pwc-archive/papers-with-abstracts huggingface dataset:
A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model
Authors: Kenji Kawaguchi, Mauricio Araya
Date: 2013-03-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Bayesian Reinforcement Learning (RL) is capable of not only incorporating
domain knowledge, but also solving the exploration-exploitation dilemma in a
natural way. As Bayesian RL is intractable except for special cases, previous
work has proposed several approximation methods. However, these methods are
usually too sensitive to parameter values, and finding an acceptable parameter
setting is practically impossible in many applications. In this paper, we
propose a new algorithm that greedily approximates Bayesian RL to achieve
robustness in parameter space. We show that for a desired learning behavior,
our proposed algorithm has a polynomial sample complexity that is lower than
those of existing algorithms. We also demonstrate that the proposed algorithm
naturally outperforms other existing algorithms when the prior distributions
are not significantly misleading. On the other hand, the proposed algorithm
cannot handle greatly misspecified priors as well as the other algorithms can.
This is a natural consequence of the fact that the proposed algorithm is
greedier than the other algorithms. Accordingly, we discuss a way to select an
appropriate algorithm for different tasks based on the algorithms' greediness.
We also introduce a new way of simplifying Bayesian planning, based on which
future work would be able to derive new algorithms.
A Convergence Theorem for the Graph Shift-type Algorithms
Authors: Xuhui Fan, Longbing Cao
Date: 2013-06-13 00:00:00
Tasks: Vocal Bursts Type Prediction

Graph Shift (GS) algorithms are recently focused as a promising approach for
discovering dense subgraphs in noisy data. However, there are no theoretical
foundations for proving the convergence of the GS Algorithm. In this paper, we
propose a generic theoretical framework consisting of three key GS components:
simplex of generated sequence set, monotonic and continuous objective function
and closed mapping. We prove that GS algorithms with such components can be
transformed to fit the Zangwill's convergence theorem, and the sequence set
generated by the GS procedures always terminates at a local maximum, or at
worst, contains a subsequence which converges to a local maximum of the
similarity measure function. The framework is verified by expanding it to other
GS-type algorithms and experimental results.
Dynamic Infinite Mixed-Membership Stochastic Blockmodel
Authors: Xuhui Fan, Longbing Cao, Richard Yi Da Xu
Date: 2013-06-13 00:00:00

Directional and pairwise measurements are often used to model
inter-relationships in a social network setting. The Mixed-Membership
Stochastic Blockmodel (MMSB) was a seminal work in this area, and many of its
capabilities were extended since then. In this paper, we propose the
\emph{Dynamic Infinite Mixed-Membership stochastic blockModel (DIM3)}, a
generalised framework that extends the existing work to a potentially infinite
number of communities and mixture memberships for each of the network's nodes.
This model is in a dynamic setting, where additional model parameters are
introduced to reflect the degree of persistence between one's memberships at
consecutive times. Accordingly, two effective posterior sampling strategies and
their results are presented using both synthetic and real data.
A Linear Approximation to the chi^2 Kernel with Geometric Convergence
Authors: Fuxin Li, Guy Lebanon, Cristian Sminchisescu
Date: 2012-06-18 00:00:00
Tasks: General Classification, image-classification, Image Classification, Segmentation, Semantic Segmentation

We propose a new analytical approximation to the $\chi^2$ kernel that
converges geometrically. The analytical approximation is derived with
elementary methods and adapts to the input distribution for optimal convergence
rate. Experiments show the new approximation leads to improved performance in
image classification and semantic segmentation tasks using a random Fourier
feature approximation of the $\exp-\chi^2$ kernel. Besides, out-of-core
principal component analysis (PCA) methods are introduced to reduce the
dimensionality of the approximation and achieve better performance at the
expense of only an additional constant factor to the time complexity. Moreover,
when PCA is performed jointly on the training and unlabeled testing data,
further performance improvements can be obtained. Experiments conducted on the
PASCAL VOC 2010 segmentation and the ImageNet ILSVRC 2010 datasets show
statistically significant improvements over alternative approximation methods.
Reinforcement learning with restrictions on the action set
Authors: Mario Bravo, Mathieu Faure
Date: 2013-06-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Consider a 2-player normal-form game repeated over time. We introduce an
adaptive learning procedure, where the players only observe their own realized
payoff at each stage. We assume that agents do not know their own payoff
function, and have no information on the other player. Furthermore, we assume
that they have restrictions on their own action set such that, at each stage,
their choice is limited to a subset of their action set. We prove that the
empirical distributions of play converge to the set of Nash equilibria for
zero-sum and potential games, and games where one player has two actions.
Robust Support Vector Machines for Speaker Verification Task
Authors: Kawthar Yasmine Zergat, Abderrahmane Amrouche
Date: 2013-06-12 00:00:00
Tasks: Dimensionality Reduction, Speaker Verification

