pwc-archive/papers-with-abstracts huggingface dataset:
Dimensionality reduction with subgaussian matrices: a unified theory
Authors: Sjoerd Dirksen
Date: 2014-02-17 00:00:00
Tasks: Dimensionality Reduction

We present a theory for Euclidean dimensionality reduction with subgaussian
matrices which unifies several restricted isometry property and
Johnson-Lindenstrauss type results obtained earlier for specific data sets. In
particular, we recover and, in several cases, improve results for sets of
sparse and structured sparse vectors, low-rank matrices and tensors, and smooth
manifolds. In addition, we establish a new Johnson-Lindenstrauss embedding for
data sets taking the form of an infinite union of subspaces of a Hilbert space.
Weyl group orbit functions in image processing
Authors: Goce Chadzitaskos, Lenka Háková, Ondřej Kajínek
Date: 2014-02-17 00:00:00

We deal with the Fourier-like analysis of functions on discrete grids in
two-dimensional simplexes using $C-$ and $E-$ Weyl group orbit functions. For
these cases we present the convolution theorem. We provide an example of
application of image processing using the $C-$ functions and the convolutions
for spatial filtering of the treated image.
Sparse Coding Approach for Multi-Frame Image Super Resolution
Authors: Toshiyuki Kato, Hideitsu Hino, Noboru Murata
Date: 2014-02-17 00:00:00
Tasks: Image Super-Resolution, Multi-Frame Super-Resolution, Super-Resolution

An image super-resolution method from multiple observation of low-resolution
images is proposed. The method is based on sub-pixel accuracy block matching
for estimating relative displacements of observed images, and sparse signal
representation for estimating the corresponding high-resolution image. Relative
displacements of small patches of observed low-resolution images are accurately
estimated by a computationally efficient block matching method. Since the
estimated displacements are also regarded as a warping component of image
degradation process, the matching results are directly utilized to generate
low-resolution dictionary for sparse image representation. The matching scores
of the block matching are used to select a subset of low-resolution patches for
reconstructing a high-resolution patch, that is, an adaptive selection of
informative low-resolution images is realized. When there is only one
low-resolution image, the proposed method works as a single-frame
super-resolution method. The proposed method is shown to perform comparable or
superior to conventional single- and multi-frame super-resolution methods
through experiments using various real-world datasets.
Performance Evaluation of Machine Learning Classifiers in Sentiment Mining
Authors: Vinodhini G Chandrasekaran RM
Date: 2014-02-17 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, text-classification, Text Classification

In recent years, the use of machine learning classifiers is of great value in
solving a variety of problems in text classification. Sentiment mining is a
kind of text classification in which, messages are classified according to
sentiment orientation such as positive or negative. This paper extends the idea
of evaluating the performance of various classifiers to show their
effectiveness in sentiment mining of online product reviews. The product
reviews are collected from Amazon reviews. To evaluate the performance of
classifiers various evaluation methods like random sampling, linear sampling
and bootstrap sampling are used. Our results shows that support vector machine
with bootstrap sampling method outperforms others classifiers and sampling
methods in terms of misclassification rate.
Correlation-based construction of neighborhood and edge features
Authors: Balázs Kégl
Date: 2013-12-20 00:00:00
Tasks: Multi-class Classification

Motivated by an abstract notion of low-level edge detector filters, we
propose a simple method of unsupervised feature construction based on pairwise
statistics of features. In the first step, we construct neighborhoods of
features by regrouping features that correlate. Then we use these subsets as
filters to produce new neighborhood features. Next, we connect neighborhood
features that correlate, and construct edge features by subtracting the
correlated neighborhood features of each other. To validate the usefulness of
the constructed features, we ran AdaBoost.MH on four multi-class classification
problems. Our most significant result is a test error of 0.94% on MNIST with an
algorithm which is essentially free of any image-specific priors. On CIFAR-10
our method is suboptimal compared to today's best deep learning techniques,
nevertheless, we show that the proposed method outperforms not only boosting on
the raw pixels, but also boosting on Haar filters.
Scalable Kernel Clustering: Approximate Kernel k-means
Authors: Radha Chitta, Rong Jin, Timothy C. Havens, Anil K. Jain
Date: 2014-02-16 00:00:00
Tasks: Clustering

