pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Multiscale Gaussian Process Regression using Hierarchical Clustering
Authors: Z. Zhang, K. Duraisamy, N. A. Gumerov
Date: 2015-11-06 00:00:00
Tasks: Clustering, regression

Standard Gaussian Process (GP) regression, a powerful machine learning tool,
is computationally expensive when it is applied to large datasets, and
potentially inaccurate when data points are sparsely distributed in a
high-dimensional feature space. To address these challenges, a new multiscale,
sparsified GP algorithm is formulated, with the goal of application to large
scientific computing datasets. In this approach, the data is partitioned into
clusters and the cluster centers are used to define a reduced training set,
resulting in an improvement over standard GPs in terms of training and
evaluation costs. Further, a hierarchical technique is used to adaptively map
the local covariance representation to the underlying sparsity of the feature
space, leading to improved prediction accuracy when the data distribution is
highly non-uniform. A theoretical investigation of the computational complexity
of the algorithm is presented. The efficacy of this method is then demonstrated
on smooth and discontinuous analytical functions and on data from a direct
numerical simulation of turbulent combustion.
Confidence-Constrained Maximum Entropy Framework for Learning from Multi-Instance Data
Authors: Behrouz Behmardi, Forrest Briggs, Xiaoli Z. Fern, Raviv Raich
Date: 2016-03-07 00:00:00

Multi-instance data, in which each object (bag) contains a collection of
instances, are widespread in machine learning, computer vision, bioinformatics,
signal processing, and social sciences. We present a maximum entropy (ME)
framework for learning from multi-instance data. In this approach each bag is
represented as a distribution using the principle of ME. We introduce the
concept of confidence-constrained ME (CME) to simultaneously learn the
structure of distribution space and infer each distribution. The shared
structure underlying each density is used to learn from instances inside each
bag. The proposed CME is free of tuning parameters. We devise a fast
optimization algorithm capable of handling large scale multi-instance data. In
the experimental section, we evaluate the performance of the proposed approach
in terms of exact rank recovery in the space of distributions and compare it
with the regularized ME approach. Moreover, we compare the performance of CME
with Multi-Instance Learning (MIL) state-of-the-art algorithms and show a
comparable performance in terms of accuracy with reduced computational
complexity.
Online Ranking with Top-1 Feedback
Authors: Sougata Chaudhuri, Ambuj Tewari
Date: 2014-10-05 00:00:00

We consider a setting where a system learns to rank a fixed set of $m$ items.
The goal is produce good item rankings for users with diverse interests who
interact online with the system for $T$ rounds. We consider a novel top-$1$
feedback model: at the end of each round, the relevance score for only the top
ranked object is revealed. However, the performance of the system is judged on
the entire ranked list. We provide a comprehensive set of results regarding
learnability under this challenging setting. For PairwiseLoss and DCG, two
popular ranking measures, we prove that the minimax regret is
$\Theta(T^{2/3})$. Moreover, the minimax regret is achievable using an
efficient strategy that only spends $O(m \log m)$ time per round. The same
efficient strategy achieves $O(T^{2/3})$ regret for Precision@$k$.
Surprisingly, we show that for normalized versions of these ranking measures,
i.e., AUC, NDCG \& MAP, no online ranking algorithm can have sublinear regret.
Personalized Advertisement Recommendation: A Ranking Approach to Address the Ubiquitous Click Sparsity Problem
Authors: Sougata Chaudhuri, Georgios Theocharous, Mohammad Ghavamzadeh
Date: 2016-03-06 00:00:00

We study the problem of personalized advertisement recommendation (PAR),
which consist of a user visiting a system (website) and the system displaying
one of $K$ ads to the user. The system uses an internal ad recommendation
policy to map the user's profile (context) to one of the ads. The user either
clicks or ignores the ad and correspondingly, the system updates its
recommendation policy. PAR problem is usually tackled by scalable
\emph{contextual bandit} algorithms, where the policies are generally based on
classifiers. A practical problem in PAR is extreme click sparsity, due to very
few users actually clicking on ads. We systematically study the drawback of
using contextual bandit algorithms based on classifier-based policies, in face
of extreme click sparsity. We then suggest an alternate policy, based on
rankers, learnt by optimizing the Area Under the Curve (AUC) ranking loss,
which can significantly alleviate the problem of click sparsity. We conduct
extensive experiments on public datasets, as well as three industry proprietary
datasets, to illustrate the improvement in click-through-rate (CTR) obtained by
using the ranker-based policy over classifier-based policies.
Generalization error bounds for learning to rank: Does the length of document lists matter?
Authors: Ambuj Tewari, Sougata Chaudhuri
Date: 2016-03-06 00:00:00
Tasks: Learning-To-Rank

