pwc-archive/papers-with-abstracts huggingface dataset:
GraATP: A Graph Theoretic Approach for Automated Theorem Proving in Plane Geometry
Authors: Mohammad Murtaza Mahmud, Swakkhar Shatabda, Mohammad Nurul Huda
Date: 2014-12-18 00:00:00
Tasks: Automated Theorem Proving

Automated Theorem Proving (ATP) is an established branch of Artificial
Intelligence. The purpose of ATP is to design a system which can automatically
figure out an algorithm either to prove or disprove a mathematical claim, on
the basis of a set of given premises, using a set of fundamental postulates and
following the method of logical inference. In this paper, we propose GraATP, a
generalized framework for automated theorem proving in plane geometry. Our
proposed method translates the geometric entities into nodes of a graph and the
relations between them as edges of that graph. The automated system searches
for different ways to reach the conclusion for a claim via graph traversal by
which the validity of the geometric theorem is examined.
Quantized Matrix Completion for Personalized Learning
Authors: Andrew S. Lan, Christoph Studer, Richard G. Baraniuk
Date: 2014-12-18 00:00:00
Tasks: Matrix Completion

The recently proposed SPARse Factor Analysis (SPARFA) framework for
personalized learning performs factor analysis on ordinal or binary-valued
(e.g., correct/incorrect) graded learner responses to questions. The underlying
factors are termed "concepts" (or knowledge components) and are used for
learning analytics (LA), the estimation of learner concept-knowledge profiles,
and for content analytics (CA), the estimation of question-concept associations
and question difficulties. While SPARFA is a powerful tool for LA and CA, it
requires a number of algorithm parameters (including the number of concepts),
which are difficult to determine in practice. In this paper, we propose
SPARFA-Lite, a convex optimization-based method for LA that builds on matrix
completion, which only requires a single algorithm parameter and enables us to
automatically identify the required number of concepts. Using a variety of
educational datasets, we demonstrate that SPARFALite (i) achieves comparable
performance in predicting unobserved learner responses to existing methods,
including item response theory (IRT) and SPARFA, and (ii) is computationally
more efficient.
Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics
Authors: Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk
Date: 2014-12-18 00:00:00
Tasks: BIG-bench Machine Learning, Collaborative Filtering, TAG

Machine learning offers novel ways and means to design personalized learning
systems wherein each student's educational experience is customized in real
time depending on their background, learning goals, and performance to date.
SPARse Factor Analysis (SPARFA) is a novel framework for machine learning-based
learning analytics, which estimates a learner's knowledge of the concepts
underlying a domain, and content analytics, which estimates the relationships
among a collection of questions and those concepts. SPARFA jointly learns the
associations among the questions and the concepts, learner concept knowledge
profiles, and the underlying question difficulties, solely based on the
correct/incorrect graded responses of a population of learners to a collection
of questions. In this paper, we extend the SPARFA framework significantly to
enable: (i) the analysis of graded responses on an ordinal scale (partial
credit) rather than a binary scale (correct/incorrect); (ii) the exploitation
of tags/labels for questions that partially describe the question{concept
associations. The resulting Ordinal SPARFA-Tag framework greatly enhances the
interpretability of the estimated concepts. We demonstrate using real
educational data that Ordinal SPARFA-Tag outperforms both SPARFA and existing
collaborative filtering techniques in predicting missing learner responses.
Large Scale Distributed Distance Metric Learning
Authors: Pengtao Xie, Eric Xing
Date: 2014-12-18 00:00:00
Tasks: CPU, Metric Learning

