
date, no handicap games have been played openly by programs using value
networks. This paper provides these programs with a useful approach to playing
handicap games.
ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification
Authors: Mark Marsden, Kevin McGuinness, Suzanne Little, Noel E. O'Connor
Date: 2017-05-30 00:00:00
Tasks: Crowd Counting, General Classification

In this paper we propose ResnetCrowd, a deep residual architecture for
simultaneous crowd counting, violent behaviour detection and crowd density
level classification. To train and evaluate the proposed multi-objective
technique, a new 100 image dataset referred to as Multi Task Crowd is
constructed. This new dataset is the first computer vision dataset fully
annotated for crowd counting, violent behaviour detection and density level
classification. Our experiments show that a multi-task approach boosts
individual task performance for all tasks and most notably for violent
behaviour detection which receives a 9\% boost in ROC curve AUC (Area under the
curve). The trained ResnetCrowd model is also evaluated on several additional
benchmarks highlighting the superior generalisation of crowd analysis models
trained for multiple objectives.
Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples
Authors: Weilin Xu, David Evans, Yanjun Qi
Date: 2017-05-30 00:00:00

Feature squeezing is a recently-introduced framework for mitigating and
detecting adversarial examples. In previous work, we showed that it is
effective against several earlier methods for generating adversarial examples.
In this short note, we report on recent results showing that simple feature
squeezing techniques also make deep learning models significantly more robust
against the Carlini/Wagner attacks, which are the best known adversarial
methods discovered to date.
Different approaches for identifying important concepts in probabilistic biomedical text summarization
Authors: Milad Moradi, Nasser Ghadiri
Date: 2016-05-10 00:00:00
Tasks: feature selection, Sentence, Text Summarization

Automatic text summarization tools help users in biomedical domain to acquire
their intended information from various textual resources more efficiently.
Some of the biomedical text summarization systems put the basis of their
sentence selection approach on the frequency of concepts extracted from the
input text. However, it seems that exploring other measures rather than the
frequency for identifying the valuable content of the input document, and
considering the correlations existing between concepts may be more useful for
this type of summarization. In this paper, we describe a Bayesian summarizer
for biomedical text documents. The Bayesian summarizer initially maps the input
text to the Unified Medical Language System (UMLS) concepts, then it selects
the important ones to be used as classification features. We introduce
different feature selection approaches to identify the most important concepts
of the text and to select the most informative content according to the
distribution of these concepts. We show that with the use of an appropriate
feature selection approach, the Bayesian biomedical summarizer can improve the
performance of summarization. We perform extensive evaluations on a corpus of
scientific papers in biomedical domain. The results show that the Bayesian
summarizer outperforms the biomedical summarizers that rely on the frequency of
concepts, the domain-independent and baseline methods based on the
Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. Moreover,
the results suggest that using the meaningfulness measure and considering the
correlations of concepts in the feature selection step lead to a significant
increase in the performance of summarization.
Multi-task Domain Adaptation for Sequence Tagging
Authors: Nanyun Peng, Mark Dredze
Date: 2016-08-09 00:00:00
Proceeding: WS 2017 8
Tasks: Chinese Word Segmentation, Domain Adaptation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Representation Learning

Many domain adaptation approaches rely on learning cross domain shared
representations to transfer the knowledge learned in one domain to other
domains. Traditional domain adaptation only considers adapting for one task. In
this paper, we explore multi-task representation learning under the domain
adaptation scenario. We propose a neural network framework that supports domain
adaptation for multiple tasks simultaneously, and learns shared representations
that better generalize for domain adaptation. We apply the proposed framework
to domain adaptation for sequence tagging problems considering two tasks:
Chinese word segmentation and named entity recognition. Experiments show that
multi-task domain adaptation works better than disjoint domain adaptation for
each task, and achieves the state-of-the-art results for both tasks in the
social media domain.
Discovering Visual Concept Structure with Sparse and Incomplete Tags
Authors: Jingya Wang, Xiatian Zhu, Shaogang Gong
Date: 2017-05-30 00:00:00
Tasks: Benchmarking, Clustering, TAG

