pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination
Authors: Qibin Zhao, Liqing Zhang, Andrzej Cichocki
Date: 2014-01-25 00:00:00
Tasks: Bayesian Inference, Image Generation, Image Inpainting

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful
technique for tensor completion through explicitly capturing the multilinear
latent factors. The existing CP algorithms require the tensor rank to be
manually specified, however, the determination of tensor rank remains a
challenging problem especially for CP rank. In addition, existing approaches do
not take into account uncertainty information of latent factors, as well as
missing entries. To address these issues, we formulate CP factorization using a
hierarchical probabilistic model and employ a fully Bayesian treatment by
incorporating a sparsity-inducing prior over multiple latent factors and the
appropriate hyperpriors over all hyperparameters, resulting in automatic rank
determination. To learn the model, we develop an efficient deterministic
Bayesian inference algorithm, which scales linearly with data size. Our method
is characterized as a tuning parameter-free approach, which can effectively
infer underlying multilinear factors with a low-rank constraint, while also
providing predictive distributions over missing entries. Extensive simulations
on synthetic data illustrate the intrinsic capability of our method to recover
the ground-truth of CP rank and prevent the overfitting problem, even when a
large amount of entries are missing. Moreover, the results from real-world
applications, including image inpainting and facial image synthesis,
demonstrate that our method outperforms state-of-the-art approaches for both
tensor factorization and tensor completion in terms of predictive performance.
Recognition of cDNA microarray image Using Feedforward artificial neural network
Authors: R. M. Farouk, S. Badr, M. Sayed Elahl
Date: 2014-10-09 00:00:00

The complementary DNA (cDNA) sequence is considered to be the magic biometric
technique for personal identification. In this paper, we present a new method
for cDNA recognition based on the artificial neural network (ANN). Microarray
imaging is used for the concurrent identification of thousands of genes. We
have segmented the location of the spots in a cDNA microarray. Thus, a precise
localization and segmenting of a spot are essential to obtain a more accurate
intensity measurement, leading to a more precise expression measurement of a
gene. The segmented cDNA microarray image is resized and it is used as an input
for the proposed artificial neural network. For matching and recognition, we
have trained the artificial neural network. Recognition results are given for
the galleries of cDNA sequences . The numerical results show that, the proposed
matching technique is an effective in the cDNA sequences process. We also
compare our results with previous results and find out that, the proposed
technique is an effective matching performance.
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
Authors: L. Jacques, C. De Vleeschouwer, Y. Boursier, P. Sudhakar, C. De Mol, A. Pizurica, S. Anthoine, P. Vandergheynst, P. Frossard, C. Bilen, S. Kitic, N. Bertin, R. Gribonval, N. Boumal, B. Mishra, P. -A. Absil, R. Sepulchre, S. Bundervoet, C. Schretter, A. Dooms, P. Schelkens, O. Chabiron, F. Malgouyres, J. -Y. Tourneret, N. Dobigeon, P. Chainais, C. Richard, B. Cornelis, I. Daubechies, D. Dunson, M. Dankova, P. Rajmic, K. Degraux, V. Cambareri, B. Geelen, G. Lafruit, G. Setti, J. -F. Determe, J. Louveaux, F. Horlin, A. Drémeau, P. Heas, C. Herzet, V. Duval, G. Peyré, A. Fawzi, M. Davies, N. Gillis, S. A. Vavasis, C. Soussen, L. Le Magoarou, J. Liang, J. Fadili, A. Liutkus, D. Martina, S. Gigan, L. Daudet, M. Maggioni, S. Minsker, N. Strawn, C. Mory, F. Ngole, J. -L. Starck, I. Loris, S. Vaiter, M. Golbabaee, D. Vukobratovic
Date: 2014-10-02 00:00:00
Tasks: Dictionary Learning

The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.
Automatic Training Data Synthesis for Handwriting Recognition Using the Structural Crossing-Over Technique
Authors: Sirisak Visessenee, Sanparith Marukatat, Rachada Kongkachandra
Date: 2014-10-09 00:00:00
Tasks: Handwriting Recognition

The paper presents a novel technique called "Structural Crossing-Over" to
synthesize qualified data for training machine learning-based handwriting
recognition. The proposed technique can provide a greater variety of patterns
of training data than the existing approaches such as elastic distortion and
tangent-based affine transformation. A couple of training characters are
chosen, then they are analyzed by their similar and different structures, and
finally are crossed over to generate the new characters. The experiments are
set to compare the performances of tangent-based affine transformation and the
proposed approach in terms of the variety of generated characters and percent
of recognition errors. The standard MNIST corpus including 60,000 training
characters and 10,000 test characters is employed in the experiments. The
proposed technique uses 1,000 characters to synthesize 60,000 characters, and
then uses these data to train and test the benchmark handwriting recognition
system that exploits Histogram of Gradient (HOG) as features and Support Vector
Machine (SVM) as recognizer. The experimental result yields 8.06% of errors. It
significantly outperforms the tangent-based affine transformation and the
original MNIST training data, which are 11.74% and 16.55%, respectively.
Generalization Analysis for Game-Theoretic Machine Learning
Authors: Haifang Li, Fei Tian, Wei Chen, Tao Qin, Tie-Yan Liu
Date: 2014-10-09 00:00:00
Tasks: BIG-bench Machine Learning

