pwc-archive/papers-with-abstracts huggingface dataset:
Document Embedding with Paragraph Vectors
Authors: Andrew M. Dai, Christopher Olah, Quoc V. Le
Date: 2015-07-29 00:00:00
Tasks: Document Embedding, Sentiment Analysis, Word Embeddings

Paragraph Vectors has been recently proposed as an unsupervised method for
learning distributed representations for pieces of texts. In their work, the
authors showed that the method can learn an embedding of movie review texts
which can be leveraged for sentiment analysis. That proof of concept, while
encouraging, was rather narrow. Here we consider tasks other than sentiment
analysis, provide a more thorough comparison of Paragraph Vectors to other
document modelling algorithms such as Latent Dirichlet Allocation, and evaluate
performance of the method as we vary the dimensionality of the learned
representation. We benchmarked the models on two document similarity data sets,
one from Wikipedia, one from arXiv. We observe that the Paragraph Vector method
performs significantly better than other methods, and propose a simple
improvement to enhance embedding quality. Somewhat surprisingly, we also show
that much like word embeddings, vector operations on Paragraph Vectors can
perform useful semantic results.
A constrained optimization perspective on actor critic algorithms and application to network routing
Authors: Prashanth L. A., H. L. Prasad, Shalabh Bhatnagar, Prakash Chandra
Date: 2015-07-28 00:00:00

We propose a novel actor-critic algorithm with guaranteed convergence to an
optimal policy for a discounted reward Markov decision process. The actor
incorporates a descent direction that is motivated by the solution of a certain
non-linear optimization problem. We also discuss an extension to incorporate
function approximation and demonstrate the practicality of our algorithms on a
network routing application.
An algorithm for online tensor prediction
Authors: John Pothier, Josh Girson, Shuchin Aeron
Date: 2015-07-28 00:00:00
Tasks: Prediction

We present a new method for online prediction and learning of tensors
($N$-way arrays, $N >2$) from sequential measurements. We focus on the specific
case of 3-D tensors and exploit a recently developed framework of structured
tensor decompositions proposed in [1]. In this framework it is possible to
treat 3-D tensors as linear operators and appropriately generalize notions of
rank and positive definiteness to tensors in a natural way. Using these notions
we propose a generalization of the matrix exponentiated gradient descent
algorithm [2] to a tensor exponentiated gradient descent algorithm using an
extension of the notion of von-Neumann divergence to tensors. Then following a
similar construction as in [3], we exploit this algorithm to propose an online
algorithm for learning and prediction of tensors with provable regret
guarantees. Simulations results are presented on semi-synthetic data sets of
ratings evolving in time under local influence over a social network. The
result indicate superior performance compared to other (online) convex tensor
completion methods.
Face Search at Scale: 80 Million Gallery
Authors: Dayong Wang, Charles Otto, Anil K. Jain
Date: 2015-07-26 00:00:00
Tasks: Face Recognition, Face Verification

Due to the prevalence of social media websites, one challenge facing computer
vision researchers is to devise methods to process and search for persons of
interest among the billions of shared photos on these websites. Facebook
revealed in a 2013 white paper that its users have uploaded more than 250
billion photos, and are uploading 350 million new photos each day. Due to this
humongous amount of data, large-scale face search for mining web images is both
important and challenging. Despite significant progress in face recognition,
searching a large collection of unconstrained face images has not been
adequately addressed. To address this challenge, we propose a face search
system which combines a fast search procedure, coupled with a state-of-the-art
commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe
face, we first filter the large gallery of photos to find the top-k most
similar faces using deep features generated from a convolutional neural
network. The k candidates are re-ranked by combining similarities from deep
features and the COTS matcher. We evaluate the proposed face search system on a
gallery containing 80 million web-downloaded face images. Experimental results
demonstrate that the deep features are competitive with state-of-the-art
methods on unconstrained face recognition benchmarks (LFW and IJB-A). Further,
the proposed face search system offers an excellent trade-off between accuracy
and scalability on datasets consisting of millions of images. Additionally, in
an experiment involving searching for face images of the Tsarnaev brothers,
convicted of the Boston Marathon bombing, the proposed face search system could
find the younger brother's (Dzhokhar Tsarnaev) photo at rank 1 in 1 second on a
5M gallery and at rank 8 in 7 seconds on an 80M gallery.
Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels
Authors: Eric Heim, Milos Hauskrecht
Date: 2015-07-28 00:00:00
Tasks: Metric Learning

