pwc-archive/papers-with-abstracts huggingface dataset:
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators
Authors: Kai Zhong, Ian E. H. Yen, Inderjit S. Dhillon, Pradeep Ravikumar
Date: 2014-06-27 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: General Classification, Structured Prediction

We consider the class of optimization problems arising from computationally
intensive L1-regularized M-estimators, where the function or gradient values
are very expensive to compute. A particular instance of interest is the
L1-regularized MLE for learning Conditional Random Fields (CRFs), which are a
popular class of statistical models for varied structured prediction problems
such as sequence labeling, alignment, and classification with label taxonomy.
L1-regularized MLEs for CRFs are particularly expensive to optimize since
computing the gradient values requires an expensive inference step. In this
work, we propose the use of a carefully constructed proximal quasi-Newton
algorithm for such computationally intensive M-estimation problems, where we
employ an aggressive active set selection technique. In a key contribution of
the paper, we show that the proximal quasi-Newton method is provably
super-linearly convergent, even in the absence of strong convexity, by
leveraging a restricted variant of strong convexity. In our experiments, the
proposed algorithm converges considerably faster than current state-of-the-art
on the problems of sequence labeling and hierarchical classification.
Complexity Measures and Concept Learning
Authors: Andreas D. Pape, Kenneth J. Kurtz, Hiroki Sayama
Date: 2014-06-28 00:00:00

The nature of concept learning is a core question in cognitive science.
Theories must account for the relative difficulty of acquiring different
concepts by supervised learners. For a canonical set of six category types, two
distinct orderings of classification difficulty have been found. One ordering,
which we call paradigm-specific, occurs when adult human learners classify
objects with easily distinguishable characteristics such as size, shape, and
shading. The general order occurs in all other known cases: when adult humans
classify objects with characteristics that are not readily distinguished (e.g.,
brightness, saturation, hue); for children and monkeys; and when categorization
difficulty is extrapolated from errors in identification learning. The
paradigm-specific order was found to be predictable mathematically by measuring
the logical complexity of tasks, i.e., how concisely the solution can be
represented by logical rules.
  However, logical complexity explains only the paradigm-specific order but not
the general order. Here we propose a new difficulty measurement, information
complexity, that calculates the amount of uncertainty remaining when a subset
of the dimensions are specified. This measurement is based on Shannon entropy.
We show that, when the metric extracts minimal uncertainties, this new
measurement predicts the paradigm-specific order for the canonical six category
types, and when the metric extracts average uncertainties, this new measurement
predicts the general order. Moreover, for learning category types beyond the
canonical six, we find that the minimal-uncertainty formulation correctly
predicts the paradigm-specific order as well or better than existing metrics
(Boolean complexity and GIST) in most cases.
Unsupervised Segmentation of Multispectral Images with Cellular Automata
Authors: Wuilian Torres, Antonio Rueda-Toicen
Date: 2015-01-23 00:00:00
Tasks: Diversity, General Classification

Multispectral images acquired by satellites are used to study phenomena on
the Earth's surface. Unsupervised classification techniques analyze
multispectral image content without considering prior knowledge of the observed
terrain; this is done using techniques which group pixels that have similar
statistics of digital level distribution in the various image channels. In this
paper, we propose a methodology for unsupervised classification based on a
deterministic cellular automaton. The automaton is initialized in an
unsupervised manner by setting seed cells, selected according to two criteria:
to be representative of the spatial distribution of the dominant elements in
the image, and to take into account the diversity of spectral signatures in the
image. The automaton's evolution is based on an attack rule that is applied
simultaneously to all its cells. Among the noteworthy advantages of
deterministic cellular automata for multispectral processing of satellite
imagery is the consideration of topological information in the image via seed
positioning, and the ability to modify the scale of the study.
A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model
Authors: Umit Rusen Aktas, Mete Ozay, Ales Leonardis, Jeremy L. Wyatt
Date: 2015-01-21 00:00:00
Tasks: Clustering, Descriptive, Retrieval