For Internet applications like sponsored search, cautions need to be taken
when using machine learning to optimize their mechanisms (e.g., auction) since
self-interested agents in these applications may change their behaviors (and
thus the data distribution) in response to the mechanisms. To tackle this
problem, a framework called game-theoretic machine learning (GTML) was recently
proposed, which first learns a Markov behavior model to characterize agents'
behaviors, and then learns the optimal mechanism by simulating agents' behavior
changes in response to the mechanism. While GTML has demonstrated practical
success, its generalization analysis is challenging because the behavior data
are non-i.i.d. and dependent on the mechanism. To address this challenge,
first, we decompose the generalization error for GTML into the behavior
learning error and the mechanism learning error; second, for the behavior
learning error, we obtain novel non-asymptotic error bounds for both parametric
and non-parametric behavior learning methods; third, for the mechanism learning
error, we derive a uniform convergence bound based on a new concept called
nested covering number of the mechanism space and the generalization analysis
techniques developed for mixing sequences. To the best of our knowledge, this
is the first work on the generalization analysis of GTML, and we believe it has
general implications to the theoretical analysis of other complicated machine
learning problems.
A Scalable, Lexicon Based Technique for Sentiment Analysis
Authors: Chetan Kaushik, Atul Mishra
Date: 2014-10-08 00:00:00
Tasks: Opinion Mining, Sentiment Analysis

Rapid increase in the volume of sentiment rich social media on the web has
resulted in an increased interest among researchers regarding Sentimental
Analysis and opinion mining. However, with so much social media available on
the web, sentiment analysis is now considered as a big data task. Hence the
conventional sentiment analysis approaches fails to efficiently handle the vast
amount of sentiment data available now a days. The main focus of the research
was to find such a technique that can efficiently perform sentiment analysis on
big data sets. A technique that can categorize the text as positive, negative
and neutral in a fast and accurate manner. In the research, sentiment analysis
was performed on a large data set of tweets using Hadoop and the performance of
the technique was measured in form of speed and accuracy. The experimental
results shows that the technique exhibits very good efficiency in handling big
sentiment data sets.
Joint Estimation of Multiple Graphical Models from High Dimensional Time Series
Authors: Huitong Qiu, Fang Han, Han Liu, Brian Caffo
Date: 2013-11-01 00:00:00
Tasks: parameter estimation, Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

In this manuscript we consider the problem of jointly estimating multiple
graphical models in high dimensions. We assume that the data are collected from
n subjects, each of which consists of T possibly dependent observations. The
graphical models of subjects vary, but are assumed to change smoothly
corresponding to a measure of closeness between subjects. We propose a kernel
based method for jointly estimating all graphical models. Theoretically, under
a double asymptotic framework, where both (T,n) and the dimension d can
increase, we provide the explicit rate of convergence in parameter estimation.
It characterizes the strength one can borrow across different individuals and
impact of data dependence on parameter estimation. Empirically, experiments on
both synthetic and real resting state functional magnetic resonance imaging
(rs-fMRI) data illustrate the effectiveness of the proposed method.
Committment-Based Data-Aware Multi-Agent-Contexts Systems
Authors: Stefania Costantini
Date: 2014-10-08 00:00:00

Communication and interaction among agents have been the subject of extensive
investigation since many years. Commitment-based communication, where
communicating agents are seen as a debtor agent who is committed to a creditor
agent to bring about something (possibly under some conditions) is now very
well-established. The approach of DACMAS (Data-Aware Commitment-based MAS)
lifts commitment-related approaches proposed in the literature from a
propositional to a first-order setting via the adoption the DRL-Lite
Description Logic. Notably, DACMASs provide, beyond commitments, simple forms
of inter-agent event-based communication. Yet, the aspect is missing of making
a MAS able to acquire knowledge from contexts which are not agents and which
are external to the MAS. This topic is coped with in Managed MCSs (Managed
Multi-Context Systems), where however exchanges are among knowledge bases and
not agents. In this paper, we propose the new approach of DACmMCMASs
(Data-Aware Commitment-based managed Multi- Context MAS), so as to obtain a
commitment-based first-order agent system which is able to interact with
heterogeneous external information sources. We show that DACmMCMASs retain the
nice formal properties of the original approaches.
An improved multimodal PSO method based on electrostatic interaction using n- nearest-neighbor local search
Authors: Taymaz Rahkar-Farshi, Sara Behjat-Jamal, Mohammad-Reza Feizi-Derakhshi
Date: 2014-10-08 00:00:00