In this work, we focus on the problem of learning a classification model that
performs inference on patient Electronic Health Records (EHRs). Often, a large
amount of costly expert supervision is required to learn such a model. To
reduce this cost, we obtain confidence labels that indicate how sure an expert
is in the class labels she provides. If meaningful confidence information can
be incorporated into a learning method, fewer patient instances may need to be
labeled to learn an accurate model. In addition, while accuracy of predictions
is important for any inference model, a model of patients must be interpretable
so that clinicians can understand how the model is making decisions. To these
ends, we develop a novel metric learning method called Confidence bAsed MEtric
Learning (CAMEL) that supports inclusion of confidence labels, but also
emphasizes interpretability in three ways. First, our method induces sparsity,
thus producing simple models that use only a few features from patient EHRs.
Second, CAMEL naturally produces confidence scores that can be taken into
consideration when clinicians make treatment decisions. Third, the metrics
learned by CAMEL induce multidimensional spaces where each dimension represents
a different "factor" that clinicians can use to assess patients. In our
experimental evaluation, we show on a real-world clinical data set that our
CAMEL methods are able to learn models that are as or more accurate as other
methods that use the same supervision. Furthermore, we show that when CAMEL
uses confidence scores it is able to learn models as or more accurate as others
we tested while using only 10% of the training instances. Finally, we perform
qualitative assessments on the metrics learned by CAMEL and show that they
identify and clearly articulate important factors in how the model performs
inference.
Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm
Authors: Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Jean-Francis Roy
Date: 2015-03-28 00:00:00
Tasks: Binary Classification, Learning Theory

We propose an extensive analysis of the behavior of majority votes in binary
classification. In particular, we introduce a risk bound for majority votes,
called the C-bound, that takes into account the average quality of the voters
and their average disagreement. We also propose an extensive PAC-Bayesian
analysis that shows how the C-bound can be estimated from various observations
contained in the training data. The analysis intends to be self-contained and
can be used as introductory material to PAC-Bayesian statistical learning
theory. It starts from a general PAC-Bayesian perspective and ends with
uncommon PAC-Bayesian bounds. Some of these bounds contain no Kullback-Leibler
divergence and others allow kernel functions to be used as voters (via the
sample compression setting). Finally, out of the analysis, we propose the MinCq
learning algorithm that basically minimizes the C-bound. MinCq reduces to a
simple quadratic program. Aside from being theoretically grounded, MinCq
achieves state-of-the-art performance, as shown in our extensive empirical
comparison with both AdaBoost and the Support Vector Machine.
Classifying informative and imaginative prose using complex networks
Authors: Henrique F. de Arruda, Luciano da F. Costa, Diego R. Amancio
Date: 2015-07-28 00:00:00
Tasks: Document Classification, General Classification, Machine Translation, Translation

Statistical methods have been widely employed in recent years to grasp many
language properties. The application of such techniques have allowed an
improvement of several linguistic applications, which encompasses machine
translation, automatic summarization and document classification. In the
latter, many approaches have emphasized the semantical content of texts, as it
is the case of bag-of-word language models. This approach has certainly yielded
reasonable performance. However, some potential features such as the structural
organization of texts have been used only on a few studies. In this context, we
probe how features derived from textual structure analysis can be effectively
employed in a classification task. More specifically, we performed a supervised
classification aiming at discriminating informative from imaginative documents.
Using a networked model that describes the local topological/dynamical
properties of function words, we achieved an accuracy rate of up to 95%, which
is much higher than similar networked approaches. A systematic analysis of
feature relevance revealed that symmetry and accessibility measurements are
among the most prominent network measurements. Our results suggest that these
measurements could be used in related language applications, as they play a
complementary role in characterizing texts.
Comparing the writing style of real and artificial papers
Authors: Diego R. Amancio
Date: 2015-06-18 00:00:00