An important step in speaker verification is extracting features that best
characterize the speaker voice. This paper investigates a front-end processing
that aims at improving the performance of speaker verification based on the
SVMs classifier, in text independent mode. This approach combines features
based on conventional Mel-cepstral Coefficients (MFCCs) and Line Spectral
Frequencies (LSFs) to constitute robust multivariate feature vectors. To reduce
the high dimensionality required for training these feature vectors, we use a
dimension reduction method called principal component analysis (PCA). In order
to evaluate the robustness of these systems, different noisy environments have
been used. The obtained results using TIMIT database showed that, using the
paradigm that combines these spectral cues leads to a significant improvement
in verification accuracy, especially with PCA reduction for low signal-to-noise
ratio noisy environment.
Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy
Authors: Catarina Moreira, Andreas Wichert
Date: 2013-06-12 00:00:00
Tasks: Information Retrieval, Retrieval

Expert finding is an information retrieval task concerned with the search for
the most knowledgeable people, in some topic, with basis on documents
describing peoples activities. The task involves taking a user query as input
and returning a list of people sorted by their level of expertise regarding the
user query. This paper introduces a novel approach for combining multiple
estimators of expertise based on a multisensor data fusion framework together
with the Dempster-Shafer theory of evidence and Shannon's entropy. More
specifically, we defined three sensors which detect heterogeneous information
derived from the textual contents, from the graph structure of the citation
patterns for the community of experts, and from profile information about the
academic experts. Given the evidences collected, each sensor may define
different candidates as experts and consequently do not agree in a final
ranking decision. To deal with these conflicts, we applied the Dempster-Shafer
theory of evidence combined with Shannon's Entropy formula to fuse this
information and come up with a more accurate and reliable final ranking list.
Experiments made over two datasets of academic publications from the Computer
Science domain attest for the adequacy of the proposed approach over the
traditional state of the art approaches. We also made experiments against
representative supervised state of the art algorithms. Results revealed that
the proposed method achieved a similar performance when compared to these
supervised techniques, confirming the capabilities of the proposed framework.
Random Drift Particle Swarm Optimization
Authors: Jun Sun, Xiao-Jun Wu, Vasile Palade, Wei Fang, Yuhui Shi
Date: 2013-06-12 00:00:00

The random drift particle swarm optimization (RDPSO) algorithm, inspired by
the free electron model in metal conductors placed in an external electric
field, is presented, systematically analyzed and empirically studied in this
paper. The free electron model considers that electrons have both a thermal and
a drift motion in a conductor that is placed in an external electric field. The
motivation of the RDPSO algorithm is described first, and the velocity equation
of the particle is designed by simulating the thermal motion as well as the
drift motion of the electrons, both of which lead the electrons to a location
with minimum potential energy in the external electric field. Then, a
comprehensive analysis of the algorithm is made, in order to provide a deep
insight into how the RDPSO algorithm works. It involves a theoretical analysis
and the simulation of the stochastic dynamical behavior of a single particle in
the RDPSO algorithm. The search behavior of the algorithm itself is also
investigated in detail, by analyzing the interaction between the particles.
Some variants of the RDPSO algorithm are proposed by incorporating different
random velocity components with different neighborhood topologies. Finally,
empirical studies on the RDPSO algorithm are performed by using a set of
benchmark functions from the CEC2005 benchmark suite. Based on the theoretical
analysis of the particle's behavior, two methods of controlling the algorithmic
parameters are employed, followed by an experimental analysis on how to select
the parameter values, in order to obtain a good overall performance of the
RDPSO algorithm and its variants in real-world applications. A further
performance comparison between the RDPSO algorithms and other variants of PSO
is made to prove the efficiency of the RDPSO algorithms.
The Quantum Challenge in Concept Theory and Natural Language Processing
Authors: Diederik Aerts, Jan Broekaert, Sandro Sozzo, Tomas Veloz
Date: 2013-06-12 00:00:00
Tasks: Information Retrieval, Retrieval

The mathematical formalism of quantum theory has been successfully used in
human cognition to model decision processes and to deliver representations of
human knowledge. As such, quantum cognition inspired tools have improved
technologies for Natural Language Processing and Information Retrieval. In this
paper, we overview the quantum cognition approach developed in our Brussels
team during the last two decades, specifically our identification of quantum
structures in human concepts and language, and the modeling of data from
psychological and corpus-text-based experiments. We discuss our
quantum-theoretic framework for concepts and their conjunctions/disjunctions in
a Fock-Hilbert space structure, adequately modeling a large amount of data
collected on concept combinations. Inspired by this modeling, we put forward
elements for a quantum contextual and meaning-based approach to information
technologies in which 'entities of meaning' are inversely reconstructed from
texts, which are considered as traces of these entities' states.
Recurrent Convolutional Neural Networks for Scene Parsing
Authors: Pedro H. O. Pinheiro, Ronan Collobert
Date: 2013-06-12 00:00:00
Tasks: Scene Parsing

