pwc-archive/papers-with-abstracts huggingface dataset:
Notes on Generalized Linear Models of Neurons
Authors: Jonathon Shlens
Date: 2014-04-08 00:00:00

Experimental neuroscience increasingly requires tractable models for
analyzing and predicting the behavior of neurons and networks. The generalized
linear model (GLM) is an increasingly popular statistical framework for
analyzing neural data that is flexible, exhibits rich dynamic behavior and is
computationally tractable (Paninski, 2004; Pillow et al., 2008; Truccolo et
al., 2005). What follows is a brief summary of the primary equations governing
the application of GLM's to spike trains with a few sentences linking this work
to the larger statistical literature. Latter sections include extensions of a
basic GLM to model spatio-temporal receptive fields as well as network activity
in an arbitrary numbers of neurons.
Human Face as human single identity
Authors: Spits Warnars
Date: 2014-04-08 00:00:00

Human face as a physical human recognition can be used as a unique identity for computer to recognize human by transforming human face with face algorithm as simple text number which can be primary key for human. Human face as single identity for human will be done by making a huge and large world centre human face database, where the human face around the world will be recorded from time to time and from generation to generation. Architecture database will be divided become human face image database which will save human face images and human face output code which will save human face output code as a transformation human face image with face algorithm. As an improvement the slightly and simple human face output code database will make human face searching process become more fast. Transaction with human face as a transaction without card can make human no need their card for the transaction and office automation and banking system as an example for implementation architecture. As an addition suspect human face database can be extended for fighting crime and terrorism by doing surveillance and searching suspect human face around the world.
A New Approach to Speeding Up Topic Modeling
Authors: Jia Zeng, Zhi-Qiang Liu, Xiao-Qin Cao
Date: 2012-04-01 00:00:00

Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic
modeling paradigm, and recently finds many applications in computer vision and
computational biology. In this paper, we propose a fast and accurate batch
algorithm, active belief propagation (ABP), for training LDA. Usually batch LDA
algorithms require repeated scanning of the entire corpus and searching the
complete topic space. To process massive corpora having a large number of
topics, the training iteration of batch LDA algorithms is often inefficient and
time-consuming. To accelerate the training speed, ABP actively scans the subset
of corpus and searches the subset of topic space for topic modeling, therefore
saves enormous training time in each iteration. To ensure accuracy, ABP selects
only those documents and topics that contribute to the largest residuals within
the residual belief propagation (RBP) framework. On four real-world corpora,
ABP performs around $10$ to $100$ times faster than state-of-the-art batch LDA
algorithms with a comparable topic modeling accuracy.
Aspect-Based Opinion Extraction from Customer reviews
Authors: Amani K Samha, Yuefeng Li, Jinglan Zhang
Date: 2014-04-08 00:00:00
Tasks: Articles, Decision Making, TAG

Text is the main method of communicating information in the digital age.
Messages, blogs, news articles, reviews, and opinionated information abound on
the Internet. People commonly purchase products online and post their opinions
about purchased items. This feedback is displayed publicly to assist others
with their purchasing decisions, creating the need for a mechanism with which
to extract and summarize useful information for enhancing the decision-making
process. Our contribution is to improve the accuracy of extraction by combining
different techniques from three major areas, named Data Mining, Natural
Language Processing techniques and Ontologies. The proposed framework
sequentially mines products aspects and users opinions, groups representative
aspects by similarity, and generates an output summary. This paper focuses on
the task of extracting product aspects and users opinions by extracting all
possible aspects and opinions from reviews using natural language, ontology,
and frequent (tag) sets. The proposed framework, when compared with an existing
baseline model, yielded promising results.
Coupled Item-based Matrix Factorization
Authors: Fangfang Li, Guandong Xu, Longbing Cao
Date: 2014-04-08 00:00:00
Tasks: Attribute, Collaborative Filtering, Recommendation Systems