In this paper, an improved multimodal optimization (MMO) algorithm,called
LSEPSO,has been proposed. LSEPSO combined Electrostatic Particle Swarm
Optimization (EPSO) algorithm and a local search method and then made some
modification on them. It has been shown to improve global and local optima
finding ability of the algorithm. This algorithm useda modified local search to
improve particle's personal best, which used n-nearest-neighbour instead of
nearest-neighbour. Then, by creating n new points among each particle and n
nearest particles, it tried to find a point which could be the alternative of
particle's personal best. This method prevented particle's attenuation and
following a specific particle by its neighbours. The performed tests on a
number of benchmark functions clearly demonstrated that the improved algorithm
is able to solve MMO problems and outperform other tested algorithms in this
article.
Bayesian tracking and parameter learning for non-linear multiple target tracking models
Authors: Lan Jiang, Sumeetpal S. Singh, Sinan Yildirim
Date: 2014-10-08 00:00:00

We propose a new Bayesian tracking and parameter learning algorithm for
non-linear non-Gaussian multiple target tracking (MTT) models. We design a
Markov chain Monte Carlo (MCMC) algorithm to sample from the posterior
distribution of the target states, birth and death times, and association of
observations to targets, which constitutes the solution to the tracking
problem, as well as the model parameters. In the numerical section, we present
performance comparisons with several competing techniques and demonstrate
significant performance improvements in all cases.
Supervised learning Methods for Bangla Web Document Categorization
Authors: Ashis Kumar Mandal, Rikta Sen
Date: 2014-10-08 00:00:00
Tasks: Text Categorization

This paper explores the use of machine learning approaches, or more
specifically, four supervised learning Methods, namely Decision Tree(C 4.5),
K-Nearest Neighbour (KNN), Na\"ive Bays (NB), and Support Vector Machine (SVM)
for categorization of Bangla web documents. This is a task of automatically
sorting a set of documents into categories from a predefined set. Whereas a
wide range of methods have been applied to English text categorization,
relatively few studies have been conducted on Bangla language text
categorization. Hence, we attempt to analyze the efficiency of those four
methods for categorization of Bangla documents. In order to validate, Bangla
corpus from various websites has been developed and used as examples for the
experiment. For Bangla, empirical results support that all four methods produce
satisfactory performance with SVM attaining good result in terms of high
dimensional and relatively noisy document feature vectors.
Deep Learning in Neural Networks: An Overview
Authors: Juergen Schmidhuber
Date: 2014-04-30 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, deep artificial neural networks (including recurrent ones)
have won numerous contests in pattern recognition and machine learning. This
historical survey compactly summarises relevant work, much of it from the
previous millennium. Shallow and deep learners are distinguished by the depth
of their credit assignment paths, which are chains of possibly learnable,
causal links between actions and effects. I review deep supervised learning
(also recapitulating the history of backpropagation), unsupervised learning,
reinforcement learning & evolutionary computation, and indirect search for
short programs encoding deep and large networks.
Analysis of corporate environmental reports using statistical techniques and data mining
Authors: J. R. Modapothala, B. Issac
Date: 2014-10-08 00:00:00

Measuring the effectiveness of corporate environmental reports, it being
highly qualitative and less regulated, is often considered as a daunting task.
The task becomes more complex if comparisons are to be performed. This study is
undertaken to overcome the physical verification problems by implementing data
mining technique. It further explores on the effectiveness by performing
exploratory analysis and structural equation model to bring out the significant
linkages between the selected 10 variables. Samples of five hundred and thirty
nine reports across various countries are used from an international directory
to perform the statistical analysis like: One way ANOVA (Analysis of Variance),
MDA (Multivariate Discriminant Analysis) and SEM (Structural Equation
Modeling). The results indicate the significant differences among the various
types of industries in their environmental reporting, and the exploratory
factors like stakeholder, organization strategy and industrial oriented
factors, proved significant. The major accomplishment is that the findings
correlate with the conceptual frame work of GRI.
Projecting Ising Model Parameters for Fast Mixing
Authors: Justin Domke, Xianghang Liu
Date: 2014-07-03 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: model

Inference in general Ising models is difficult, due to high treewidth making
tree-based algorithms intractable. Moreover, when interactions are strong,
Gibbs sampling may take exponential time to converge to the stationary
distribution. We present an algorithm to project Ising model parameters onto a
parameter set that is guaranteed to be fast mixing, under several divergences.
We find that Gibbs sampling using the projected parameters is more accurate
than with the original parameters when interaction strengths are strong and
when limited time is available for sampling.
GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstract
Authors: QI, Yu, Xinran He, Yan Liu
Date: 2014-10-07 00:00:00
Tasks: Anomaly Detection, Group Anomaly Detection