Scene parsing is a technique that consist on giving a label to all pixels in
an image according to the class they belong to. To ensure a good visual
coherence and a high class accuracy, it is essential for a scene parser to
capture image long range dependencies. In a feed-forward architecture, this can
be simply achieved by considering a sufficiently large input context patch,
around each pixel to be labeled. We propose an approach consisting of a
recurrent convolutional neural network which allows us to consider a large
input context, while limiting the capacity of the model. Contrary to most
standard approaches, our method does not rely on any segmentation methods, nor
any task-specific features. The system is trained in an end-to-end manner over
raw pixels, and models complex spatial dependencies with low inference cost. As
the context size increases with the built-in recurrence, the system identifies
and corrects its own errors. Our approach yields state-of-the-art performance
on both the Stanford Background Dataset and the SIFT Flow Dataset, while
remaining very fast at test time.
Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks
Authors: Dimitri Palaz, Ronan Collobert, Mathew Magimai. -Doss
Date: 2013-04-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Phoneme Recognition, speech-recognition, Speech Recognition

In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic
speech recognition (ASR) system, the phoneme class conditional probabilities
are estimated by first extracting acoustic features from the speech signal
based on prior knowledge such as, speech perception or/and speech production
knowledge, and, then modeling the acoustic features with an ANN. Recent
advances in machine learning techniques, more specifically in the field of
image processing and text processing, have shown that such divide and conquer
strategy (i.e., separating feature extraction and modeling steps) may not be
necessary. Motivated from these studies, in the framework of convolutional
neural networks (CNNs), this paper investigates a novel approach, where the
input to the ANN is raw speech signal and the output is phoneme class
conditional probability estimates. On TIMIT phoneme recognition task, we study
different ANN architectures to show the benefit of CNNs and compare the
proposed approach against conventional approach where, spectral-based feature
MFCC is extracted and modeled by a multilayer perceptron. Our studies show that
the proposed approach can yield comparable or better phoneme recognition
performance when compared to the conventional approach. It indicates that CNNs
can learn features relevant for phoneme classification automatically from the
raw speech signal.
Horizontal and Vertical Ensemble with Deep Representation for Classification
Authors: Jingjing Xie, Bing Xu, Zhang Chuang
Date: 2013-06-12 00:00:00
Tasks: Classification, General Classification, Representation Learning

Representation learning, especially which by using deep learning, has been
widely applied in classification. However, how to use limited size of labeled
data to achieve good classification performance with deep neural network, and
how can the learned features further improve classification remain indefinite.
In this paper, we propose Horizontal Voting Vertical Voting and Horizontal
Stacked Ensemble methods to improve the classification performance of deep
neural networks. In the ICML 2013 Black Box Challenge, via using these methods
independently, Bing Xu achieved 3rd in public leaderboard, and 7th in private
leaderboard; Jingjing Xie achieved 4th in public leaderboard, and 5th in
private leaderboard.
Sparse Representation-based Image Quality Assessment
Authors: Tanaya Guha, Ehsan Nezhadarya, Rabab K. Ward
Date: 2013-06-12 00:00:00
Tasks: Image Quality Assessment

A successful approach to image quality assessment involves comparing the
structural information between a distorted and its reference image. However,
extracting structural information that is perceptually important to our visual
system is a challenging task. This paper addresses this issue by employing a
sparse representation-based approach and proposes a new metric called the
\emph{sparse representation-based quality} (SPARQ) \emph{index}. The proposed
method learns the inherent structures of the reference image as a set of basis
vectors, such that any structure in the image can be represented by a linear
combination of only a few of those basis vectors. This sparse strategy is
employed because it is known to generate basis vectors that are qualitatively
similar to the receptive field of the simple cells present in the mammalian
primary visual cortex. The visual quality of the distorted image is estimated
by comparing the structures of the reference and the distorted images in terms
of the learnt basis vectors resembling cortical cells. Our approach is
evaluated on six publicly available subject-rated image quality assessment
datasets. The proposed SPARQ index consistently exhibits high correlation with
the subjective ratings on all datasets and performs better or at par with the
state-of-the-art.
Distance Majorization and Its Applications
Authors: Eric C. Chi, Hua Zhou, Kenneth Lange
Date: 2012-11-16 00:00:00