The essence of the challenges cold start and sparsity in Recommender Systems
(RS) is that the extant techniques, such as Collaborative Filtering (CF) and
Matrix Factorization (MF), mainly rely on the user-item rating matrix, which
sometimes is not informative enough for predicting recommendations. To solve
these challenges, the objective item attributes are incorporated as
complementary information. However, most of the existing methods for inferring
the relationships between items assume that the attributes are "independently
and identically distributed (iid)", which does not always hold in reality. In
fact, the attributes are more or less coupled with each other by some implicit
relationships. Therefore, in this pa-per we propose an attribute-based coupled
similarity measure to capture the implicit relationships between items. We then
integrate the implicit item coupling into MF to form the Coupled Item-based
Matrix Factorization (CIMF) model. Experimental results on two open data sets
demonstrate that CIMF outperforms the benchmark methods.
Polish and English wordnets -- statistical analysis of interconnected networks
Authors: Maksymilian Bujok, Piotr Fronczak, Agata Fronczak
Date: 2014-04-07 00:00:00

Wordnets are semantic networks containing nouns, verbs, adjectives, and
adverbs organized according to linguistic principles, by means of semantic
relations. In this work, we adopt a complex network perspective to perform a
comparative analysis of the English and Polish wordnets. We determine their
similarities and show that the networks exhibit some of the typical
characteristics observed in other real-world networks. We analyse interlingual
relations between both wordnets and deliberate over the problem of mapping the
Polish lexicon onto the English one.
From Shading to Local Shape
Authors: Ying Xiong, Ayan Chakrabarti, Ronen Basri, Steven J. Gortler, David W. Jacobs, Todd Zickler
Date: 2013-10-10 00:00:00
Tasks: Surface Reconstruction

We develop a framework for extracting a concise representation of the shape
information available from diffuse shading in a small image patch. This
produces a mid-level scene descriptor, comprised of local shape distributions
that are inferred separately at every image patch across multiple scales. The
framework is based on a quadratic representation of local shape that, in the
absence of noise, has guarantees on recovering accurate local shape and
lighting. And when noise is present, the inferred local shape distributions
provide useful shape information without over-committing to any particular
image explanation. These local shape distributions naturally encode the fact
that some smooth diffuse regions are more informative than others, and they
enable efficient and robust reconstruction of object-scale shape. Experimental
results show that this approach to surface reconstruction compares well against
the state-of-art on both synthetic images and captured photographs.
Plug and Play! A Simple, Universal Model for Energy Disaggregation
Authors: Guoming Tang, Kui Wu, Jing-sheng Lei, Jiuyang Tang
Date: 2014-04-07 00:00:00

Energy disaggregation is to discover the energy consumption of individual
appliances from their aggregated energy values. To solve the problem, most
existing approaches rely on either appliances' signatures or their state
transition patterns, both hard to obtain in practice. Aiming at developing a
simple, universal model that works without depending on sophisticated machine
learning techniques or auxiliary equipments, we make use of easily accessible
knowledge of appliances and the sparsity of the switching events to design a
Sparse Switching Event Recovering (SSER) method. By minimizing the total
variation (TV) of the (sparse) event matrix, SSER can effectively recover the
individual energy consumption values from the aggregated ones. To speed up the
process, a Parallel Local Optimization Algorithm (PLOA) is proposed to solve
the problem in active epochs of appliance activities in parallel. Using
real-world trace data, we compare the performance of our method with that of
the state-of-the-art solutions, including Least Square Estimation (LSE) and
iterative Hidden Markov Model (HMM). The results show that our approach has an
overall higher detection accuracy and a smaller overhead.
A Hierarchical Graphical Model for Big Inverse Covariance Estimation with an Application to fMRI
Authors: Xi Luo
Date: 2014-03-19 00:00:00

