pwc-archive/papers-with-abstracts huggingface dataset:
Structure Regularization for Structured Prediction: Theories and Experiments
Authors: Xu Sun
Date: 2014-11-23 00:00:00
Tasks: Prediction, Structured Prediction

While there are many studies on weight regularization, the study on structure
regularization is rare. Many existing systems on structured prediction focus on
increasing the level of structural dependencies within the model. However, this
trend could have been misdirected, because our study suggests that complex
structures are actually harmful to generalization ability in structured
prediction. To control structure-based overfitting, we propose a structure
regularization framework via \emph{structure decomposition}, which decomposes
training samples into mini-samples with simpler structures, deriving a model
with better generalization power. We show both theoretically and empirically
that structure regularization can effectively control overfitting risk and lead
to better accuracy. As a by-product, the proposed method can also substantially
accelerate the training speed. The method and the theoretical results can apply
to general graphical models with arbitrary structures. Experiments on
well-known tasks demonstrate that our method can easily beat the benchmark
systems on those highly-competitive tasks, achieving state-of-the-art
accuracies yet with substantially faster training speed.
Downscaling Microwave Brightness Temperatures Using Self Regularized Regressive Models
Authors: Subit Chakrabarti, Jasmeet Judge, Anand Rangarajan, Sanjay Ranka
Date: 2015-01-30 00:00:00
Tasks: Clustering

A novel algorithm is proposed to downscale microwave brightness temperatures
($\mathrm{T_B}$), at scales of 10-40 km such as those from the Soil Moisture
Active Passive mission to a resolution meaningful for hydrological and
agricultural applications. This algorithm, called Self-Regularized Regressive
Models (SRRM), uses auxiliary variables correlated to $\mathrm{T_B}$ along-with
a limited set of \textit{in-situ} SM observations, which are converted to high
resolution $\mathrm{T_B}$ observations using biophysical models. It includes an
information-theoretic clustering step based on all auxiliary variables to
identify areas of similarity, followed by a kernel regression step that
produces downscaled $\mathrm{T_B}$. This was implemented on a multi-scale
synthetic data-set over NC-Florida for one year. An RMSE of 5.76~K with
standard deviation of 2.8~k was achieved during the vegetated season and an
RMSE of 1.2~K with a standard deviation of 0.9~K during periods of no
vegetation.
Vector Quantization by Minimizing Kullback-Leibler Divergence
Authors: Lan Yang, Jingbin Wang, Yujin Tu, Prarthana Mahapatra, Nelson Cardoso
Date: 2015-01-30 00:00:00
Tasks: General Classification, image-classification, Image Classification, Quantization

This paper proposes a new method for vector quantization by minimizing the
Kullback-Leibler Divergence between the class label distributions over the
quantization inputs, which are original vectors, and the output, which is the
quantization subsets of the vector set. In this way, the vector quantization
output can keep as much information of the class label as possible. An
objective function is constructed and we also developed an iterative algorithm
to minimize it. The new method is evaluated on bag-of-features based image
classification problem.
Towards Resolving Software Quality-in-Use Measurement Challenges
Authors: Issa Atoum, Chih How Bong, Narayanan Kulathuramaiyer
Date: 2015-01-30 00:00:00
Tasks: Management, Sentiment Analysis

Software quality-in-use comprehends the quality from user's perspectives. It
has gained its importance in e-learning applications, mobile service based
applications and project management tools. User's decisions on software
acquisitions are often ad hoc or based on preference due to difficulty in
quantitatively measure software quality-in-use. However, why quality-in-use
measurement is difficult? Although there are many software quality models to
our knowledge, no works surveys the challenges related to software
quality-in-use measurement. This paper has two main contributions; 1) presents
major issues and challenges in measuring software quality-in-use in the context
of the ISO SQuaRE series and related software quality models, 2) Presents a
novel framework that can be used to predict software quality-in-use, and 3)
presents preliminary results of quality-in-use topic prediction. Concisely, the
issues are related to the complexity of the current standard models and the
limitations and incompleteness of the customized software quality models. The
proposed framework employs sentiment analysis techniques to predict software
quality-in-use.
ImageNet Large Scale Visual Recognition Challenge
Authors: Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei
Date: 2014-09-01 00:00:00
Tasks: General Classification, image-classification, Image Classification, Object, object-detection, Object Detection, Object Recognition