Kernel-based clustering algorithms have the ability to capture the non-linear
structure in real world data. Among various kernel-based clustering algorithms,
kernel k-means has gained popularity due to its simple iterative nature and
ease of implementation. However, its run-time complexity and memory footprint
increase quadratically in terms of the size of the data set, and hence, large
data sets cannot be clustered efficiently. In this paper, we propose an
approximation scheme based on randomization, called the Approximate Kernel
k-means. We approximate the cluster centers using the kernel similarity between
a few sampled points and all the points in the data set. We show that the
proposed method achieves better clustering performance than the traditional low
rank kernel approximation based clustering schemes. We also demonstrate that
its running time and memory requirements are significantly lower than those of
kernel k-means, with only a small reduction in the clustering quality on
several public domain large data sets. We then employ ensemble clustering
techniques to further enhance the performance of our algorithm.
Sparse similarity-preserving hashing
Authors: Jonathan Masci, Alex M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro
Date: 2013-12-19 00:00:00

In recent years, a lot of attention has been devoted to efficient nearest
neighbor search by means of similarity-preserving hashing. One of the plights
of existing hashing techniques is the intrinsic trade-off between performance
and computational complexity: while longer hash codes allow for lower false
positive rates, it is very difficult to increase the embedding dimensionality
without incurring in very high false negatives rates or prohibiting
computational costs. In this paper, we propose a way to overcome this
limitation by enforcing the hash codes to be sparse. Sparse high-dimensional
codes enjoy from the low false positive rates typical of long hashes, while
keeping the false negative rates similar to those of a shorter dense hashing
scheme with equal number of degrees of freedom. We use a tailored feed-forward
neural network for the hashing function. Extensive experimental evaluation
involving visual and multi-modal data shows the benefits of the proposed
method.
Unsupervised feature learning by augmenting single images
Authors: Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox
Date: 2013-12-18 00:00:00
Tasks: Data Augmentation, Object Recognition

When deep learning is applied to visual object recognition, data augmentation
is often used to generate additional training data without extra labeling cost.
It helps to reduce overfitting and increase the performance of the algorithm.
In this paper we investigate if it is possible to use data augmentation as the
main component of an unsupervised feature learning architecture. To that end we
sample a set of random image patches and declare each of them to be a separate
single-image surrogate class. We then extend these trivial one-element classes
by applying a variety of transformations to the initial 'seed' patches. Finally
we train a convolutional neural network to discriminate between these surrogate
classes. The feature representation learned by the network can then be used in
various vision tasks. We find that this simple feature learning algorithm is
surprisingly successful, achieving competitive classification results on
several popular vision datasets (STL-10, CIFAR-10, Caltech-101).
Multi-View Priors for Learning Detectors from Sparse Viewpoint Data
Authors: Bojan Pepik, Michael Stark, Peter Gehler, Bernt Schiele
Date: 2013-12-20 00:00:00
Tasks: 3D geometry, Object, Object Localization, Transfer Learning, Viewpoint Estimation

While the majority of today's object class models provide only 2D bounding
boxes, far richer output hypotheses are desirable including viewpoint,
fine-grained category, and 3D geometry estimate. However, models trained to
provide richer output require larger amounts of training data, preferably well
covering the relevant aspects such as viewpoint and fine-grained categories. In
this paper, we address this issue from the perspective of transfer learning,
and design an object class model that explicitly leverages correlations between
visual features. Specifically, our model represents prior distributions over
permissible multi-view detectors in a parametric way -- the priors are learned
once from training data of a source object class, and can later be used to
facilitate the learning of a detector for a target class. As we show in our
experiments, this transfer is not only beneficial for detectors based on
basic-level category representations, but also enables the robust learning of
detectors that represent classes at finer levels of granularity, where training
data is typically even scarcer and more unbalanced. As a result, we report
largely improved performance in simultaneous 2D object localization and
viewpoint estimation on a recent dataset of challenging street scenes.
On the Resilience of an Ant-based System in Fuzzy Environments. An Empirical Study
Authors: Gloria Cerasela Crisan, Camelia-M. Pintea, Petrica C. Pop
Date: 2014-01-19 00:00:00
Tasks: Traveling Salesman Problem