Recent years have witnessed the increase of competition in science. While
promoting the quality of research in many cases, an intense competition among
scientists can also trigger unethical scientific behaviors. To increase the
total number of published papers, some authors even resort to software tools
that are able to produce grammatical, but meaningless scientific manuscripts.
Because automatically generated papers can be misunderstood as real papers, it
becomes of paramount importance to develop means to identify these scientific
frauds. In this paper, I devise a methodology to distinguish real manuscripts
from those generated with SCIGen, an automatic paper generator. Upon modeling
texts as complex networks (CN), it was possible to discriminate real from fake
papers with at least 89\% of accuracy. A systematic analysis of features
relevance revealed that the accessibility and betweenness were useful in
particular cases, even though the relevance depended upon the dataset. The
successful application of the methods described here show, as a proof of
principle, that network features can be used to identify scientific gibberish
papers. In addition, the CN-based approach can be combined in a straightforward
fashion with traditional statistical language processing methods to improve the
performance in identifying artificially generated papers.
A Multi-Camera Image Processing and Visualization System for Train Safety Assessment
Authors: Giuseppe Lisanti, Svebor Karaman, Daniele Pezzatini, Alberto del Bimbo
Date: 2015-07-28 00:00:00

In this paper we present a machine vision system to efficiently monitor,
analyze and present visual data acquired with a railway overhead gantry
equipped with multiple cameras. This solution aims to improve the safety of
daily life railway transportation in a two- fold manner: (1) by providing
automatic algorithms that can process large imagery of trains (2) by helping
train operators to keep attention on any possible malfunction. The system is
designed with the latest cutting edge, high-rate visible and thermal cameras
that ob- serve a train passing under an railway overhead gantry. The machine
vision system is composed of three principal modules: (1) an automatic wagon
identification system, recognizing the wagon ID according to the UIC
classification of railway coaches; (2) a temperature monitoring system; (3) a
system for the detection, localization and visualization of the pantograph of
the train. These three machine vision modules process batch trains sequences
and their resulting analysis are presented to an operator using a multitouch
user interface. We detail all technical aspects of our multi-camera portal: the
hardware requirements, the software developed to deal with the high-frame rate
cameras and ensure reliable acquisition, the algorithms proposed to solve each
computer vision task, and the multitouch interaction and visualization
interface. We evaluate each component of our system on a dataset recorded in an
ad-hoc railway test-bed, showing the potential of our proposed portal for train
safety assessment.
An Analytically Tractable Bayesian Approximation to Optimal Point Process Filtering
Authors: Yuval Harel, Ron Meir, Manfred Opper
Date: 2015-07-28 00:00:00
Tasks: State Estimation

The process of dynamic state estimation (filtering) based on point process
observations is in general intractable. Numerical sampling techniques are often
practically useful, but lead to limited conceptual insight about optimal
encoding/decoding strategies, which are of significant relevance to
Computational Neuroscience. We develop an analytically tractable Bayesian
approximation to optimal filtering based on point process observations, which
allows us to introduce distributional assumptions about sensory cell
properties, that greatly facilitates the analysis of optimal encoding in
situations deviating from common assumptions of uniform coding. The analytic
framework leads to insights which are difficult to obtain from numerical
algorithms, and is consistent with experiments about the distribution of tuning
curve centers. Interestingly, we find that the information gained from the
absence of spikes may be crucial to performance.
Application of Independent Component Analysis Techniques in Speckle Noise Reduction of Retinal OCT Images
Authors: Ahmadreza Baghaie, Roshan M. D'souza, Zeyun Yu
Date: 2015-02-19 00:00:00