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in
object category classification and detection on hundreds of object categories
and millions of images. The challenge has been run annually from 2010 to
present, attracting participation from more than fifty institutions.
  This paper describes the creation of this benchmark dataset and the advances
in object recognition that have been possible as a result. We discuss the
challenges of collecting large-scale ground truth annotation, highlight key
breakthroughs in categorical object recognition, provide a detailed analysis of
the current state of the field of large-scale image classification and object
detection, and compare the state-of-the-art computer vision accuracy with human
accuracy. We conclude with lessons learned in the five years of the challenge,
and propose future directions and improvements.
Representing Objects, Relations, and Sequences
Authors: Stephen I. Gallant, T. Wendy Okaywe
Date: 2015-01-29 00:00:00
Tasks: BIG-bench Machine Learning, Sentence

Vector Symbolic Architectures (VSAs) are high-dimensional vector
representations of objects (eg., words, image parts), relations (eg., sentence
structures), and sequences for use with machine learning algorithms. They
consist of a vector addition operator for representing a collection of
unordered objects, a Binding operator for associating groups of objects, and a
methodology for encoding complex structures.
  We first develop Constraints that machine learning imposes upon VSAs: for
example, similar structures must be represented by similar vectors. The
constraints suggest that current VSAs should represent phrases ("The smart
Brazilian girl") by binding sums of terms, in addition to simply binding the
terms directly.
  We show that matrix multiplication can be used as the binding operator for a
VSA, and that matrix elements can be chosen at random. A consequence for living
systems is that binding is mathematically possible without the need to specify,
in advance, precise neuron-to-neuron connection properties for large numbers of
synapses.
  A VSA that incorporates these ideas, MBAT (Matrix Binding of Additive Terms),
is described that satisfies all Constraints.
  With respect to machine learning, for some types of problems appropriate VSA
representations permit us to prove learnability, rather than relying on
simulations. We also propose dividing machine (and neural) learning and
representation into three Stages, with differing roles for learning in each
stage.
  For neural modeling, we give "representational reasons" for nervous systems
to have many recurrent connections, as well as for the importance of phrases in
language processing.
  Sizing simulations and analyses suggest that VSAs in general, and MBAT in
particular, are ready for real-world applications.
Deep Representations for Iris, Face, and Fingerprint Spoofing Detection
Authors: David Menotti, Giovani Chiachia, Allan Pinto, William Robson Schwartz, Helio Pedrini, Alexandre Xavier Falcao, Anderson Rocha
Date: 2014-10-08 00:00:00
Tasks: Person Identification

Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or "spoofed") and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, while the second approach focuses on learning the weights of the network via back-propagation. We consider nine biometric spoofing benchmarks --- each one containing real and fake samples of a given biometric modality and attack type --- and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.
Efficient Divide-And-Conquer Classification Based on Feature-Space Decomposition
Authors: Qi Guo, Bo-Wei Chen, Feng Jiang, Xiangyang Ji, Sun-Yuan Kung
Date: 2015-01-29 00:00:00
Tasks: Classification, General Classification