A graph theoretic approach is proposed for object shape representation in a
hierarchical compositional architecture called Compositional Hierarchy of Parts
(CHOP). In the proposed approach, vocabulary learning is performed using a
hybrid generative-descriptive model. First, statistical relationships between
parts are learned using a Minimum Conditional Entropy Clustering algorithm.
Then, selection of descriptive parts is defined as a frequent subgraph
discovery problem, and solved using a Minimum Description Length (MDL)
principle. Finally, part compositions are constructed by compressing the
internal data representation with discovered substructures. Shape
representation and computational complexity properties of the proposed approach
and algorithms are examined using six benchmark two-dimensional shape image
datasets. Experiments show that CHOP can employ part shareability and indexing
mechanisms for fast inference of part compositions using learned shape
vocabularies. Additionally, CHOP provides better shape retrieval performance
than the state-of-the-art shape retrieval methods.
Taking a Deeper Look at Pedestrians
Authors: Jan Hosang, Mohamed Omran, Rodrigo Benenson, Bernt Schiele
Date: 2015-01-23 00:00:00
Proceeding: CVPR 2015 6
Tasks: Optical Flow Estimation, Pedestrian Detection

In this paper we study the use of convolutional neural networks (convnets)
for the task of pedestrian detection. Despite their recent diverse successes,
convnets historically underperform compared to other pedestrian detectors. We
deliberately omit explicitly modelling the problem into the network (e.g. parts
or occlusion modelling) and show that we can reach competitive performance
without bells and whistles. In a wide range of experiments we analyse small and
big convnets, their architectural choices, parameters, and the influence of
different training data, including pre-training on surrogate tasks.
  We present the best convnet detectors on the Caltech and KITTI dataset. On
Caltech our convnets reach top performance both for the Caltech1x and
Caltech10x training setup. Using additional data at training time our strongest
convnet model is competitive even to detectors that use additional data
(optical flow) at test time.
Filtered Channel Features for Pedestrian Detection
Authors: Shanshan Zhang, Rodrigo Benenson, Bernt Schiele
Date: 2015-01-23 00:00:00
Tasks: Optical Flow Estimation, Pedestrian Detection

This paper starts from the observation that multiple top performing
pedestrian detectors can be modelled by using an intermediate layer filtering
low-level features in combination with a boosted decision forest. Based on this
observation we propose a unifying framework and experimentally explore
different filter families. We report extensive results enabling a systematic
analysis.
  Using filtered channel features we obtain top performance on the challenging
Caltech and KITTI datasets, while using only HOG+LUV as low-level features.
When adding optical flow features we further improve detection quality and
report the best known results on the Caltech dataset, reaching 93% recall at 1
FPPI.
Application of the SP theory of intelligence to the understanding of natural vision and the development of computer vision
Authors: J. Gerard Wolff
Date: 2013-03-08 00:00:00

The SP theory of intelligence aims to simplify and integrate concepts in
computing and cognition, with information compression as a unifying theme. This
article discusses how it may be applied to the understanding of natural vision
and the development of computer vision. The theory, which is described quite
fully elsewhere, is described here in outline but with enough detail to ensure
that the rest of the article makes sense.
  Low level perceptual features such as edges or corners may be identified by
the extraction of redundancy in uniform areas in a manner that is comparable
with the run-length encoding technique for information compression.
  The concept of multiple alignment in the SP theory may be applied to the
recognition of objects, and to scene analysis, with a hierarchy of parts and
sub-parts, and at multiple levels of abstraction.
  The theory has potential for the unsupervised learning of visual objects and
classes of objects, and suggests how coherent concepts may be derived from
fragments.
  As in natural vision, both recognition and learning in the SP system is
robust in the face of errors of omission, commission and substitution.
  The theory suggests how, via vision, we may piece together a knowledge of the
three-dimensional structure of objects and of our environment, it provides an
account of how we may see things that are not objectively present in an image,
and how we recognise something despite variations in the size of its retinal
image. And it has things to say about the phenomena of lightness constancy and
colour constancy, the role of context in recognition, and ambiguities in visual
perception.
  A strength of the SP theory is that it provides for the integration of vision
with other sensory modalities and with other aspects of intelligence.
Bayesian Learning for Low-Rank matrix reconstruction
Authors: Martin Sundin, Cristian R. Rojas, Magnus Jansson, Saikat Chatterjee
Date: 2015-01-23 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