Discovering automatically the semantic structure of tagged visual data (e.g.
web videos and images) is important for visual data analysis and
interpretation, enabling the machine intelligence for effectively processing
the fast-growing amount of multi-media data. However, this is non-trivial due
to the need for jointly learning underlying correlations between heterogeneous
visual and tag data. The task is made more challenging by inherently sparse and
incomplete tags. In this work, we develop a method for modelling the inherent
visual data concept structures based on a novel Hierarchical-Multi-Label Random
Forest model capable of correlating structured visual and tag information so as
to more accurately interpret the visual semantics, e.g. disclosing meaningful
visual groups with similar high-level concepts, and recovering missing tags for
individual visual data samples. Specifically, our model exploits hierarchically
structured tags of different semantic abstractness and multiple tag statistical
correlations in addition to modelling visual and tag interactions. As a result,
our model is able to discover more accurate semantic correlation between
textual tags and visual features, and finally providing favourable visual
semantics interpretation even with highly sparse and incomplete tags. We
demonstrate the advantages of our proposed approach in two fundamental
applications, visual data clustering and missing tag completion, on
benchmarking video (i.e. TRECVID MED 2011) and image (i.e. NUS-WIDE) datasets.
Fixed Points of Belief Propagation -- An Analysis via Polynomial Homotopy Continuation
Authors: Christian Knoll, Franz Pernkopf, Dhagash Mehta, Tianran Chen
Date: 2016-05-20 00:00:00

Belief propagation (BP) is an iterative method to perform approximate
inference on arbitrary graphical models. Whether BP converges and if the
solution is a unique fixed point depends on both the structure and the
parametrization of the model. To understand this dependence it is interesting
to find \emph{all} fixed points. In this work, we formulate a set of polynomial
equations, the solutions of which correspond to BP fixed points. To solve such
a nonlinear system we present the numerical polynomial-homotopy-continuation
(NPHC) method. Experiments on binary Ising models and on error-correcting codes
show how our method is capable of obtaining all BP fixed points. On Ising
models with fixed parameters we show how the structure influences both the
number of fixed points and the convergence properties. We further asses the
accuracy of the marginals and weighted combinations thereof. Weighting
marginals with their respective partition function increases the accuracy in
all experiments. Contrary to the conjecture that uniqueness of BP fixed points
implies convergence, we find graphs for which BP fails to converge, even though
a unique fixed point exists. Moreover, we show that this fixed point gives a
good approximation, and the NPHC method is able to obtain this fixed point.
Face Aging With Conditional Generative Adversarial Networks
Authors: Grigory Antipov, Moez Baccouche, Jean-Luc Dugelay
Date: 2017-02-07 00:00:00
Tasks: Age Estimation, Face Recognition

It has been recently shown that Generative Adversarial Networks (GANs) can
produce synthetic images of exceptional visual fidelity. In this work, we
propose the GAN-based method for automatic face aging. Contrary to previous
works employing GANs for altering of facial attributes, we make a particular
emphasize on preserving the original person's identity in the aged version of
his/her face. To this end, we introduce a novel approach for
"Identity-Preserving" optimization of GAN's latent vectors. The objective
evaluation of the resulting aged and rejuvenated face images by the
state-of-the-art face recognition and age estimation solutions demonstrate the
high potential of the proposed method.
Examining the Impact of Blur on Recognition by Convolutional Networks
Authors: Igor Vasiljevic, Ayan Chakrabarti, Gregory Shakhnarovich
Date: 2016-11-17 00:00:00

State-of-the-art algorithms for many semantic visual tasks are based on the
use of convolutional neural networks. These networks are commonly trained, and
evaluated, on large annotated datasets of artifact-free high-quality images. In
this paper, we investigate the effect of one such artifact that is quite common
in natural capture settings: optical blur. We show that standard network
models, trained only on high-quality images, suffer a significant degradation
in performance when applied to those degraded by blur due to defocus, or
subject or camera motion. We investigate the extent to which this degradation
is due to the mismatch between training and input image statistics.
Specifically, we find that fine-tuning a pre-trained model with blurred images
added to the training set allows it to regain much of the lost accuracy. We
also show that there is a fair amount of generalization between different
degrees and types of blur, which implies that a single network model can be
used robustly for recognition when the nature of the blur in the input is
unknown. We find that this robustness arises as a result of these models
learning to generate blur invariant representations in their hidden layers. Our
findings provide useful insights towards developing vision systems that can
perform reliably on real world images affected by blur.
Grammatical Inference as a Satisfiability Modulo Theories Problem
Authors: Rick Smetsers
Date: 2017-05-30 00:00:00

The problem of learning a minimal consistent model from a set of labeled
sequences of symbols is addressed from a satisfiability modulo theories
perspective. We present two encodings for deterministic finite automata and
extend one of these for Moore and Mealy machines. Our experimental results show
that these encodings improve upon the state-of-the-art, and are useful in
practice for learning small models.
Stochastic continuum armed bandit problem of few linear parameters in high dimensions
Authors: Hemant Tyagi, Sebastian Stich, Bernd Gärtner
Date: 2013-12-01 00:00:00