In large scale machine learning and data mining problems with high feature
dimensionality, the Euclidean distance between data points can be
uninformative, and Distance Metric Learning (DML) is often desired to learn a
proper similarity measure (using side information such as example data pairs
being similar or dissimilar). However, high dimensionality and large volume of
pairwise constraints in modern big data can lead to prohibitive computational
cost for both the original DML formulation in Xing et al. (2002) and later
extensions. In this paper, we present a distributed algorithm for DML, and a
large-scale implementation on a parameter server architecture. Our approach
builds on a parallelizable reformulation of Xing et al. (2002), and an
asynchronous stochastic gradient descent optimization procedure. To our
knowledge, this is the first distributed solution to DML, and we show that, on
a system with 256 CPU cores, our program is able to complete a DML task on a
dataset with 1 million data points, 22-thousand features, and 200 million
labeled data pairs, in 15 hours; and the learned metric shows great
effectiveness in properly measuring distances.
Deep Recurrent Neural Networks for Time Series Prediction
Authors: Sharat C. Prasad, Piyush Prasad
Date: 2014-07-22 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Prediction

Ability of deep networks to extract high level features and of recurrent
networks to perform time-series inference have been studied. In view of
universality of one hidden layer network at approximating functions under weak
constraints, the benefit of multiple layers is to enlarge the space of
dynamical systems approximated or, given the space, reduce the number of units
required for a certain error. Traditionally shallow networks with manually
engineered features are used, back-propagation extent is limited to one and
attempt to choose a large number of hidden units to satisfy the Markov
condition is made. In case of Markov models, it has been shown that many
systems need to be modeled as higher order. In the present work, we present
deep recurrent networks with longer backpropagation through time extent as a
solution to modeling systems that are high order and to predicting ahead. We
study epileptic seizure suppression electro-stimulator. Extraction of manually
engineered complex features and prediction employing them has not allowed small
low-power implementations as, to avoid possibility of surgery, extraction of
any features that may be required has to be included. In this solution, a
recurrent neural network performs both feature extraction and prediction. We
prove analytically that adding hidden layers or increasing backpropagation
extent increases the rate of decrease of approximation error. A Dynamic
Programming (DP) training procedure employing matrix operations is derived. DP
and use of matrix operations makes the procedure efficient particularly when
using data-parallel computing. The simulation studies show the geometry of the
parameter space, that the network learns the temporal structure, that
parameters converge while model output displays same dynamic behavior as the
system and greater than .99 Average Detection Rate on all real seizure data
tried.
Structured Learning Modulo Theories
Authors: Stefano Teso, Roberto Sebastiani, Andrea Passerini
Date: 2014-05-07 00:00:00

Modelling problems containing a mixture of Boolean and numerical variables is
a long-standing interest of Artificial Intelligence. However, performing
inference and learning in hybrid domains is a particularly daunting task. The
ability to model this kind of domains is crucial in "learning to design" tasks,
that is, learning applications where the goal is to learn from examples how to
perform automatic {\em de novo} design of novel objects. In this paper we
present Structured Learning Modulo Theories, a max-margin approach for learning
in hybrid domains based on Satisfiability Modulo Theories, which allows to
combine Boolean reasoning and optimization over continuous linear arithmetical
constraints. The main idea is to leverage a state-of-the-art generalized
Satisfiability Modulo Theory solver for implementing the inference and
separation oracles of Structured Output SVMs. We validate our method on
artificial and real world scenarios.
A theoretical basis for efficient computations with noisy spiking neurons
Authors: Zeno Jonke, Stefan Habenschuss, Wolfgang Maass
Date: 2014-12-18 00:00:00
Tasks: Traveling Salesman Problem

Network of neurons in the brain apply - unlike processors in our current
generation of computer hardware - an event-based processing strategy, where
short pulses (spikes) are emitted sparsely by neurons to signal the occurrence
of an event at a particular point in time. Such spike-based computations
promise to be substantially more power-efficient than traditional clocked
processing schemes. However it turned out to be surprisingly difficult to
design networks of spiking neurons that are able to carry out demanding
computations. We present here a new theoretical framework for organizing
computations of networks of spiking neurons. In particular, we show that a
suitable design enables them to solve hard constraint satisfaction problems
from the domains of planning - optimization and verification - logical
inference. The underlying design principles employ noise as a computational
resource. Nevertheless the timing of spikes (rather than just spike rates)
plays an essential role in the resulting computations. Furthermore, one can
demonstrate for the Traveling Salesman Problem a surprising computational
advantage of networks of spiking neurons compared with traditional artificial
neural networks and Gibbs sampling. The identification of such advantage has
been a well-known open problem.
Contour Detection Using Contrast Formulas in the Framework of Logarithmic Models
Authors: Vasile Patrascu
Date: 2014-12-18 00:00:00
Tasks: Contour Detection, Edge Detection