Optical Coherence Tomography (OCT) is an emerging technique in the field of
biomedical imaging, with applications in ophthalmology, dermatology, coronary
imaging etc. OCT images usually suffer from a granular pattern, called speckle
noise, which restricts the process of interpretation. Therefore the need for
speckle noise reduction techniques is of high importance. To the best of our
knowledge, use of Independent Component Analysis (ICA) techniques has never
been explored for speckle reduction of OCT images. Here, a comparative study of
several ICA techniques (InfoMax, JADE, FastICA and SOBI) is provided for noise
reduction of retinal OCT images. Having multiple B-scans of the same location,
the eye movements are compensated using a rigid registration technique. Then,
different ICA techniques are applied to the aggregated set of B-scans for
extracting the noise-free image. Signal-to-Noise-Ratio (SNR),
Contrast-to-Noise-Ratio (CNR) and Equivalent-Number-of-Looks (ENL), as well as
analysis on the computational complexity of the methods, are considered as
metrics for comparison. The results show that use of ICA can be beneficial,
especially in case of having fewer number of B-scans.
SynapCountJ --- a Tool for Analyzing Synaptic Densities in Neurons
Authors: Gadea Mata, Jónathan Heras, Miguel Morales, Ana Romero, Julio Rubio
Date: 2015-07-28 00:00:00

The quantification of synapses is instrumental to measure the evolution of
synaptic densities of neurons under the effect of some physiological
conditions, neuronal diseases or even drug treatments. However, the manual
quantification of synapses is a tedious, error-prone, time-consuming and
subjective task; therefore, tools that might automate this process are
desirable. In this paper, we present SynapCountJ, an ImageJ plugin, that can
measure synaptic density of individual neurons obtained by immunofluorescence
techniques, and also can be applied for batch processing of neurons that have
been obtained in the same experiment or using the same setting. The procedure
to quantify synapses implemented in SynapCountJ is based on the colocalization
of three images of the same neuron (the neuron marked with two antibody markers
and the structure of the neuron) and is inspired by methods coming from
Computational Algebraic Topology. SynapCountJ provides a procedure to
semi-automatically quantify the number of synapses of neuron cultures; as a
result, the time required for such an analysis is greatly reduced.
Fast Segmentation of Left Ventricle in CT Images by Explicit Shape Regression using Random Pixel Difference Features
Authors: Peng Sun, Haoyin Zhou, Devon Lundine, James K. Min, Guanglei Xiong
Date: 2015-07-27 00:00:00
Tasks: Computed Tomography (CT), LV Segmentation, Segmentation

Recently, machine learning has been successfully applied to model-based left
ventricle (LV) segmentation. The general framework involves two stages, which
starts with LV localization and is followed by boundary delineation. Both are
driven by supervised learning techniques. When compared to previous
non-learning-based methods, several advantages have been shown, including full
automation and improved accuracy. However, the speed is still slow, in the
order of several seconds, for applications involving a large number of cases or
case loads requiring real-time performance. In this paper, we propose a fast LV
segmentation algorithm by joint localization and boundary delineation via
training explicit shape regressor with random pixel difference features. Tested
on 3D cardiac computed tomography (CT) image volumes, the average running time
of the proposed algorithm is 1.2 milliseconds per case. On a dataset consisting
of 139 CT volumes, a 5-fold cross validation shows the segmentation error is
$1.21 \pm 0.11$ for LV endocardium and $1.23 \pm 0.11$ millimeters for
epicardium. Compared with previous work, the proposed method is more stable
(lower standard deviation) without significant compromise to the accuracy.
A Hyperelastic Two-Scale Optimization Model for Shape Matching
Authors: Konrad Simon, Sameer Sheorey, David Jacobs, Ronen Basri
Date: 2015-07-28 00:00:00
Tasks: Vocal Bursts Valence Prediction