The current work describes an empirical study conducted in order to
investigate the behavior of an optimization method in a fuzzy environment.
MAX-MIN Ant System, an efficient implementation of a heuristic method is used
for solving an optimization problem derived from the Traveling Salesman Problem
(TSP). Several publicly-available symmetric TSP instances and their fuzzy
variants are tested in order to extract some general features. The entry data
was adapted by introducing a two-dimensional systematic degree of fuzziness,
proportional with the number of nodes, the dimension of the instance and also
with the distances between nodes, the scale of the instance. The results show
that our proposed method can handle the data uncertainty, showing good
resilience and adaptability.
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
Authors: Yoav Goldberg, Omer Levy
Date: 2014-02-15 00:00:00
Tasks: Language Modeling, Language Modelling, Word Embeddings

The word2vec software of Tomas Mikolov and colleagues
(https://code.google.com/p/word2vec/ ) has gained a lot of traction lately, and
provides state-of-the-art word embeddings. The learning models behind the
software are described in two research papers. We found the description of the
models in these papers to be somewhat cryptic and hard to follow. While the
motivations and presentation may be obvious to the neural-networks
language-modeling crowd, we had to struggle quite a bit to figure out the
rationale behind the equations.
  This note is an attempt to explain equation (4) (negative sampling) in
"Distributed Representations of Words and Phrases and their Compositionality"
by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado and Jeffrey Dean.
Can recursive neural tensor networks learn logical reasoning?
Authors: Samuel R. Bowman
Date: 2013-12-21 00:00:00
Tasks: Logical Reasoning, Tensor Networks

Recursive neural network models and their accompanying vector representations
for words have seen success in an array of increasingly semantically
sophisticated tasks, but almost nothing is known about their ability to
accurately capture the aspects of linguistic meaning that are necessary for
interpretation or reasoning. To evaluate this, I train a recursive model on a
new corpus of constructed examples of logical reasoning in short sentences,
like the inference of "some animal walks" from "some dog walks" or "some cat
walks," given that dogs and cats are animals. This model learns representations
that generalize well to new types of reasoning pattern in all but a few cases,
a result which is promising for the ability of learned representation models to
capture logical reasoning.
One-Shot-Learning Gesture Recognition using HOG-HOF Features
Authors: Jakub Konečný, Michal Hagara
Date: 2013-12-15 00:00:00
Tasks: Dynamic Time Warping, Gesture Recognition, One-Shot Learning, Optical Flow Estimation

The purpose of this paper is to describe one-shot-learning gesture recognition systems developed on the \textit{ChaLearn Gesture Dataset}. We use RGB and depth images and combine appearance (Histograms of Oriented Gradients) and motion descriptors (Histogram of Optical Flow) for parallel temporal segmentation and recognition. The Quadratic-Chi distance family is used to measure differences between histograms to capture cross-bin relationships. We also propose a new algorithm for trimming videos --- to remove all the unimportant frames from videos. We present two methods that use combination of HOG-HOF descriptors together with variants of Dynamic Time Warping technique. Both methods outperform other published methods and help narrow down the gap between human performance and algorithms on this task. The code has been made publicly available in the MLOSS repository.
A Linguistic Model for Terminology Extraction based Conditional Random Fields
Authors: Fethi Fkih, Mohamed Nazih Omri, Imen Toumia
Date: 2012-09-30 00:00:00