This study presents a divide-and-conquer (DC) approach based on feature space
decomposition for classification. When large-scale datasets are present,
typical approaches usually employed truncated kernel methods on the feature
space or DC approaches on the sample space. However, this did not guarantee
separability between classes, owing to overfitting. To overcome such problems,
this work proposes a novel DC approach on feature spaces consisting of three
steps. Firstly, we divide the feature space into several subspaces using the
decomposition method proposed in this paper. Subsequently, these feature
subspaces are sent into individual local classifiers for training. Finally, the
outcomes of local classifiers are fused together to generate the final
classification results. Experiments on large-scale datasets are carried out for
performance evaluation. The results show that the error rates of the proposed
DC method decreased comparing with the state-of-the-art fast SVM solvers, e.g.,
reducing error rates by 10.53% and 7.53% on RCV1 and covtype datasets
respectively.
High-Dimensional Longitudinal Classification with the Multinomial Fused Lasso
Authors: Samrachana Adhikari, Fabrizio Lecci, James T. Becker, Brian W. Junker, Lewis H. Kuller, Oscar L. Lopez, Ryan J. Tibshirani
Date: 2015-01-29 00:00:00
Tasks: Classification, General Classification, Vocal Bursts Intensity Prediction

We study regularized estimation in high-dimensional longitudinal
classification problems, using the lasso and fused lasso regularizers. The
constructed coefficient estimates are piecewise constant across the time
dimension in the longitudinal problem, with adaptively selected change points
(break points). We present an efficient algorithm for computing such estimates,
based on proximal gradient descent. We apply our proposed technique to a
longitudinal data set on Alzheimer's disease from the Cardiovascular Health
Study Cognition Study, and use this data set to motivate and demonstrate
several practical considerations such as the selection of tuning parameters,
and the assessment of model stability.
Implementation of an Automatic Syllabic Division Algorithm from Speech Files in Portuguese Language
Authors: E. L. F. Da Silva, H. M. de Oliveira
Date: 2015-01-29 00:00:00

A new algorithm for voice automatic syllabic splitting in the Portuguese
language is proposed, which is based on the envelope of the speech signal of
the input audio file. A computational implementation in MatlabTM is presented
and made available at the URL
http://www2.ee.ufpe.br/codec/divisao_silabica.html. Due to its
straightforwardness, the proposed method is very attractive for embedded
systems (e.g. i-phones). It can also be used as a screen to assist more
sophisticated methods. Voice excerpts containing more than one syllable and
identified by the same envelope are named as super-syllables and they are
subsequently separated. The results indicate which samples corresponds to the
beginning and end of each detected syllable. Preliminary tests were performed
to fifty words at an identification rate circa 70% (further improvements may be
incorporated to treat particular phonemes). This algorithm is also useful in
voice command systems, as a tool in the teaching of Portuguese language or even
for patients with speech pathology.
Regression and Learning to Rank Aggregation for User Engagement Evaluation
Authors: Hamed Zamani, Azadeh Shakery, Pooya Moradi
Date: 2015-01-29 00:00:00
Tasks: Learning-To-Rank, Recommendation Systems, regression

User engagement refers to the amount of interaction an instance (e.g., tweet,
news, and forum post) achieves. Ranking the items in social media websites
based on the amount of user participation in them, can be used in different
applications, such as recommender systems. In this paper, we consider a tweet
containing a rating for a movie as an instance and focus on ranking the
instances of each user based on their engagement, i.e., the total number of
retweets and favorites it will gain.
  For this task, we define several features which can be extracted from the
meta-data of each tweet. The features are partitioned into three categories:
user-based, movie-based, and tweet-based. We show that in order to obtain good
results, features from all categories should be considered. We exploit
regression and learning to rank methods to rank the tweets and propose to
aggregate the results of regression and learning to rank methods to achieve
better performance. We have run our experiments on an extended version of
MovieTweeting dataset provided by ACM RecSys Challenge 2014. The results show
that learning to rank approach outperforms most of the regression models and
the combination can improve the performance significantly.
A Flexible Coupling Approach to Multi-Agent Planning under Incomplete Information
Authors: Alejandro Torreño, Eva Onaindia, Óscar Sapena
Date: 2015-01-29 00:00:00