We consider the generalization ability of algorithms for learning to rank at
a query level, a problem also called subset ranking. Existing generalization
error bounds necessarily degrade as the size of the document list associated
with a query increases. We show that such a degradation is not intrinsic to the
problem. For several loss functions, including the cross-entropy loss used in
the well known ListNet method, there is \emph{no} degradation in generalization
ability as document lists become longer. We also provide novel generalization
error bounds under $\ell_1$ regularization and faster convergence rates if the
loss function is smooth.
Online Learning to Rank with Feedback at the Top
Authors: Sougata Chaudhuri, Ambuj Tewari
Date: 2016-03-06 00:00:00
Tasks: Learning-To-Rank

We consider an online learning to rank setting in which, at each round, an
oblivious adversary generates a list of $m$ documents, pertaining to a query,
and the learner produces scores to rank the documents. The adversary then
generates a relevance vector and the learner updates its ranker according to
the feedback received. We consider the setting where the feedback is restricted
to be the relevance levels of only the top $k$ documents in the ranked list for
$k \ll m$. However, the performance of learner is judged based on the
unrevealed full relevance vectors, using an appropriate learning to rank loss
function. We develop efficient algorithms for well known losses in the
pointwise, pairwise and listwise families. We also prove that no online
algorithm can have sublinear regret, with top-1 feedback, for any loss that is
calibrated with respect to NDCG. We apply our algorithms on benchmark datasets
demonstrating efficient online learning of a ranking function from highly
restricted feedback.
Proximal groupoid patterns In digital images
Authors: Enoch A-iyeh, James F. Peters
Date: 2016-03-06 00:00:00
Tasks: Classification, Descriptive, General Classification

The focus of this article is on the detection and classification of patterns
based on groupoids. The approach hinges on descriptive proximity of points in a
set based on the neighborliness property. This approach lends support to image
analysis and understanding and in studying nearness of image segments. A
practical application of the approach is in terms of the analysis of natural
images for pattern identification and classification.
Hierarchical Decision Making In Electricity Grid Management
Authors: Gal Dalal, Elad Gilboa, Shie Mannor
Date: 2016-03-06 00:00:00
Tasks: Decision Making, Management, Reinforcement Learning, Reinforcement Learning (RL)

The power grid is a complex and vital system that necessitates careful
reliability management. Managing the grid is a difficult problem with multiple
time scales of decision making and stochastic behavior due to renewable energy
generations, variable demand and unplanned outages. Solving this problem in the
face of uncertainty requires a new methodology with tractable algorithms. In
this work, we introduce a new model for hierarchical decision making in complex
systems. We apply reinforcement learning (RL) methods to learn a proxy, i.e., a
level of abstraction, for real-time power grid reliability. We devise an
algorithm that alternates between slow time-scale policy improvement, and fast
time-scale value function approximation. We compare our results to prevailing
heuristics, and show the strength of our method.
Semi-Automatic Data Annotation, POS Tagging and Mildly Context-Sensitive Disambiguation: the eXtended Revised AraMorph (XRAM)
Authors: Giuliano Lancioni, Valeria Pettinari, Laura Garofalo, Marta Campanelli, Ivana Pepe, Simona Olivieri, Ilaria Cicola
Date: 2016-03-06 00:00:00
Tasks: POS, POS Tagging

An extended, revised form of Tim Buckwalter's Arabic lexical and
morphological resource AraMorph, eXtended Revised AraMorph (henceforth XRAM),
is presented which addresses a number of weaknesses and inconsistencies of the
original model by allowing a wider coverage of real-world Classical and
contemporary (both formal and informal) Arabic texts. Building upon previous
research, XRAM enhancements include (i) flag-selectable usage markers, (ii)
probabilistic mildly context-sensitive POS tagging, filtering, disambiguation
and ranking of alternative morphological analyses, (iii) semi-automatic
increment of lexical coverage through extraction of lexical and morphological
information from existing lexical resources. Testing of XRAM through a
front-end Python module showed a remarkable success level.
A Parallel Corpus of Translationese
Authors: Ella Rabinovich, Shuly Wintner, Ofek Luis Lewinsohn
Date: 2015-09-11 00:00:00
Tasks: Machine Translation, Translation

