pwc-archive/papers-with-abstracts huggingface dataset:
Convex Denoising using Non-Convex Tight Frame Regularization
Authors: Ankit Parekh, Ivan W. Selesnick
Date: 2015-04-04 00:00:00
Tasks: Denoising

This paper considers the problem of signal denoising using a sparse
tight-frame analysis prior. The L1 norm has been extensively used as a
regularizer to promote sparsity; however, it tends to under-estimate non-zero
values of the underlying signal. To more accurately estimate non-zero values,
we propose the use of a non-convex regularizer, chosen so as to ensure
convexity of the objective function. The convexity of the objective function is
ensured by constraining the parameter of the non-convex penalty. We use ADMM to
obtain a solution and show how to guarantee that ADMM converges to the global
optimum of the objective function. We illustrate the proposed method for 1D and
2D signal denoising.
PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples
Authors: Toby Dylan Hocking, Guillaume Bourque
Date: 2015-06-03 00:00:00
Tasks: Segmentation

Joint peak detection is a central problem when comparing samples in genomic
data analysis, but current algorithms for this task are unsupervised and
limited to at most 2 sample types. We propose PeakSegJoint, a new constrained
maximum likelihood segmentation model for any number of sample types. To select
the number of peaks in the segmentation, we propose a supervised penalty
learning model. To infer the parameters of these two models, we propose to use
a discrete optimization heuristic for the segmentation, and convex optimization
for the penalty learning. In comparisons with state-of-the-art peak detection
algorithms, PeakSegJoint achieves similar accuracy, faster speeds, and a more
interpretable model with overlapping peaks that occur in exactly the same
positions across all samples.
On the Stability of Deep Networks
Authors: Raja Giryes, Guillermo Sapiro, Alex M. Bronstein
Date: 2014-12-18 00:00:00

In this work we study the properties of deep neural networks (DNN) with
random weights. We formally prove that these networks perform a
distance-preserving embedding of the data. Based on this we then draw
conclusions on the size of the training data and the networks' structure. A
longer version of this paper with more results and details can be found in
(Giryes et al., 2015). In particular, we formally prove in the longer version
that DNN with random Gaussian weights perform a distance-preserving embedding
of the data, with a special treatment for in-class and out-of-class data.
A density compensation-based path computing model for measuring semantic similarity
Authors: Xinhua Zhu, Fei Li, Hongchao Chen, Qi Peng
Date: 2015-06-03 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

The shortest path between two concepts in a taxonomic ontology is commonly
used to represent the semantic distance between concepts in the edge-based
semantic similarity measures. In the past, the edge counting is considered to
be the default method for the path computation, which is simple, intuitive and
has low computational complexity. However, a large lexical taxonomy of such as
WordNet has the irregular densities of links between concepts due to its broad
domain but. The edge counting-based path computation is powerless for this
non-uniformity problem. In this paper, we advocate that the path computation is
able to be separated from the edge-based similarity measures and form various
general computing models. Therefore, in order to solve the problem of
non-uniformity of concept density in a large taxonomic ontology, we propose a
new path computing model based on the compensation of local area density of
concepts, which is equal to the number of direct hyponyms of the subsumers of
concepts in their shortest path. This path model considers the local area
density of concepts as an extension of the edge-based path and converts the
local area density divided by their depth into the compensation for edge-based
path with an adjustable parameter, which idea has been proven to be consistent
with the information theory. This model is a general path computing model and
can be applied in various edge-based similarity algorithms. The experiment
results show that the proposed path model improves the average correlation
between edge-based measures with human judgments on Miller and Charles
benchmark from less than 0.8 to more than 0.85, and has a big advantage in
efficiency than information content (IC) computation in a dynamic ontology,
thereby successfully solving the non-uniformity problem of taxonomic ontology.
High Dynamic Range Imaging by Perceptual Logarithmic Exposure Merging
Authors: Corneliu Florea, Constantin Vertan, Laura Florea
Date: 2014-11-02 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper we emphasize a similarity between the Logarithmic-Type Image
Processing (LTIP) model and the Naka-Rushton model of the Human Visual System
(HVS). LTIP is a derivation of the Logarithmic Image Processing (LIP), which
further replaces the logarithmic function with a ratio of polynomial functions.
Based on this similarity, we show that it is possible to present an unifying
framework for the High Dynamic Range (HDR) imaging problem, namely that
performing exposure merging under the LTIP model is equivalent to standard
irradiance map fusion. The resulting HDR algorithm is shown to provide high
quality in both subjective and objective evaluations.
Design of a Mobile Face Recognition System for Visually Impaired Persons
Authors: Shonal Chaudhry, Rohitash Chandra
Date: 2015-02-03 00:00:00
Tasks: Face Detection, Face Recognition