In this paper we use a new logarithmic model of image representation,
developed in [1,2], for edge detection. In fact, in the framework of the new
model we obtain the formulas for computing the "contrast of a pixel" and the
"contrast" image is just the "contour" or edge image. In our setting the range
of values is preserved and the quality of the contour is good for high as well
as for low luminosity regions. We present the comparison of our results with
the results using classical edge detection operators.
Image Enhancement Using a Generalization of Homographic Function
Authors: Vasile Patrascu
Date: 2014-12-18 00:00:00
Tasks: Image Enhancement

This paper presents a new method of gray level image enhancement, based on
point transforms. In order to define the transform function, it was used a
generalization of the homographic function.
Image enhancement using the mean dynamic range maximization with logarithmic operations
Authors: Vasile Patrascu
Date: 2014-12-18 00:00:00
Tasks: Image Enhancement

In this paper we use a logarithmic model for gray level image enhancement. We
begin with a short presentation of the model and then, we propose a new formula
for the mean dynamic range. After that we present two image transforms: one
performs an optimal enhancement of the mean dynamic range using the logarithmic
addition, and the other does the same for positive and negative values using
the logarithmic scalar multiplication. We present the comparison of the results
obtained by dynamic ranges optimization with the results obtained using
classical image enhancement methods like gamma correction and histogram
equalization.
Gray Level Image Enhancement Using Polygonal Functions
Authors: Vasile Patrascu
Date: 2014-12-18 00:00:00
Tasks: Image Enhancement

This paper presents a method for enhancing the gray level images. This method
takes part from the category of point transforms and it is based on
interpolation functions. The latter have a graphic represented by polygonal
lines. The interpolation nodes of these functions are calculated taking into
account the statistics of gray levels belonging to the image.
Gray level image enhancement using the Bernstein polynomials
Authors: Vasile Patrascu
Date: 2014-12-18 00:00:00
Tasks: Image Enhancement

This paper presents a method for enhancing the gray level images. This
presented method takes part from the category of point operations and it is
based on piecewise linear functions. The interpolation nodes of these functions
are calculated using the Bernstein polynomials.
Image Dynamic Range Enhancement in the Context of Logarithmic Models
Authors: Vasile Patrascu, Vasile Buzuloiu
Date: 2014-12-18 00:00:00

Images of a scene observed under a variable illumination or with a variable
optical aperture are not identical. Does a privileged representant exist? In
which mathematical context? How to obtain it? The authors answer to such
questions in the context of logarithmic models for images. After a short
presentation of the model, the paper presents two image transforms: one
performs an optimal enhancement of the dynamic range, and the other does the
same for the mean dynamic range. Experimental results are shown.
Deep Convolutional Neural Fields for Depth Estimation from a Single Image
Authors: Fayao Liu, Chunhua Shen, Guosheng Lin
Date: 2014-11-24 00:00:00
Proceeding: CVPR 2015 6
Tasks: Depth Estimation