Brain networks has attracted the interests of many neuroscientists. From
functional MRI (fMRI) data, statistical tools have been developed to recover
brain networks. However, the dimensionality of whole-brain fMRI, usually in
hundreds of thousands, challenges the applicability of these methods. We
develop a hierarchical graphical model (HGM) to remediate this difficulty. This
model introduces a hidden layer of networks based on sparse Gaussian graphical
models, and the observed data are sampled from individual network nodes. In
fMRI, the network layer models the underlying signals of different brain
functional units, and how these units directly interact with each other. The
introduction of this hierarchical structure not only provides a formal and
interpretable approach, but also enables efficient computation for inferring
big networks with hundreds of thousands of nodes. Based on the conditional
convexity of our formulation, we develop an alternating update algorithm to
compute the HGM model parameters simultaneously. The effectiveness of this
approach is demonstrated on simulated data and a real dataset from a stop/go
fMRI experiment.
Intégration des données d'un lexique syntaxique dans un analyseur syntaxique probabiliste
Authors: Anthony Sigogne, Matthieu Constant, Eric Laporte
Date: 2014-04-07 00:00:00

This article reports the evaluation of the integration of data from a
syntactic-semantic lexicon, the Lexicon-Grammar of French, into a syntactic
parser. We show that by changing the set of labels for verbs and predicational
nouns, we can improve the performance on French of a non-lexicalized
probabilistic parser.
DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
Authors: Forrest Iandola, Matt Moskewicz, Sergey Karayev, Ross Girshick, Trevor Darrell, Kurt Keutzer
Date: 2014-04-07 00:00:00
Tasks: General Classification, Object, object-detection, Object Detection

Convolutional Neural Networks (CNNs) can provide accurate object
classification. They can be extended to perform object detection by iterating
over dense or selected proposed object regions. However, the runtime of such
detectors scales as the total number and/or area of regions to examine per
image, and training such detectors may be prohibitively slow. However, for some
CNN classifier topologies, it is possible to share significant work among
overlapping regions to be classified. This paper presents DenseNet, an open
source system that computes dense, multiscale features from the convolutional
layers of a CNN based object classifier. Future work will involve training
efficient object detectors with DenseNet feature descriptors.
Evaluation and Ranking of Machine Translated Output in Hindi Language using Precision and Recall Oriented Metrics
Authors: Aditi Kalyani, Hemant Kumud, Shashi Pal Singh, Ajai Kumar, Hemant Darbari
Date: 2014-04-07 00:00:00
Tasks: Machine Translation, Translation

Evaluation plays a crucial role in development of Machine translation
systems. In order to judge the quality of an existing MT system i.e. if the
translated output is of human translation quality or not, various automatic
metrics exist. We here present the implementation results of different metrics
when used on Hindi language along with their comparisons, illustrating how
effective are these metrics on languages like Hindi (free word order language).
Structural Intervention Distance (SID) for Evaluating Causal Graphs
Authors: Jonas Peters, Peter Bühlmann
Date: 2013-06-05 00:00:00
Tasks: Causal Inference

Causal inference relies on the structure of a graph, often a directed acyclic
graph (DAG). Different graphs may result in different causal inference
statements and different intervention distributions. To quantify such
differences, we propose a (pre-) distance between DAGs, the structural
intervention distance (SID). The SID is based on a graphical criterion only and
quantifies the closeness between two DAGs in terms of their corresponding
causal inference statements. It is therefore well-suited for evaluating graphs
that are used for computing interventions. Instead of DAGs it is also possible
to compare CPDAGs, completed partially directed acyclic graphs that represent
Markov equivalence classes. Since it differs significantly from the popular
Structural Hamming Distance (SHD), the SID constitutes a valuable additional
measure. We discuss properties of this distance and provide an efficient
implementation with software code available on the first author's homepage (an
R package is under construction).
Improving Bilayer Product Quantization for Billion-Scale Approximate Nearest Neighbors in High Dimensions
Authors: Artem Babenko, Victor Lempitsky
Date: 2014-04-07 00:00:00
Tasks: Data Compression, Quantization