It is estimated that 285 million people globally are visually impaired. A
majority of these people live in developing countries and are among the elderly
population. One of the most difficult tasks faced by the visually impaired is
identification of people. While naturally, voice recognition is a common method
of identification, it is an intuitive and difficult process. The rise of
computation capability of mobile devices gives motivation to develop
applications that can assist visually impaired persons. With the availability
of mobile devices, these people can be assisted by an additional method of
identification through intelligent software based on computer vision
techniques. In this paper, we present the design and implementation of a face
detection and recognition system for the visually impaired through the use of
mobile computing. This mobile system is assisted by a server-based support
system. The system was tested on a custom video database. Experiment results
show high face detection accuracy and promising face recognition accuracy in
suitable conditions. The challenges of the system lie in better recognition
techniques for difficult situations in terms of lighting and weather.
Semantic Graph for Zero-Shot Learning
Authors: Zhen-Yong Fu, Tao Xiang, Shaogang Gong
Date: 2014-06-16 00:00:00
Tasks: Computational Efficiency, Transfer Learning, zero-shot-classification, Zero-Shot Learning

Zero-shot learning aims to classify visual objects without any training data
via knowledge transfer between seen and unseen classes. This is typically
achieved by exploring a semantic embedding space where the seen and unseen
classes can be related. Previous works differ in what embedding space is used
and how different classes and a test image can be related. In this paper, we
utilize the annotation-free semantic word space for the former and focus on
solving the latter issue of modeling relatedness. Specifically, in contrast to
previous work which ignores the semantic relationships between seen classes and
focus merely on those between seen and unseen classes, in this paper a novel
approach based on a semantic graph is proposed to represent the relationships
between all the seen and unseen class in a semantic word space. Based on this
semantic graph, we design a special absorbing Markov chain process, in which
each unseen class is viewed as an absorbing state. After incorporating one test
image into the semantic graph, the absorbing probabilities from the test data
to each unseen class can be effectively computed; and zero-shot classification
can be achieved by finding the class label with the highest absorbing
probability. The proposed model has a closed-form solution which is linear with
respect to the number of test images. We demonstrate the effectiveness and
computational efficiency of the proposed method over the state-of-the-arts on
the AwA (animals with attributes) dataset.
A Hybrid Model for Enhancing Lexical Statistical Machine Translation (SMT)
Authors: Ahmed G. M. ElSayed, Ahmed S. Salama, Alaa El-Din M. El-Ghazali
Date: 2015-06-03 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Sentence, Translation

The interest in statistical machine translation systems increases currently
due to political and social events in the world. A proposed Statistical Machine
Translation (SMT) based model that can be used to translate a sentence from the
source Language (English) to the target language (Arabic) automatically through
efficiently incorporating different statistical and Natural Language Processing
(NLP) models such as language model, alignment model, phrase based model,
reordering model, and translation model. These models are combined to enhance
the performance of statistical machine translation (SMT). Many implementation
tools have been used in this work such as Moses, Gizaa++, IRSTLM, KenLM, and
BLEU. Based on the implementation, evaluation of this model, and comparing the
generated translation with other implemented machine translation systems like
Google Translate, it was proved that this proposed model has enhanced the
results of the statistical machine translation, and forms a reliable and
efficient model in this field of research.
A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systems
Authors: Stefano V. Albrecht, Subramanian Ramamoorthy
Date: 2015-06-03 00:00:00