In this paper, we show the possibility of using a linear Conditional Random
Fields (CRF) for terminology extraction from a specialized text corpus.
Parameter estimation based on interval-valued belief structures
Authors: Xinyang Deng, Yong Hu, Felix Chan, Sankaran Mahadevan, Yong Deng
Date: 2014-02-15 00:00:00
Tasks: parameter estimation

Parameter estimation based on uncertain data represented as belief structures
is one of the latest problems in the Dempster-Shafer theory. In this paper, a
novel method is proposed for the parameter estimation in the case where belief
structures are uncertain and represented as interval-valued belief structures.
Within our proposed method, the maximization of likelihood criterion and
minimization of estimated parameter's uncertainty are taken into consideration
simultaneously. As an illustration, the proposed method is employed to estimate
parameters for deterministic and uncertain belief structures, which
demonstrates its effectiveness and versatility.
A Narrative Vehicle Protection Representation for Vehicle Speed Regulator Under Driver Exhaustion -- A Study
Authors: V. Karthikeyan, B. Praveen Kumar, S. Suresh Babu, R. Purusothaman, shijin Thomas
Date: 2014-02-15 00:00:00

Driver fatigue is one of the important factors that cause traffic accidents,
and the ever-increasing number due to diminished drivers vigilance level has
become a problem of serious concern to society. Drivers with a diminished
vigilance level suffer from a marked decline in their abilities of perception,
recognition, and vehicle control, and therefore pose serious danger to their
own life and the lives of other people. Exhaustion resulting from sleep
deprivation or sleep disorders is an important factor in the creasing number of
accidents. In this projected work, we discuss the various methods of the
existing and the proposed method based on a real time online safety prototype
that controls the vehicle speed under driver fatigue. The purpose of such a
model is to advance a system to detect fatigue symptoms in drivers and control
the speed of vehicle to avoid accidents. This system was tested adequately with
subjects of different technology of various researchers finally the validity of
the proposed model for vehicle speed controller based on driver fatigue
detection is shown.
Auto Spell Suggestion for High Quality Speech Synthesis in Hindi
Authors: Shikha Kabra, Ritika Agarwal
Date: 2014-02-15 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Vocal Bursts Intensity Prediction

The goal of Text-to-Speech (TTS) synthesis in a particular language is to
convert arbitrary input text to intelligible and natural sounding speech.
However, for a particular language like Hindi, which is a highly confusing
language (due to very close spellings), it is not an easy task to identify
errors/mistakes in input text and an incorrect text degrade the quality of
output speech hence this paper is a contribution to the development of high
quality speech synthesis with the involvement of Spellchecker which generates
spell suggestions for misspelled words automatically. Involvement of
spellchecker would increase the efficiency of speech synthesis by providing
spell suggestions for incorrect input text. Furthermore, we have provided the
comparative study for evaluating the resultant effect on to phonetic text by
adding spellchecker on to input text.
Finding Coordinated Paths for Multiple Holonomic Agents in 2-d Polygonal Environment
Authors: Pavel Janovský, Michal Čáp, Jiří Vokřínek
Date: 2014-02-14 00:00:00
Tasks: Collision Avoidance, Multi-Agent Path Finding

Avoiding collisions is one of the vital tasks for systems of autonomous
mobile agents. We focus on the problem of finding continuous coordinated paths
for multiple mobile disc agents in a 2-d environment with polygonal obstacles.
The problem is PSPACE-hard, with the state space growing exponentially in the
number of agents. Therefore, the state of the art methods include mainly
reactive techniques and sampling-based iterative algorithms.
  We compare the performance of a widely-used reactive method ORCA with three
variants of a popular planning algorithm RRT* applied to multi-agent path
planning and find that an algorithm combining reactive collision avoidance and
RRT* planning, which we call ORCA-RRT* can be used to solve instances that are
out of the reach of either of the techniques. We experimentally show that: 1)
the reactive part of the algorithm can efficiently solve many multi-agent path
finding problems involving large number of agents, for which RRT* algorithm is
often unable to find a solution in limited time and 2) the planning component
of the algorithm is able to solve many instances containing local minima, where
reactive techniques typically fail.
Automated Fabric Defect Inspection: A Survey of Classifiers
Authors: Md. Tarek Habib, Rahat Hossain Faisal, M. Rokonuzzaman, Farruk Ahmed
Date: 2014-02-14 00:00:00
Tasks: Defect Detection, General Classification, Survey