Traditional anomaly detection on social media mostly focuses on individual
point anomalies while anomalous phenomena usually occur in groups. Therefore it
is valuable to study the collective behavior of individuals and detect group
anomalies. Existing group anomaly detection approaches rely on the assumption
that the groups are known, which can hardly be true in real world social media
applications. In this paper, we take a generative approach by proposing a
hierarchical Bayes model: Group Latent Anomaly Detection (GLAD) model. GLAD
takes both pair-wise and point-wise data as input, automatically infers the
groups and detects group anomalies simultaneously. To account for the dynamic
properties of the social media data, we further generalize GLAD to its dynamic
extension d-GLAD. We conduct extensive experiments to evaluate our models on
both synthetic and real world datasets. The empirical results demonstrate that
our approach is effective and robust in discovering latent groups and detecting
group anomalies.
Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation
Authors: Jean Pouget-Abadie, Dzmitry Bahdanau, Bart van Merrienboer, Kyunghyun Cho, Yoshua Bengio
Date: 2014-09-03 00:00:00
Proceeding: WS 2014 10
Tasks: Machine Translation, Sentence, Translation

The authors of (Cho et al., 2014a) have shown that the recently introduced
neural network translation systems suffer from a significant drop in
translation quality when translating long sentences, unlike existing
phrase-based translation systems. In this paper, we propose a way to address
this issue by automatically segmenting an input sentence into phrases that can
be easily translated by the neural network translation model. Once each segment
has been independently translated by the neural machine translation model, the
translated clauses are concatenated to form a final translation. Empirical
results show a significant improvement in translation quality for long
sentences.
On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
Authors: Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, Yoshua Bengio
Date: 2014-09-03 00:00:00
Tasks: Decoder, Machine Translation, Sentence, Translation

Neural machine translation is a relatively new approach to statistical
machine translation based purely on neural networks. The neural machine
translation models often consist of an encoder and a decoder. The encoder
extracts a fixed-length representation from a variable-length input sentence,
and the decoder generates a correct translation from this representation. In
this paper, we focus on analyzing the properties of the neural machine
translation using two models; RNN Encoder--Decoder and a newly proposed gated
recursive convolutional neural network. We show that the neural machine
translation performs relatively well on short sentences without unknown words,
but its performance degrades rapidly as the length of the sentence and the
number of unknown words increase. Furthermore, we find that the proposed gated
recursive convolutional network learns a grammatical structure of a sentence
automatically.
Generalization Bounds for Learning with Linear, Polygonal, Quadratic and Conic Side Knowledge
Authors: Theja Tulabandhula, Cynthia Rudin
Date: 2014-05-30 00:00:00
Tasks: Generalization Bounds

In this paper, we consider a supervised learning setting where side knowledge
is provided about the labels of unlabeled examples. The side knowledge has the
effect of reducing the hypothesis space, leading to tighter generalization
bounds, and thus possibly better generalization. We consider several types of
side knowledge, the first leading to linear and polygonal constraints on the
hypothesis space, the second leading to quadratic constraints, and the last
leading to conic constraints. We show how different types of domain knowledge
can lead directly to these kinds of side knowledge. We prove bounds on
complexity measures of the hypothesis space for quadratic and conic side
knowledge, and show that these bounds are tight in a specific sense for the
quadratic case.
Ontology-based Representation and Reasoning on Process Models: A Logic Programming Approach
Authors: Fabrizio Smith, Maurizio Proietti
Date: 2014-10-07 00:00:00

We propose a framework grounded in Logic Programming for representing and
reasoning about business processes from both the procedural and ontological
point of views. In particular, our goal is threefold: (1) define a logical
language and a formal semantics for process models enriched with ontology-based
annotations; (2) provide an effective inference mechanism that supports the
combination of reasoning services dealing with the structural definition of a
process model, its behavior, and the domain knowledge related to the
participating business entities; (3) implement such a theoretical framework
into a process modeling and reasoning platform. To this end we define a process
ontology coping with a relevant fragment of the popular BPMN modeling notation.
The behavioral semantics of a process is defined as a state transition system
by following an approach similar to the Fluent Calculus, and allows us to
specify state change in terms of preconditions and effects of the enactment of
activities. Then we show how the procedural process knowledge can be seamlessly
integrated with the domain knowledge specified by using the OWL 2 RL rule-based
ontology language. Our framework provides a wide range of reasoning services,
including CTL model checking, which can be performed by using standard Logic
Programming inference engines through a goal-oriented, efficient, sound and
complete evaluation procedure. We also present a software environment
implementing the proposed framework, and we report on an experimental
evaluation of the system, whose results are encouraging and show the viability
of the approach.