The ad hoc coordination problem is to design an autonomous agent which is
able to achieve optimal flexibility and efficiency in a multiagent system with
no mechanisms for prior coordination. We conceptualise this problem formally
using a game-theoretic model, called the stochastic Bayesian game, in which the
behaviour of a player is determined by its private information, or type. Based
on this model, we derive a solution, called Harsanyi-Bellman Ad Hoc
Coordination (HBA), which utilises the concept of Bayesian Nash equilibrium in
a planning procedure to find optimal actions in the sense of Bellman optimal
control. We evaluate HBA in a multiagent logistics domain called level-based
foraging, showing that it achieves higher flexibility and efficiency than
several alternative algorithms. We also report on a human-machine experiment at
a public science exhibition in which the human participants played repeated
Prisoner's Dilemma and Rock-Paper-Scissors against HBA and alternative
algorithms, showing that HBA achieves equal efficiency and a significantly
higher welfare and winning rate.
Color Image Retrieval Using Fuzzy Measure Hamming and S-Tree
Authors: Thanh The Van, Thanh Manh Le
Date: 2015-06-03 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

This chapter approaches the image retrieval system on the base of the colors
of image. It creates fuzzy signature to describe the color of image on color
space HSV and builds fuzzy Hamming distance (FHD) to evaluate the similarity
between the images. In order to reduce the storage space and speed up the
search of similar images, it aims to create S-tree to store fuzzy signature
relies on FHD and builds image retrieval algorithm on S-tree. Then, it provides
the content-based image retrieval (CBIR) and an image retrieval method on FHD
and S-tree. Last but not least, based on this theory, it also presents an
application and experimental assessment of the process of querying similar
image on the database system over 10,000 images.
Image Retrieval System Base on EMD Similarity Measure and S-Tree
Authors: Thanh Manh Le, Thanh The Van
Date: 2015-06-03 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

The paper approaches the binary signature for each image based on the
percentage of the pixels in each color images, at the same time the paper
builds a similar measure between images based on EMD (Earth Mover's Distance).
Besides, the paper proceeded to create the S-tree based on the similar measure
EMD to store the image's binary signatures to quickly query image signature
data. From there, the paper build an image retrieval algorithm and CBIR
(Content-Based Image Retrieval) based on a similar measure EMD and S-tree.
Based on this theory, the paper proceeded to build application and experimental
assessment of the process of querying image on the database system which have
over 10,000 images.
Towards Structured Deep Neural Network for Automatic Speech Recognition
Authors: Yi-Hsiu Liao, Hung-Yi Lee, Lin-shan Lee
Date: 2015-06-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In this paper we propose the Structured Deep Neural Network (Structured DNN)
as a structured and deep learning algorithm, learning to find the best
structured object (such as a label sequence) given a structured input (such as
a vector sequence) by globally considering the mapping relationships between
the structure rather than item by item.
  When automatic speech recognition is viewed as a special case of such a
structured learning problem, where we have the acoustic vector sequence as the
input and the phoneme label sequence as the output, it becomes possible to
comprehensively learned utterance by utterance as a whole, rather than frame by
frame.
  Structured Support Vector Machine (structured SVM) was proposed to perform
ASR with structured learning previously, but limited by the linear nature of
SVM. Here we propose structured DNN to use nonlinear transformations in
multi-layers as a structured and deep learning algorithm. It was shown to beat
structured SVM in preliminary experiments on TIMIT.
Do semidefinite relaxations solve sparse PCA up to the information limit?
Authors: Robert Krauthgamer, Boaz Nadler, Dan Vilenchik
Date: 2013-06-16 00:00:00