Quality control at each stage of production in textile industry has become a
key factor to retaining the existence in the highly competitive global market.
Problems of manual fabric defect inspection are lack of accuracy and high time
consumption, where early and accurate fabric defect detection is a significant
phase of quality control. Computer vision based, i.e. automated fabric defect
inspection systems are thought by many researchers of different countries to be
very useful to resolve these problems. There are two major challenges to be
resolved to attain a successful automated fabric defect inspection system. They
are defect detection and defect classification. In this work, we discuss
different techniques used for automated fabric defect classification, then show
a survey of classifiers used in automated fabric defect inspection systems, and
finally, compare these classifiers by using performance metrics. This work is
expected to be very useful for the researchers in the area of automated fabric
defect inspection to understand and evaluate the many potential options in this
field.
Improving Streaming Video Segmentation with Early and Mid-Level Visual Processing
Authors: Subarna Tripathi, Youngbae Hwang, Serge Belongie, Truong Nguyen
Date: 2014-02-14 00:00:00
Tasks: Motion Segmentation, Segmentation, Video Segmentation, Video Semantic Segmentation

Despite recent advances in video segmentation, many opportunities remain to
improve it using a variety of low and mid-level visual cues. We propose
improvements to the leading streaming graph-based hierarchical video
segmentation (streamGBH) method based on early and mid level visual processing.
The extensive experimental analysis of our approach validates the improvement
of hierarchical supervoxel representation by incorporating motion and color
with effective filtering. We also pose and illuminate some open questions
towards intermediate level video analysis as further extension to streamGBH. We
exploit the supervoxels as an initialization towards estimation of dominant
affine motion regions, followed by merging of such motion regions in order to
hierarchically segment a video in a novel motion-segmentation framework which
aims at subsequent applications such as foreground recognition.
Modeling Human Decision-making in Generalized Gaussian Multi-armed Bandits
Authors: Paul Reverdy, Vaibhav Srivastava, Naomi E. Leonard
Date: 2013-07-23 00:00:00
Tasks: Bayesian Inference, Decision Making, Multi-Armed Bandits

We present a formal model of human decision-making in explore-exploit tasks using the context of multi-armed bandit problems, where the decision-maker must choose among multiple options with uncertain rewards. We address the standard multi-armed bandit problem, the multi-armed bandit problem with transition costs, and the multi-armed bandit problem on graphs. We focus on the case of Gaussian rewards in a setting where the decision-maker uses Bayesian inference to estimate the reward values. We model the decision-maker's prior knowledge with the Bayesian prior on the mean reward. We develop the upper credible limit (UCL) algorithm for the standard multi-armed bandit problem and show that this deterministic algorithm achieves logarithmic cumulative expected regret, which is optimal performance for uninformative priors. We show how good priors and good assumptions on the correlation structure among arms can greatly enhance decision-making performance, even over short time horizons. We extend to the stochastic UCL algorithm and draw several connections to human decision-making behavior. We present empirical data from human experiments and show that human performance is efficiently captured by the stochastic UCL algorithm with appropriate parameters. For the multi-armed bandit problem with transition costs and the multi-armed bandit problem on graphs, we generalize the UCL algorithm to the block UCL algorithm and the graphical block UCL algorithm, respectively. We show that these algorithms also achieve logarithmic cumulative expected regret and require a sub-logarithmic expected number of transitions among arms. We further illustrate the performance of these algorithms with numerical examples. NB: Appendix G included in this version details minor modifications that correct for an oversight in the previously-published proofs. The remainder of the text reflects the published work.