We develop latent variable models for Bayesian learning based low-rank matrix
completion and reconstruction from linear measurements. For under-determined
systems, the developed methods are shown to reconstruct low-rank matrices when
neither the rank nor the noise power is known a-priori. We derive relations
between the latent variable models and several low-rank promoting penalty
functions. The relations justify the use of Kronecker structured covariance
matrices in a Gaussian based prior. In the methods, we use evidence
approximation and expectation-maximization to learn the model parameters. The
performance of the methods is evaluated through extensive numerical
simulations.
Autonomous robots and the SP theory of intelligence
Authors: J. Gerard Wolff
Date: 2014-09-29 00:00:00
Tasks: Information Retrieval, Retrieval

This article is about how the "SP theory of intelligence" and its realisation
in the "SP machine" (both outlined in the article) may help to solve
computer-related problems in the design of autonomous robots, meaning robots
that do not depend on external intelligence or power supplies, are mobile, and
are designed to exhibit as much human-like intelligence as possible. The
article is about: how to increase the computational and energy efficiency of
computers and reduce their bulk; how to achieve human-like versatility in
intelligence; and likewise for human-like adaptability in intelligence. The SP
system has potential for substantial gains in computational and energy
efficiency and reductions in the bulkiness of computers: by reducing the size
of data to be processed; by exploiting statistical information that the system
gathers; and via an updated version of Donald Hebb's concept of a "cell
assembly". Towards human-like versatility in intelligence, the SP system has
strengths in unsupervised learning, natural language processing, pattern
recognition, information retrieval, several kinds of reasoning, planning,
problem solving, and more, with seamless integration amongst structures and
functions. The SP system's strengths in unsupervised learning and other aspects
of intelligence may help to achieve human-like adaptability in intelligence
via: the learning of natural language; learning to see; building 3D models of
objects and of a robot's surroundings; learning regularities in the workings of
a robot and in the robot's environment; exploration and play; learning major
skills; and secondary forms of learning. Also discussed are: how the SP system
may process parallel streams of information; generalisation of knowledge,
correction of over-generalisations, and learning from dirty data; how to cut
the cost of learning; and reinforcements, motivations, goals, and
demonstration.
Uncertainty in Ontology Matching: A Decision Rule-Based Approach
Authors: Amira Essaid, Arnaud Martin, Grégory Smits, Boutheina Ben Yaghlane
Date: 2015-01-23 00:00:00
Tasks: Ontology Matching

Considering the high heterogeneity of the ontologies pub-lished on the web,
ontology matching is a crucial issue whose aim is to establish links between an
entity of a source ontology and one or several entities from a target ontology.
Perfectible similarity measures, consid-ered as sources of information, are
combined to establish these links. The theory of belief functions is a powerful
mathematical tool for combining such uncertain information. In this paper, we
introduce a decision pro-cess based on a distance measure to identify the best
possible matching entities for a given source entity.
On the Expressive Efficiency of Sum Product Networks
Authors: James Martens, Venkatesh Medabalimi
Date: 2014-11-27 00:00:00