The problem of minimizing a continuously differentiable convex function over
an intersection of closed convex sets is ubiquitous in applied mathematics. It
is particularly interesting when it is easy to project onto each separate set,
but nontrivial to project onto their intersection. Algorithms based on Newton's
method such as the interior point method are viable for small to medium-scale
problems. However, modern applications in statistics, engineering, and machine
learning are posing problems with potentially tens of thousands of parameters
or more. We revisit this convex programming problem and propose an algorithm
that scales well with dimensionality. Our proposal is an instance of a
sequential unconstrained minimization technique and revolves around three
ideas: the majorization-minimization (MM) principle, the classical penalty
method for constrained optimization, and quasi-Newton acceleration of
fixed-point algorithms. The performance of our distance majorization algorithms
is illustrated in several applications.
Large Margin Low Rank Tensor Analysis
Authors: Guoqiang Zhong, Mohamed Cheriet
Date: 2013-06-11 00:00:00
Tasks: Face Recognition, Object Recognition

Other than vector representations, the direct objects of human cognition are
generally high-order tensors, such as 2D images and 3D textures. From this
fact, two interesting questions naturally arise: How does the human brain
represent these tensor perceptions in a "manifold" way, and how can they be
recognized on the "manifold"? In this paper, we present a supervised model to
learn the intrinsic structure of the tensors embedded in a high dimensional
Euclidean space. With the fixed point continuation procedures, our model
automatically and jointly discovers the optimal dimensionality and the
representations of the low dimensional embeddings. This makes it an effective
simulation of the cognitive process of human brain. Furthermore, the
generalization of our model based on similarity between the learned low
dimensional embeddings can be viewed as counterpart of recognition of human
brain. Experiments on applications for object recognition and face recognition
demonstrate the superiority of our proposed model over state-of-the-art
approaches.
Stopping Criterion for the Mean Shift Iterative Algorithm
Authors: Yasel Garcés Suárez, Esley Torres, Osvaldo Pereira, Claudia Pérez, Roberto Rogríguez
Date: 2013-06-11 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation is a critical step in computer vision tasks constituting
an essential issue for pattern recognition and visual interpretation. In this
paper, we propose a new stopping criterion for the mean shift iterative
algorithm by using images defined in Zn ring, with the goal of reaching a
better segmentation. We carried out also a study on the weak and strong of
equivalence classes between two images. An analysis on the convergence with
this new stopping criterion is carried out too.
Hand Gesture Recognition Based on Karhunen-Loeve Transform
Authors: Joyeeta Singha, Karen Das
Date: 2013-06-11 00:00:00
Tasks: Edge Detection, General Classification, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

In this paper, we have proposed a system based on K-L Transform to recognize different hand gestures. The system consists of five steps: skin filtering, palm cropping, edge detection, feature extraction, and classification. Firstly the hand is detected using skin filtering and palm cropping was performed to extract out only the palm portion of the hand. The extracted image was then processed using the Canny Edge Detection technique to extract the outline images of palm. After palm extraction, the features of hand were extracted using K-L Transform technique and finally the input gesture was recognized using proper classifier. In our system, we have tested for 10 different hand gestures, and recognizing rate obtained was 96%. Hence we propose an easy approach to recognize different hand gestures.
A Perceptual Alphabet for the 10-dimensional Phonetic-prosodic Space
Authors: Elaine Y L Tsiang
Date: 2013-06-11 00:00:00

We define an alphabet, the IHA, of the 10-D phonetic-prosodic space. The dimensions of this space are perceptual observables, rather than articulatory specifications. Speech is defined as a random chain in time of the 4-D phonetic subspace, that is, a symbolic sequence, augmented with diacritics of the remaining 6-D prosodic subspace. The definitions here are based on the model of speech of oral billiards, and supersedes an earlier version. This paper only enumerates the IHA in detail as a supplement to the exposition of oral billiards in a separate paper. The IHA has been implemented as the target random variable in a speech recognizer.
A biological gradient descent for prediction through a combination of STDP and homeostatic plasticity
Authors: Mathieu Galtier, Gilles Wainrib
Date: 2012-06-21 00:00:00

Identifying, formalizing and combining biological mechanisms which implement
known brain functions, such as prediction, is a main aspect of current research
in theoretical neuroscience. In this letter, the mechanisms of Spike Timing
Dependent Plasticity (STDP) and homeostatic plasticity, combined in an original
mathematical formalism, are shown to shape recurrent neural networks into
predictors. Following a rigorous mathematical treatment, we prove that they
implement the online gradient descent of a distance between the network
activity and its stimuli. The convergence to an equilibrium, where the network
can spontaneously reproduce or predict its stimuli, does not suffer from
bifurcation issues usually encountered in learning in recurrent neural
networks.