Estimating the leading principal components of data, assuming they are
sparse, is a central task in modern high-dimensional statistics. Many
algorithms were developed for this sparse PCA problem, from simple diagonal
thresholding to sophisticated semidefinite programming (SDP) methods. A key
theoretical question is under what conditions can such algorithms recover the
sparse principal components? We study this question for a single-spike model
with an $\ell_0$-sparse eigenvector, in the asymptotic regime as dimension $p$
and sample size $n$ both tend to infinity. Amini and Wainwright [Ann. Statist.
37 (2009) 2877-2921] proved that for sparsity levels $k\geq\Omega(n/\log p)$,
no algorithm, efficient or not, can reliably recover the sparse eigenvector. In
contrast, for $k\leq O(\sqrt{n/\log p})$, diagonal thresholding is consistent.
It was further conjectured that an SDP approach may close this gap between
computational and information limits. We prove that when
$k\geq\Omega(\sqrt{n})$, the proposed SDP approach, at least in its standard
usage, cannot recover the sparse spike. In fact, we conjecture that in the
single-spike model, no computationally-efficient algorithm can recover a spike
of $\ell_0$-sparsity $k\geq\Omega(\sqrt{n})$. Finally, we present empirical
results suggesting that up to sparsity levels $k=O(\sqrt{n})$, recovery is
possible by a simple covariance thresholding algorithm.
Innovated interaction screening for high-dimensional nonlinear classification
Authors: Yingying Fan, Yinfei Kong, Daoji Li, Zemin Zheng
Date: 2015-01-05 00:00:00
Tasks: Classification, General Classification, Vocal Bursts Intensity Prediction

This paper is concerned with the problems of interaction screening and
nonlinear classification in a high-dimensional setting. We propose a two-step
procedure, IIS-SQDA, where in the first step an innovated interaction screening
(IIS) approach based on transforming the original $p$-dimensional feature
vector is proposed, and in the second step a sparse quadratic discriminant
analysis (SQDA) is proposed for further selecting important interactions and
main effects and simultaneously conducting classification. Our IIS approach
screens important interactions by examining only $p$ features instead of all
two-way interactions of order $O(p^2)$. Our theory shows that the proposed
method enjoys sure screening property in interaction selection in the
high-dimensional setting of $p$ growing exponentially with the sample size. In
the selection and classification step, we establish a sparse inequality on the
estimated coefficient vector for QDA and prove that the classification error of
our procedure can be upper-bounded by the oracle classification error plus some
smaller order term. Extensive simulation studies and real data analysis show
that our proposal compares favorably with existing methods in interaction
selection and high-dimensional classification.
Understanding deep features with computer-generated imagery
Authors: Mathieu Aubry, Bryan Russell
Date: 2015-06-03 00:00:00
Proceeding: ICCV 2015 12

We introduce an approach for analyzing the variation of features generated by
convolutional neural networks (CNNs) with respect to scene factors that occur
in natural images. Such factors may include object style, 3D viewpoint, color,
and scene lighting configuration. Our approach analyzes CNN feature responses
corresponding to different scene factors by controlling for them via rendering
using a large database of 3D CAD models. The rendered images are presented to a
trained CNN and responses for different layers are studied with respect to the
input scene factors. We perform a decomposition of the responses based on
knowledge of the input scene factors and analyze the resulting components. In
particular, we quantify their relative importance in the CNN responses and
visualize them using principal component analysis. We show qualitative and
quantitative results of our study on three CNNs trained on large image
datasets: AlexNet, Places, and Oxford VGG. We observe important differences
across the networks and CNN layers for different scene factors and object
categories. Finally, we demonstrate that our analysis based on
computer-generated imagery translates to the network representation of natural
images.
A generalized back-door criterion
Authors: Marloes H. Maathuis, Diego Colombo
Date: 2013-07-22 00:00:00