Sum Product Networks (SPNs) are a recently developed class of deep generative
models which compute their associated unnormalized density functions using a
special type of arithmetic circuit. When certain sufficient conditions, called
the decomposability and completeness conditions (or "D&C" conditions), are
imposed on the structure of these circuits, marginal densities and other useful
quantities, which are typically intractable for other deep generative models,
can be computed by what amounts to a single evaluation of the network (which is
a property known as "validity"). However, the effect that the D&C conditions
have on the capabilities of D&C SPNs is not well understood.
  In this work we analyze the D&C conditions, expose the various connections
that D&C SPNs have with multilinear arithmetic circuits, and consider the
question of how well they can capture various distributions as a function of
their size and depth. Among our various contributions is a result which
establishes the existence of a relatively simple distribution with fully
tractable marginal densities which cannot be efficiently captured by D&C SPNs
of any depth, but which can be efficiently captured by various other deep
generative models. We also show that with each additional layer of depth
permitted, the set of distributions which can be efficiently captured by D&C
SPNs grows in size. This kind of "depth hierarchy" property has been widely
conjectured to hold for various deep models, but has never been proven for any
of them. Some of our other contributions include a new characterization of the
D&C conditions as sufficient and necessary ones for a slightly strengthened
notion of validity, and various state-machine characterizations of the types of
computations that can be performed efficiently by D&C SPNs.
Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models
Authors: Paul Honeine, Fei Zhu
Date: 2015-01-22 00:00:00

Nonnegative matrix factorization (NMF) is a powerful class of feature
extraction techniques that has been successfully applied in many fields, namely
in signal and image processing. Current NMF techniques have been limited to a
single-objective problem in either its linear or nonlinear kernel-based
formulation. In this paper, we propose to revisit the NMF as a multi-objective
problem, in particular a bi-objective one, where the objective functions
defined in both input and feature spaces are taken into account. By taking the
advantage of the sum-weighted method from the literature of multi-objective
optimization, the proposed bi-objective NMF determines a set of nondominated,
Pareto optimal, solutions instead of a single optimal decomposition. Moreover,
the corresponding Pareto front is studied and approximated. Experimental
results on unmixing real hyperspectral images confirm the efficiency of the
proposed bi-objective NMF compared with the state-of-the-art methods.
A New Efficient Method for Calculating Similarity Between Web Services
Authors: T. Rachad, J. Boutahar, S. El ghazi
Date: 2015-01-22 00:00:00

Web services allow communication between heterogeneous systems in a
distributed environment. Their enormous success and their increased use led to
the fact that thousands of Web services are present on the Internet. This
significant number of Web services which not cease to increase has led to
problems of the difficulty in locating and classifying web services, these
problems are encountered mainly during the operations of web services discovery
and substitution. Traditional ways of search based on keywords are not
successful in this context, their results do not support the structure of Web
services and they consider in their search only the identifiers of the web
service description language (WSDL) interface elements. The methods based on
semantics (WSDLS, OWLS, SAWSDL...) which increase the WSDL description of a Web
service with a semantic description allow raising partially this problem, but
their complexity and difficulty delays their adoption in real cases. Measuring
the similarity between the web services interfaces is the most suitable
solution for this kind of problems, it will classify available web services so
as to know those that best match the searched profile and those that do not
match. Thus, the main goal of this work is to study the degree of similarity
between any two web services by offering a new method that is more effective
than existing works.
An Offline Technique for Localization of License Plates for Indian Commercial Vehicles
Authors: Satadal Saha, Subhadip Basu, Mita Nasipuri, Dipak Kumar Basu
Date: 2010-03-04 00:00:00
Tasks: License Plate Recognition, Optical Character Recognition, Optical Character Recognition (OCR)

Automatic License Plate Recognition (ALPR) is a challenging area of research
due to its importance to variety of commercial applications. The overall
problem may be subdivided into two key modules, firstly, localization of
license plates from vehicle images, and secondly, optical character recognition
of extracted license plates. In the current work, we have concentrated on the
first part of the problem, i.e., localization of license plate regions from
Indian commercial vehicles as a significant step towards development of a
complete ALPR system for Indian vehicles. The technique is based on color based
segmentation of vehicle images and identification of potential license plate
regions. True license plates are finally localized based on four spatial and
horizontal contrast features. The technique successfully localizes the actual
license plates in 73.4% images.
Development of an automated Red Light Violation Detection System (RLVDS) for Indian vehicles
Authors: Satadal Saha, Subhadip Basu, Mita Nasipuri, Dipak Kumar Basu
Date: 2010-03-31 00:00:00
Tasks: Management