The top-performing systems for billion-scale high-dimensional approximate
nearest neighbor (ANN) search are all based on two-layer architectures that
include an indexing structure and a compressed datapoints layer. An indexing
structure is crucial as it allows to avoid exhaustive search, while the lossy
data compression is needed to fit the dataset into RAM. Several of the most
successful systems use product quantization (PQ) for both the indexing and the
dataset compression layers. These systems are however limited in the way they
exploit the interaction of product quantization processes that happen at
different stages of these systems.
  Here we introduce and evaluate two approximate nearest neighbor search
systems that both exploit the synergy of product quantization processes in a
more efficient way. The first system, called Fast Bilayer Product Quantization
(FBPQ), speeds up the runtime of the baseline system (Multi-D-ADC) by several
times, while achieving the same accuracy. The second system, Hierarchical
Bilayer Product Quantization (HBPQ) provides a significantly better recall for
the same runtime at a cost of small memory footprint increase. For the BIGANN
dataset of billion SIFT descriptors, the 10% increase in Recall@1 and the 17%
increase in Recall@10 is observed.
Determining the Consistency factor of Autopilot using Rough Set Theory
Authors: Anugrah Kumar
Date: 2014-04-07 00:00:00

Autopilot is a system designed to guide a vehicle without aid. Due to
increase in flight hours and complexity of modern day flight it has become
imperative to equip the aircrafts with autopilot. Thus reliability and
consistency of an Autopilot system becomes a crucial role in a flight. But the
increased complexity and demand for better accuracy has made the process of
evaluating the autopilot for consistency a difficult process .A vast amount of
imprecise data has been involved. Rough sets can be a potent tool for such kind
of Applications containing vague data. This paper proposes an approach towards
Consistency factor determination using Rough Set Theory. The seventeen basic
factors, that are crucial in determining the consistency of an Autopilot
system, are grouped into five Payloads based on their functionality.
Consistency Factor is evaluated through these payloads, using Rough Set Theory.
Consistency Factor determines the consistency and reliability of an autopilot
system and the conditions under which manual override becomes imperative. Using
Rough set Theory the most and the least influential factors towards Autopilot
system are also determined.
Probabilistic Archetypal Analysis
Authors: Sohan Seth, Manuel J. A. Eugster
Date: 2013-12-29 00:00:00

Archetypal analysis represents a set of observations as convex combinations
of pure patterns, or archetypes. The original geometric formulation of finding
archetypes by approximating the convex hull of the observations assumes them to
be real valued. This, unfortunately, is not compatible with many practical
situations. In this paper we revisit archetypal analysis from the basic
principles, and propose a probabilistic framework that accommodates other
observation types such as integers, binary, and probability vectors. We
corroborate the proposed methodology with convincing real-world applications on
finding archetypal winter tourists based on binary survey data, archetypal
disaster-affected countries based on disaster count data, and document
archetypes based on term-frequency data. We also present an appropriate
visualization tool to summarize archetypal analysis solution better.
MBIS: Multivariate Bayesian Image Segmentation Tool
Authors: Oscar Esteban, Gert Wollny, Subrahmanyam Gorthi, Maria-J. Ledesma-Carbayo, Jean-Philippe Thiran, Andres Santos, Meritxell Bach-Cuadra
Date: 2014-04-02 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering
tool based on the mixture of multivariate normal distributions model. MBIS
supports multi-channel bias field correction based on a B-spline model. A
second methodological novelty is the inclusion of graph-cuts optimization for
the stationary anisotropic hidden Markov random field model. Along with MBIS,
we release an evaluation framework that contains three different experiments on
multi-site data. We first validate the accuracy of segmentation and the
estimated bias field for each channel. MBIS outperforms a widely used
segmentation tool in a cross-comparison evaluation. The second experiment
demonstrates the robustness of results on atlas-free segmentation of two image
sets from scan-rescan protocols on 21 healthy subjects. Multivariate
segmentation is more replicable than the monospectral counterpart on
T1-weighted images. Finally, we provide a third experiment to illustrate how
MBIS can be used in a large-scale study of tissue volume change with increasing
age in 584 healthy subjects. This last result is meaningful as multivariate
segmentation performs robustly without the need for prior knowledge
Pseudo-Marginal Bayesian Inference for Gaussian Processes
Authors: Maurizio Filippone, Mark Girolami
Date: 2013-10-02 00:00:00
Tasks: Bayesian Inference, Gaussian Processes