We generalize Pearl's back-door criterion for directed acyclic graphs (DAGs)
to more general types of graphs that describe Markov equivalence classes of
DAGs and/or allow for arbitrarily many hidden variables. We also give easily
checkable necessary and sufficient graphical criteria for the existence of a
set of variables that satisfies our generalized back-door criterion, when
considering a single intervention and a single outcome variable. Moreover, if
such a set exists, we provide an explicit set that fulfills the criterion. We
illustrate the results in several examples. R-code is available in the
R-package pcalg.
A Distance-based Paraconsistent Semantics for DL-Lite
Authors: Xiaowang Zhang, Kewen Wang, Zhe Wang, Yue Ma, Guilin Qi
Date: 2013-01-09 00:00:00

DL-Lite is an important family of description logics. Recently, there is an
increasing interest in handling inconsistency in DL-Lite as the constraint
imposed by a TBox can be easily violated by assertions in ABox in DL-Lite. In
this paper, we present a distance-based paraconsistent semantics based on the
notion of feature in DL-Lite, which provides a novel way to rationally draw
meaningful conclusions even from an inconsistent knowledge base. Finally, we
investigate several important logical properties of this entailment relation
based on the new semantics and show its promising advantages in non-monotonic
reasoning for DL-Lite.
Robust and computationally feasible community detection in the presence of arbitrary outlier nodes
Authors: T. Tony Cai, Xiao-Dong Li
Date: 2014-04-23 00:00:00
Tasks: Clustering, Community Detection, Stochastic Block Model

Community detection, which aims to cluster $N$ nodes in a given graph into
$r$ distinct groups based on the observed undirected edges, is an important
problem in network data analysis. In this paper, the popular stochastic block
model (SBM) is extended to the generalized stochastic block model (GSBM) that
allows for adversarial outlier nodes, which are connected with the other nodes
in the graph in an arbitrary way. Under this model, we introduce a procedure
using convex optimization followed by $k$-means algorithm with $k=r$. Both
theoretical and numerical properties of the method are analyzed. A theoretical
guarantee is given for the procedure to accurately detect the communities with
small misclassification rate under the setting where the number of clusters can
grow with $N$. This theoretical result admits to the best-known result in the
literature of computationally feasible community detection in SBM without
outliers. Numerical results show that our method is both computationally fast
and robust to different kinds of outliers, while some popular computationally
fast community detection algorithms, such as spectral clustering applied to
adjacency matrices or graph Laplacians, may fail to retrieve the major clusters
due to a small portion of outliers. We apply a slight modification of our
method to a political blogs data set, showing that our method is competent in
practice and comparable to existing computationally feasible methods in the
literature. To the best of the authors' knowledge, our result is the first in
the literature in terms of clustering communities with fast growing numbers
under the GSBM where a portion of arbitrary outlier nodes exist.
Unsupervised domain adaption dictionary learning for visual recognition
Authors: Zhun Zhong, Zongmin Li, Runlin Li, Xiaoxia Sun
Date: 2015-06-03 00:00:00
Tasks: Dictionary Learning, Domain Adaptation

Over the last years, dictionary learning method has been extensively applied
to deal with various computer vision recognition applications, and produced
state-of-the-art results. However, when the data instances of a target domain
have a different distribution than that of a source domain, the dictionary
learning method may fail to perform well. In this paper, we address the
cross-domain visual recognition problem and propose a simple but effective
unsupervised domain adaption approach, where labeled data are only from source
domain. In order to bring the original data in source and target domain into
the same distribution, the proposed method forcing nearest coupled data between
source and target domain to have identical sparse representations while jointly
learning dictionaries for each domain, where the learned dictionaries can
reconstruct original data in source and target domain respectively. So that
sparse representations of original data can be used to perform visual
recognition tasks. We demonstrate the effectiveness of our approach on standard
datasets. Our method performs on par or better than competitive
state-of-the-art methods.