We describe a set of bilingual English--French and English--German parallel
corpora in which the direction of translation is accurately and reliably
annotated. The corpora are diverse, consisting of parliamentary proceedings,
literary works, transcriptions of TED talks and political commentary. They will
be instrumental for research of translationese and its applications to (human
and machine) translation; specifically, they can be used for the task of
translationese identification, a research direction that enjoys a growing
interest in recent years. To validate the quality and reliability of the
corpora, we replicated previous results of supervised and unsupervised
identification of translationese, and further extended the experiments to
additional datasets and languages.
Fast calculation of correlations in recognition systems
Authors: Pavel Dourbal, Mikhail Pekker
Date: 2016-03-06 00:00:00
Tasks: General Classification

Computationally efficient classification system architecture is proposed. It
utilizes fast tensor-vector multiplication algorithm to apply linear operators
upon input signals . The approach is applicable to wide variety of recognition
system architectures ranging from single stage matched filter bank classifiers
to complex neural networks with unlimited number of hidden layers.
An Argument-based Creative Assistant for Harmonic Blending
Authors: Maximos Kaliakatsos-Papakostas, Roberto Confalonieri, Joseph Corneli, Asterios Zacharakis, Emilios Cambouropoulos
Date: 2016-03-06 00:00:00

Conceptual blending is a powerful tool for computational creativity where,
for example, the properties of two harmonic spaces may be combined in a
consistent manner to produce a novel harmonic space. However, deciding about
the importance of property features in the input spaces and evaluating the
results of conceptual blending is a nontrivial task. In the specific case of
musical harmony, defining the salient features of chord transitions and
evaluating invented harmonic spaces requires deep musicological background
knowledge. In this paper, we propose a creative tool that helps musicologists
to evaluate and to enhance harmonic innovation. This tool allows a music expert
to specify arguments over given transition properties. These arguments are then
considered by the system when defining combinations of features in an
idiom-blending process. A music expert can assess whether the new harmonic
idiom makes musicological sense and re-adjust the arguments (selection of
features) to explore alternative blends that can potentially produce better
harmonic spaces. We conclude with a discussion of future work that would
further automate the harmonisation process.
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
Authors: Alex J. Champandard
Date: 2016-03-05 00:00:00
Tasks: Image Generation, Semantic Segmentation, Style Transfer, Vocal Bursts Valence Prediction

Convolutional neural networks (CNNs) have proven highly effective at image
synthesis and style transfer. For most users, however, using them as tools can
be a challenging task due to their unpredictable behavior that goes against
common intuitions. This paper introduces a novel concept to augment such
generative architectures with semantic annotations, either by manually
authoring pixel labels or using existing solutions for semantic segmentation.
The result is a content-aware generative algorithm that offers meaningful
control over the outcome. Thus, we increase the quality of images generated by
avoiding common glitches, make the results look significantly more plausible,
and extend the functional range of these algorithms---whether for portraits or
landscapes, etc. Applications include semantic style transfer and turning
doodles with few colors into masterful paintings!
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Authors: Ankit Kumar, Ozan .Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher
Date: 2015-06-24 00:00:00
Tasks: General Classification, Part-Of-Speech Tagging, Question Answering, Sentiment Analysis, Text Classification

Most tasks in natural language processing can be cast into question answering
(QA) problems over language input. We introduce the dynamic memory network
(DMN), a neural network architecture which processes input sequences and
questions, forms episodic memories, and generates relevant answers. Questions
trigger an iterative attention process which allows the model to condition its
attention on the inputs and the result of previous iterations. These results
are then reasoned over in a hierarchical recurrent sequence model to generate
answers. The DMN can be trained end-to-end and obtains state-of-the-art results
on several types of tasks and datasets: question answering (Facebook's bAbI
dataset), text classification for sentiment analysis (Stanford Sentiment
Treebank) and sequence modeling for part-of-speech tagging (WSJ-PTB). The
training for these different tasks relies exclusively on trained word vector
representations and input-question-answer triplets.
Feature Selection via Binary Simultaneous Perturbation Stochastic Approximation
Authors: Vural Aksakalli, Milad Malekipirbazari
Date: 2015-08-30 00:00:00
Tasks: feature selection