We consider the problem of depth estimation from a single monocular image in
this work. It is a challenging task as no reliable depth cues are available,
e.g., stereo correspondences, motions, etc. Previous efforts have been focusing
on exploiting geometric priors or additional sources of information, with all
using hand-crafted features. Recently, there is mounting evidence that features
from deep convolutional neural networks (CNN) are setting new records for
various vision applications. On the other hand, considering the continuous
characteristic of the depth values, depth estimations can be naturally
formulated into a continuous conditional random field (CRF) learning problem.
Therefore, we in this paper present a deep convolutional neural field model for
estimating depths from a single image, aiming to jointly explore the capacity
of deep CNN and continuous CRF. Specifically, we propose a deep structured
learning scheme which learns the unary and pairwise potentials of continuous
CRF in a unified deep CNN framework.
  The proposed method can be used for depth estimations of general scenes with
no geometric priors nor any extra information injected. In our case, the
integral of the partition function can be analytically calculated, thus we can
exactly solve the log-likelihood optimization. Moreover, solving the MAP
problem for predicting depths of a new image is highly efficient as closed-form
solutions exist. We experimentally demonstrate that the proposed method
outperforms state-of-the-art depth estimation methods on both indoor and
outdoor scene datasets.
Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm
Authors: Jiaqi Zhao, Vitor Basto Fernandes, Licheng Jiao, Iryna Yevseyeva, Asep Maulana, Rui Li, Thomas Bäck, Michael T. M. Emmerich
Date: 2014-12-18 00:00:00
Tasks: Binary Classification, Classification, Evolutionary Algorithms, feature selection, General Classification, Multi-class Classification, Multiobjective Optimization

Finding a good classifier is a multiobjective optimization problem with
different error rates and the costs to be minimized. The receiver operating
characteristic is widely used in the machine learning community to analyze the
performance of parametric classifiers or sets of Pareto optimal classifiers. In
order to directly compare two sets of classifiers the area (or volume) under
the convex hull can be used as a scalar indicator for the performance of a set
of classifiers in receiver operating characteristic space.
  Recently, the convex hull based multiobjective genetic programming algorithm
was proposed and successfully applied to maximize the convex hull area for
binary classification problems. The contribution of this paper is to extend
this algorithm for dealing with higher dimensional problem formulations. In
particular, we discuss problems where parsimony (or classifier complexity) is
stated as a third objective and multi-class classification with three different
true classification rates to be maximized.
  The design of the algorithm proposed in this paper is inspired by
indicator-based evolutionary algorithms, where first a performance indicator
for a solution set is established and then a selection operator is designed
that complies with the performance indicator. In this case, the performance
indicator will be the volume under the convex hull. The algorithm is tested and
analyzed in a proof of concept study on different benchmarks that are designed
for measuring its capability to capture relevant parts of a convex hull.
  Further benchmark and application studies on email classification and feature
selection round up the analysis and assess robustness and usefulness of the new
algorithm in real world settings.
High Frequency Content based Stimulus for Perceptual Sharpness Assessment in Natural Images
Authors: Ashirbani Saha, Q. M. Jonathan Wu
Date: 2014-12-17 00:00:00

A blind approach to evaluate the perceptual sharpness present in a natural
image is proposed. Though the literature demonstrates a set of variegated
visual cues to detect or evaluate the absence or presence of sharpness, we
emphasize in the current work that high frequency content and local standard
deviation can form strong features to compute perceived sharpness in any
natural image, and can be considered an able alternative for the existing cues.
Unsharp areas in a natural image happen to exhibit uniform intensity or lack of
sharp changes between regions. Sharp region transitions in an image are caused
by the presence of spatial high frequency content. Therefore, in the proposed
approach, we hypothesize that using the high frequency content as the principal
stimulus, the perceived sharpness can be quantified in an image. When an image
is convolved with a high pass filter, higher values at any pixel location
signify the presence of high frequency content at those locations. Considering
these values as the stimulus, the exponent of the stimulus is weighted by local
standard deviation to impart the contribution of the local contrast within the
formation of the sharpness map. The sharpness map highlights the relatively
sharper regions in the image and is used to calculate the perceived sharpness
score of the image. The advantages of the proposed method lie in its use of
simple visual cues of high frequency content and local contrast to arrive at
the perceptual score, and requiring no training with the images. The promise of
the proposed method is demonstrated by its ability to compute perceived
sharpness for within image and across image sharpness changes and for blind
evaluation of perceptual degradation resulting due to presence of blur.
Experiments conducted on several databases demonstrate improved performance of
the proposed method over that of the state-of-the-art techniques.
cuDNN: Efficient Primitives for Deep Learning
Authors: Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer
Date: 2014-10-03 00:00:00
Tasks: Deep Learning