We consider a stochastic continuum armed bandit problem where the arms are
indexed by the $\ell_2$ ball $B_{d}(1+\nu)$ of radius $1+\nu$ in
$\mathbb{R}^d$. The reward functions $r :B_{d}(1+\nu) \rightarrow \mathbb{R}$
are considered to intrinsically depend on $k \ll d$ unknown linear parameters
so that $r(\mathbf{x}) = g(\mathbf{A} \mathbf{x})$ where $\mathbf{A}$ is a full
rank $k \times d$ matrix. Assuming the mean reward function to be smooth we
make use of results from low-rank matrix recovery literature and derive an
efficient randomized algorithm which achieves a regret bound of $O(C(k,d)
n^{\frac{1+k}{2+k}} (\log n)^{\frac{1}{2+k}})$ with high probability. Here
$C(k,d)$ is at most polynomial in $d$ and $k$ and $n$ is the number of rounds
or the sampling budget which is assumed to be known beforehand.
On Residual CNN in text-dependent speaker verification task
Authors: Egor Malykh, Sergey Novoselov, Oleg Kudashev
Date: 2017-05-29 00:00:00
Tasks: Speaker Verification, Text-Dependent Speaker Verification

Deep learning approaches are still not very common in the speaker
verification field. We investigate the possibility of using deep residual
convolutional neural network with spectrograms as an input features in the
text-dependent speaker verification task. Despite the fact that we were not
able to surpass the baseline system in quality, we achieved a quite good
results for such a new approach getting an 5.23% ERR on the RSR2015 evaluation
part. Fusion of the baseline and proposed systems outperformed the best
individual system by 18% relatively.
Understanding Probabilistic Sparse Gaussian Process Approximations
Authors: Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen
Date: 2016-06-15 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Gaussian Processes, regression

Good sparse approximations are essential for practical inference in Gaussian
Processes as the computational cost of exact methods is prohibitive for large
datasets. The Fully Independent Training Conditional (FITC) and the Variational
Free Energy (VFE) approximations are two recent popular methods. Despite
superficial similarities, these approximations have surprisingly different
theoretical properties and behave differently in practice. We thoroughly
investigate the two methods for regression both analytically and through
illustrative examples, and draw conclusions to guide practical application.
Nighttime sky/cloud image segmentation
Authors: Soumyabrata Dev, Florian M. Savoy, Yee Hui Lee, Stefan Winkler
Date: 2017-05-30 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Imaging the atmosphere using ground-based sky cameras is a popular approach
to study various atmospheric phenomena. However, it usually focuses on the
daytime. Nighttime sky/cloud images are darker and noisier, and thus harder to
analyze. An accurate segmentation of sky/cloud images is already challenging
because of the clouds' non-rigid structure and size, and the lower and less
stable illumination of the night sky increases the difficulty. Nonetheless,
nighttime cloud imaging is essential in certain applications, such as
continuous weather analysis and satellite communication.
  In this paper, we propose a superpixel-based method to segment nighttime
sky/cloud images. We also release the first nighttime sky/cloud image
segmentation database to the research community. The experimental results show
the efficacy of our proposed algorithm for nighttime images.
Multi-Focus Image Fusion Using Sparse Representation and Coupled Dictionary Learning
Authors: Farshad G. Veshki, Sergiy A. Vorobyov
Date: 2017-05-30 00:00:00
Tasks: Dictionary Learning, Multi Focus Image Fusion

We address the multi-focus image fusion problem, where multiple images captured with different focal settings are to be fused into an all-in-focus image of higher quality. Algorithms for this problem necessarily admit the source image characteristics along with focused and blurred features. However, most sparsity-based approaches use a single dictionary in focused feature space to describe multi-focus images, and ignore the representations in blurred feature space. We propose a multi-focus image fusion approach based on sparse representation using a coupled dictionary. It exploits the observations that the patches from a given training set can be sparsely represented by a couple of overcomplete dictionaries related to the focused and blurred categories of images and that a sparse approximation based on such coupled dictionary leads to a more flexible and therefore better fusion strategy than the one based on just selecting the sparsest representation in the original image estimate. In addition, to improve the fusion performance, we employ a coupled dictionary learning approach that enforces pairwise correlation between atoms of dictionaries learned to represent the focused and blurred feature spaces. We also discuss the advantages of the fusion approach based on coupled dictionary learning, and present efficient algorithms for fusion based on coupled dictionary learning. Extensive experimental comparisons with state-of-the-art multi-focus image fusion algorithms validate the effectiveness of the proposed approach.