The main challenges that arise when adopting Gaussian Process priors in
probabilistic modeling are how to carry out exact Bayesian inference and how to
account for uncertainty on model parameters when making model-based predictions
on out-of-sample data. Using probit regression as an illustrative working
example, this paper presents a general and effective methodology based on the
pseudo-marginal approach to Markov chain Monte Carlo that efficiently addresses
both of these issues. The results presented in this paper show improvements
over existing sampling methods to simulate from the posterior distribution over
the parameters defining the covariance function of the Gaussian Process prior.
This is particularly important as it offers a powerful tool to carry out full
Bayesian inference of Gaussian Process based hierarchic statistical models in
general. The results also demonstrate that Monte Carlo based integration of all
model parameters is actually feasible in this class of models providing a
superior quantification of uncertainty in predictions. Extensive comparisons
with respect to state-of-the-art probabilistic classifiers confirm this
assertion.
Successive Nonnegative Projection Algorithm for Robust Nonnegative Blind Source Separation
Authors: Nicolas Gillis
Date: 2013-10-28 00:00:00
Tasks: blind source separation, Single Particle Analysis

In this paper, we propose a new fast and robust recursive algorithm for
near-separable nonnegative matrix factorization, a particular nonnegative blind
source separation problem. This algorithm, which we refer to as the successive
nonnegative projection algorithm (SNPA), is closely related to the popular
successive projection algorithm (SPA), but takes advantage of the nonnegativity
constraint in the decomposition. We prove that SNPA is more robust than SPA and
can be applied to a broader class of nonnegative matrices. This is illustrated
on some synthetic data sets, and on a real-world hyperspectral image.
Icon Based Information Retrieval and Disease Identification in Agriculture
Authors: Namita Mittal, Basant Agarwal, Ajay Gupta, Hemant Madhur
Date: 2014-04-07 00:00:00
Tasks: Information Retrieval, Retrieval

Recent developments in the ICT industry in past few decades has enabled the
quick and easy access to the information available on the internet. But,
digital literacy is the pre-requisite for its use. The main purpose of this
paper is to provide an interface for digitally illiterate users, especially
farmers to efficiently and effectively retrieve information through Internet.
In addition, to enable the farmers to identify the disease in their crop, its
cause and symptoms using digital image processing and pattern recognition
instantly without waiting for an expert to visit the farms and identify the
disease.
Penalty-regulated dynamics and robust learning procedures in games
Authors: Pierre Coucheney, Bruno Gaujal, Panayotis Mertikopoulos
Date: 2013-03-09 00:00:00

Starting from a heuristic learning scheme for N-person games, we derive a new
class of continuous-time learning dynamics consisting of a replicator-like
drift adjusted by a penalty term that renders the boundary of the game's
strategy space repelling. These penalty-regulated dynamics are equivalent to
players keeping an exponentially discounted aggregate of their on-going payoffs
and then using a smooth best response to pick an action based on these
performance scores. Owing to this inherent duality, the proposed dynamics
satisfy a variant of the folk theorem of evolutionary game theory and they
converge to (arbitrarily precise) approximations of Nash equilibria in
potential games. Motivated by applications to traffic engineering, we exploit
this duality further to design a discrete-time, payoff-based learning algorithm
which retains these convergence properties and only requires players to observe
their in-game payoffs: moreover, the algorithm remains robust in the presence
of stochastic perturbations and observation errors, and it does not require any
synchronization between players.
A Denoising Autoencoder that Guides Stochastic Search
Authors: Alexander W. Churchill, Siddharth Sigtia, Chrisantha Fernando
Date: 2014-04-06 00:00:00
Tasks: Denoising

An algorithm is described that adaptively learns a non-linear mutation
distribution. It works by training a denoising autoencoder (DA) online at each
generation of a genetic algorithm to reconstruct a slowly decaying memory of
the best genotypes so far. A compressed hidden layer forces the autoencoder to
learn hidden features in the training set that can be used to accelerate search
on novel problems with similar structure. Its output neurons define a
probability distribution that we sample from to produce offspring solutions.
The algorithm outperforms a canonical genetic algorithm on several
combinatorial optimisation problems, e.g. multidimensional 0/1 knapsack
problem, MAXSAT, HIFF, and on parameter optimisation problems, e.g. Rastrigin
and Rosenbrock functions.