Multi-agent planning (MAP) approaches are typically oriented at solving
loosely-coupled problems, being ineffective to deal with more complex,
strongly-related problems. In most cases, agents work under complete
information, building complete knowledge bases. The present article introduces
a general-purpose MAP framework designed to tackle problems of any coupling
levels under incomplete information. Agents in our MAP model are partially
unaware of the information managed by the rest of agents and share only the
critical information that affects other agents, thus maintaining a distributed
vision of the task.
Pairwise Rotation Hashing for High-dimensional Features
Authors: Kohta Ishikawa, Ikuro Sato, Mitsuru Ambai
Date: 2015-01-29 00:00:00
Tasks: Quantization, Retrieval, Vocal Bursts Intensity Prediction

Binary Hashing is widely used for effective approximate nearest neighbors
search. Even though various binary hashing methods have been proposed, very few
methods are feasible for extremely high-dimensional features often used in
visual tasks today. We propose a novel highly sparse linear hashing method
based on pairwise rotations. The encoding cost of the proposed algorithm is
$\mathrm{O}(n \log n)$ for n-dimensional features, whereas that of the existing
state-of-the-art method is typically $\mathrm{O}(n^2)$. The proposed method is
also remarkably faster in the learning phase. Along with the efficiency, the
retrieval accuracy is comparable to or slightly outperforming the
state-of-the-art. Pairwise rotations used in our method are formulated from an
analytical study of the trade-off relationship between quantization error and
entropy of binary codes. Although these hashing criteria are widely used in
previous researches, its analytical behavior is rarely studied. All building
blocks of our algorithm are based on the analytical solution, and it thus
provides a fairly simple and efficient procedure.
Particle swarm optimization for time series motif discovery
Authors: Joan Serrà, Josep Lluis Arcos
Date: 2015-01-29 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Streams

Efficiently finding similar segments or motifs in time series data is a
fundamental task that, due to the ubiquity of these data, is present in a wide
range of domains and situations. Because of this, countless solutions have been
devised but, to date, none of them seems to be fully satisfactory and flexible.
In this article, we propose an innovative standpoint and present a solution
coming from it: an anytime multimodal optimization algorithm for time series
motif discovery based on particle swarms. By considering data from a variety of
domains, we show that this solution is extremely competitive when compared to
the state-of-the-art, obtaining comparable motifs in considerably less time
using minimal memory. In addition, we show that it is robust to different
implementation choices and see that it offers an unprecedented degree of
flexibility with regard to the task. All these qualities make the presented
solution stand out as one of the most prominent candidates for motif discovery
in long time series streams. Besides, we believe the proposed standpoint can be
exploited in further time series analysis and mining tasks, widening the scope
of research and potentially yielding novel effective solutions.
Optimal Adaptive Learning in Uncontrolled Restless Bandit Problems
Authors: Cem Tekin, Mingyan Liu
Date: 2011-07-20 00:00:00

In this paper we consider the problem of learning the optimal policy for
uncontrolled restless bandit problems. In an uncontrolled restless bandit
problem, there is a finite set of arms, each of which when pulled yields a
positive reward. There is a player who sequentially selects one of the arms at
each time step. The goal of the player is to maximize its undiscounted reward
over a time horizon T. The reward process of each arm is a finite state Markov
chain, whose transition probabilities are unknown by the player. State
transitions of each arm is independent of the selection of the player. We
propose a learning algorithm with logarithmic regret uniformly over time with
respect to the optimal finite horizon policy. Our results extend the optimal
adaptive learning of MDPs to POMDPs.
Reasoning with Probabilistic Logics
Authors: Riccardo Zese
Date: 2014-05-05 00:00:00