We suggest a novel shape matching algorithm for three-dimensional surface
meshes of disk or sphere topology. The method is based on the physical theory
of nonlinear elasticity and can hence handle large rotations and deformations.
Deformation boundary conditions that supplement the underlying equations are
usually unknown. Given an initial guess, these are optimized such that the
mechanical boundary forces that are responsible for the deformation are of a
simple nature. We show a heuristic way to approximate the nonlinear
optimization problem by a sequence of convex problems using finite elements.
The deformation cost, i.e, the forces, is measured on a coarse scale while
ICP-like matching is done on the fine scale. We demonstrate the plausibility of
our algorithm on examples taken from different datasets.
Projected Model Counting
Authors: Rehan Abdul Aziz, Geoffrey Chu, Christian Muise, Peter Stuckey
Date: 2015-07-28 00:00:00
Tasks: model

Model counting is the task of computing the number of assignments to
variables V that satisfy a given propositional theory F. Model counting is an
essential tool in probabilistic reasoning. In this paper, we introduce the
problem of model counting projected on a subset P of original variables that we
call 'priority' variables. The task is to compute the number of assignments to
P such that there exists an extension to 'non-priority' variables V\P that
satisfies F. Projected model counting arises when some parts of the model are
irrelevant to the counts, in particular when we require additional variables to
model the problem we are counting in SAT. We discuss three different approaches
to projected model counting (two of which are novel), and compare their
performance on different benchmark problems.
  To appear in 18th International Conference on Theory and Applications of
Satisfiability Testing, September 24-27, 2015, Austin, Texas, USA
Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds
Authors: Sridhar Mahadevan, Sarath Chandar
Date: 2015-07-28 00:00:00
Tasks: Word Embeddings

Recent work has explored methods for learning continuous vector space word
representations reflecting the underlying semantics of words. Simple vector
space arithmetic using cosine distances has been shown to capture certain types
of analogies, such as reasoning about plurals from singulars, past tense from
present tense, etc. In this paper, we introduce a new approach to capture
analogies in continuous word representations, based on modeling not just
individual word vectors, but rather the subspaces spanned by groups of words.
We exploit the property that the set of subspaces in n-dimensional Euclidean
space form a curved manifold space called the Grassmannian, a quotient subgroup
of the Lie group of rotations in n- dimensions. Based on this mathematical
model, we develop a modified cosine distance model based on geodesic kernels
that captures relation-specific distances across word categories. Our
experiments on analogy tasks show that our approach performs significantly
better than the previous approaches for the given task.
Complete stability analysis of a heuristic ADP control design
Authors: Yury Sokolov, Robert Kozma, Ludmilla D. Werbos, Paul J. Werbos
Date: 2013-08-15 00:00:00

This paper provides new stability results for Action-Dependent Heuristic
Dynamic Programming (ADHDP), using a control algorithm that iteratively
improves an internal model of the external world in the autonomous system based
on its continuous interaction with the environment. We extend previous results
by ADHDP control to the case of general multi-layer neural networks with deep
learning across all layers. In particular, we show that the introduced control
approach is uniformly ultimately bounded (UUB) under specific conditions on the
learning rates, without explicit constraints on the temporal discount factor.
We demonstrate the benefit of our results to the control of linear and
nonlinear systems, including the cart-pole balancing problem. Our results show
significantly improved learning and control performance as compared to the
state-of-art.
Variational Inference for Gaussian Process Modulated Poisson Processes
Authors: Chris Lloyd, Tom Gunter, Michael A. Osborne, Stephen J. Roberts
Date: 2014-11-02 00:00:00
Tasks: Astronomy, Bayesian Inference, Point Processes, Variational Inference

We present the first fully variational Bayesian inference scheme for
continuous Gaussian-process-modulated Poisson processes. Such point processes
are used in a variety of domains, including neuroscience, geo-statistics and
astronomy, but their use is hindered by the computational cost of existing
inference schemes. Our scheme: requires no discretisation of the domain; scales
linearly in the number of observed events; and is many orders of magnitude
faster than previous sampling based approaches. The resulting algorithm is
shown to outperform standard methods on synthetic examples, coal mining
disaster data and in the prediction of Malaria incidences in Kenya.