Integrated Traffic Management Systems (ITMS) are now implemented in different
cities in India to primarily address the concerns of road-safety and security.
An automated Red Light Violation Detection System (RLVDS) is an integral part
of the ITMS. In our present work we have designed and developed a complete
system for generating the list of all stop-line violating vehicle images
automatically from video snapshots of road-side surveillance cameras. The
system first generates adaptive background images for each camera view,
subtracts captured images from the corresponding background images and analyses
potential occlusions over the stop-line in a traffic signal. Considering
round-the-clock operations in a real-life test environment, the developed
system could successfully track 92% images of vehicles with violations on the
stop-line in a "Red" traffic signal.
A novel scheme for binarization of vehicle images using hierarchical histogram equalization technique
Authors: Satadal Saha, Subhadip Basu, Mita Nasipuri, Dipak Kumar Basu
Date: 2010-03-31 00:00:00
Tasks: Binarization, License Plate Recognition

Automatic License Plate Recognition system is a challenging area of research
now-a-days and binarization is an integral and most important part of it. In
case of a real life scenario, most of existing methods fail to properly
binarize the image of a vehicle in a congested road, captured through a CCD
camera. In the current work we have applied histogram equalization technique
over the complete image and also over different hierarchy of image
partitioning. A novel scheme is formulated for giving the membership value to
each pixel for each hierarchy of histogram equalization. Then the image is
binarized depending on the net membership value of each pixel. The technique is
exhaustively evaluated on the vehicle image dataset as well as the license
plate dataset, giving satisfactory performances.
A Collaborative Kalman Filter for Time-Evolving Dyadic Processes
Authors: San Gultekin, John Paisley
Date: 2015-01-22 00:00:00
Tasks: Collaborative Filtering

We present the collaborative Kalman filter (CKF), a dynamic model for
collaborative filtering and related factorization models. Using the matrix
factorization approach to collaborative filtering, the CKF accounts for time
evolution by modeling each low-dimensional latent embedding as a
multidimensional Brownian motion. Each observation is a random variable whose
distribution is parameterized by the dot product of the relevant Brownian
motions at that moment in time. This is naturally interpreted as a Kalman
filter with multiple interacting state space vectors. We also present a method
for learning a dynamically evolving drift parameter for each location by
modeling it as a geometric Brownian motion. We handle posterior intractability
via a mean-field variational approximation, which also preserves tractability
for downstream calculations in a manner similar to the Kalman filter. We
evaluate the model on several large datasets, providing quantitative evaluation
on the 10 million Movielens and 100 million Netflix datasets and qualitative
evaluation on a set of 39 million stock returns divided across roughly 6,500
companies from the years 1962-2014.
Unsupervised image segmentation by Global and local Criteria Optimization Based on Bayesian Networks
Authors: Mohamed Ali Mahjoub, Mohamed Mhiri
Date: 2015-01-22 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Superpixels, Unsupervised Image Segmentation

Today Bayesian networks are more used in many areas of decision support and
image processing. In this way, our proposed approach uses Bayesian Network to
modelize the segmented image quality. This quality is calculated on a set of
attributes that represent local evaluation measures. The idea is to have these
local levels chosen in a way to be intersected into them to keep the overall
appearance of segmentation. The approach operates in two phases: the first
phase is to make an over-segmentation which gives superpixels card. In the
second phase, we model the superpixels by a Bayesian Network. To find the
segmented image with the best overall quality we used two approximate inference
methods, the first using ICM algorithm which is widely used in Markov Models
and a second is a recursive method called algorithm of model decomposition
based on max-product algorithm which is very popular in the recent works of
image segmentation. For our model, we have shown that the composition of these
two algorithms leads to good segmentation performance.