The interest in the combination of probability with logics for modeling the
world has rapidly increased in the last few years. One of the most effective
approaches is the Distribution Semantics which was adopted by many logic
programming languages and in Descripion Logics. In this paper, we illustrate
the work we have done in this research field by presenting a probabilistic
semantics for description logics and reasoning and learning algorithms. In
particular, we present in detail the system TRILL P, which computes the
probability of queries w.r.t. probabilistic knowledge bases, which has been
implemented in Prolog. Note: An extended abstract / full version of a paper
accepted to be presented at the Doctoral Consortium of the 30th International
Conference on Logic Programming (ICLP 2014), July 19-22, Vienna, Austria
Online discrete optimization in social networks in the presence of Knightian uncertainty
Authors: Maxim Raginsky, Angelia Nedić
Date: 2013-07-01 00:00:00
Tasks: Decision Making

We study a model of collective real-time decision-making (or learning) in a
social network operating in an uncertain environment, for which no a priori
probabilistic model is available. Instead, the environment's impact on the
agents in the network is seen through a sequence of cost functions, revealed to
the agents in a causal manner only after all the relevant actions are taken.
There are two kinds of costs: individual costs incurred by each agent and
local-interaction costs incurred by each agent and its neighbors in the social
network. Moreover, agents have inertia: each agent has a default mixed strategy
that stays fixed regardless of the state of the environment, and must expend
effort to deviate from this strategy in order to respond to cost signals coming
from the environment. We construct a decentralized strategy, wherein each agent
selects its action based only on the costs directly affecting it and on the
decisions made by its neighbors in the network. In this setting, we quantify
social learning in terms of regret, which is given by the difference between
the realized network performance over a given time horizon and the best
performance that could have been achieved in hindsight by a fictitious
centralized entity with full knowledge of the environment's evolution. We show
that our strategy achieves the regret that scales polylogarithmically with the
time horizon and polynomially with the number of agents and the maximum number
of neighbors of any agent in the social network.
Sequential Probability Assignment with Binary Alphabets and Large Classes of Experts
Authors: Alexander Rakhlin, Karthik Sridharan
Date: 2015-01-29 00:00:00

We analyze the problem of sequential probability assignment for binary
outcomes with side information and logarithmic loss, where regret---or,
redundancy---is measured with respect to a (possibly infinite) class of
experts. We provide upper and lower bounds for minimax regret in terms of
sequential complexities of the class. These complexities were recently shown to
give matching (up to logarithmic factors) upper and lower bounds for sequential
prediction with general convex Lipschitz loss functions (Rakhlin and Sridharan,
2015). To deal with unbounded gradients of the logarithmic loss, we present a
new analysis that employs a sequential chaining technique with a Bernstein-type
bound. The introduced complexities are intrinsic to the problem of sequential
probability assignment, as illustrated by our lower bound.
  We also consider an example of a large class of experts parametrized by
vectors in a high-dimensional Euclidean ball (or a Hilbert ball). The typical
discretization approach fails, while our techniques give a non-trivial bound.
For this problem we also present an algorithm based on regularization with a
self-concordant barrier. This algorithm is of an independent interest, as it
requires a bound on the function values rather than gradients.
Pixel-wise Orthogonal Decomposition for Color Illumination Invariant and Shadow-free Image
Authors: Liangqiong Qu, Jiandong Tian, Zhi Han, Yandong Tang
Date: 2014-06-30 00:00:00
Tasks: Shadow Detection

In this paper, we propose a novel, effective and fast method to obtain a
color illumination invariant and shadow-free image from a single outdoor image.
Different from state-of-the-art methods for shadow-free image that either need
shadow detection or statistical learning, we set up a linear equation set for
each pixel value vector based on physically-based shadow invariants, deduce a
pixel-wise orthogonal decomposition for its solutions, and then get an
illumination invariant vector for each pixel value vector on an image. The
illumination invariant vector is the unique particular solution of the linear
equation set, which is orthogonal to its free solutions. With this illumination
invariant vector and Lab color space, we propose an algorithm to generate a
shadow-free image which well preserves the texture and color information of the
original image. A series of experiments on a diverse set of outdoor images and
the comparisons with the state-of-the-art methods validate our method.