We present a library of efficient implementations of deep learning
primitives. Deep learning workloads are computationally intensive, and
optimizing their kernels is difficult and time-consuming. As parallel
architectures evolve, kernels must be reoptimized, which makes maintaining
codebases difficult over time. Similar issues have long been addressed in the
HPC community by libraries such as the Basic Linear Algebra Subroutines (BLAS).
However, there is no analogous library for deep learning. Without such a
library, researchers implementing deep learning workloads on parallel
processors must create and optimize their own implementations of the main
computational kernels, and this work must be repeated as new parallel
processors emerge. To address this problem, we have created a library similar
in intent to BLAS, with optimized routines for deep learning workloads. Our
implementation contains routines for GPUs, although similarly to the BLAS
library, these routines could be implemented for other platforms. The library
is easy to integrate into existing frameworks, and provides optimized
performance and memory usage. For example, integrating cuDNN into Caffe, a
popular framework for convolutional networks, improves performance by 36% on a
standard model while also reducing memory consumption.
Efficient Visual Coding: From Retina To V2
Authors: Honghao Shan, Garrison Cottrell
Date: 2013-12-20 00:00:00

The human visual system has a hierarchical structure consisting of layers of
processing, such as the retina, V1, V2, etc. Understanding the functional roles
of these visual processing layers would help to integrate the
psychophysiological and neurophysiological models into a consistent theory of
human vision, and would also provide insights to computer vision research. One
classical theory of the early visual pathway hypothesizes that it serves to
capture the statistical structure of the visual inputs by efficiently coding
the visual information in its outputs. Until recently, most computational
models following this theory have focused upon explaining the receptive field
properties of one or two visual layers. Recent work in deep networks has
eliminated this concern, however, there is till the retinal layer to consider.
Here we improve on a previously-described hierarchical model Recursive ICA
(RICA) [1] which starts with PCA, followed by a layer of sparse coding or ICA,
followed by a component-wise nonlinearity derived from considerations of the
variable distributions expected by ICA. This process is then repeated. In this
work, we improve on this model by using a new version of sparse PCA (sPCA),
which results in biologically-plausible receptive fields for both the sPCA and
ICA/sparse coding. When applied to natural image patches, our model learns
visual features exhibiting the receptive field properties of retinal ganglion
cells/lateral geniculate nucleus (LGN) cells, V1 simple cells, V1 complex
cells, and V2 cells. Our work provides predictions for experimental
neuroscience studies. For example, our result suggests that a previous
neurophysiological study improperly discarded some of their recorded neurons;
we predict that their discarded neurons capture the shape contour of objects.
Road Detection by One-Class Color Classification: Dataset and Experiments
Authors: Jose M. Alvarez, Theo Gevers, Antonio M. Lopez
Date: 2014-12-11 00:00:00
Tasks: Autonomous Driving, Classification, General Classification

Detecting traversable road areas ahead a moving vehicle is a key process for
modern autonomous driving systems. A common approach to road detection consists
of exploiting color features to classify pixels as road or background. These
algorithms reduce the effect of lighting variations and weather conditions by
exploiting the discriminant/invariant properties of different color
representations. Furthermore, the lack of labeled datasets has motivated the
development of algorithms performing on single images based on the assumption
that the bottom part of the image belongs to the road surface.
  In this paper, we first introduce a dataset of road images taken at different
times and in different scenarios using an onboard camera. Then, we devise a
simple online algorithm and conduct an exhaustive evaluation of different
classifiers and the effect of using different color representation to
characterize pixels.