Feature selection (FS) has become an indispensable task in dealing with
today's highly complex pattern recognition problems with massive number of
features. In this study, we propose a new wrapper approach for FS based on
binary simultaneous perturbation stochastic approximation (BSPSA). This
pseudo-gradient descent stochastic algorithm starts with an initial feature
vector and moves toward the optimal feature vector via successive iterations.
In each iteration, the current feature vector's individual components are
perturbed simultaneously by random offsets from a qualified probability
distribution. We present computational experiments on datasets with numbers of
features ranging from a few dozens to thousands using three widely-used
classifiers as wrappers: nearest neighbor, decision tree, and linear support
vector machine. We compare our methodology against the full set of features as
well as a binary genetic algorithm and sequential FS methods using
cross-validated classification error rate and AUC as the performance criteria.
Our results indicate that features selected by BSPSA compare favorably to
alternative methods in general and BSPSA can yield superior feature sets for
datasets with tens of thousands of features by examining an extremely small
fraction of the solution space. We are not aware of any other wrapper FS
methods that are computationally feasible with good convergence properties for
such large datasets.
Grading of Mammalian Cumulus Oocyte Complexes using Machine Learning for in Vitro Embryo Culture
Authors: Viswanath P Sudarshan, Tobias Weiser, Phalgun Chintala, Subhamoy Mandal, Rahul Dutta
Date: 2016-03-05 00:00:00
Tasks: BIG-bench Machine Learning, Cultural Vocal Bursts Intensity Prediction, General Classification

Visual observation of Cumulus Oocyte Complexes provides only limited
information about its functional competence, whereas the molecular evaluations
methods are cumbersome or costly. Image analysis of mammalian oocytes can
provide attractive alternative to address this challenge. However, it is
complex, given the huge number of oocytes under inspection and the subjective
nature of the features inspected for identification. Supervised machine
learning methods like random forest with annotations from expert biologists can
make the analysis task standardized and reduces inter-subject variability. We
present a semi-automatic framework for predicting the class an oocyte belongs
to, based on multi-object parametric segmentation on the acquired microscopic
image followed by a feature based classification using random forests.
A Linked Data Scalability Challenge: Concept Reuse Leads to Semantic Decay
Authors: Paolo Pareti, Ewan Klein, Adam Barker
Date: 2016-03-05 00:00:00

The increasing amount of available Linked Data resources is laying the
foundations for more advanced Semantic Web applications. One of their main
limitations, however, remains the general low level of data quality. In this
paper we focus on a measure of quality which is negatively affected by the
increase of the available resources. We propose a measure of semantic richness
of Linked Data concepts and we demonstrate our hypothesis that the more a
concept is reused, the less semantically rich it becomes. This is a significant
scalability issue, as one of the core aspects of Linked Data is the propagation
of semantic information on the Web by reusing common terms. We prove our
hypothesis with respect to our measure of semantic richness and we validate our
model empirically. Finally, we suggest possible future directions to address
this scalability problem.
Classifier ensemble creation via false labelling
Authors: Bálint Antal
Date: 2016-03-05 00:00:00

In this paper, a novel approach to classifier ensemble creation is presented.
While other ensemble creation techniques are based on careful selection of
existing classifiers or preprocessing of the data, the presented approach
automatically creates an optimal labelling for a number of classifiers, which
are then assigned to the original data instances and fed to classifiers. The
approach has been evaluated on high-dimensional biomedical datasets. The
results show that the approach outperformed individual approaches in all cases.
A Feature Learning and Object Recognition Framework for Underwater Fish Images
Authors: Meng-Che Chuang, Jenq-Neng Hwang, Kresimir Williams
Date: 2016-03-05 00:00:00
Tasks: Clustering, Object Recognition

Live fish recognition is one of the most crucial elements of fisheries survey
applications where vast amount of data are rapidly acquired. Different from
general scenarios, challenges to underwater image recognition are posted by
poor image quality, uncontrolled objects and environment, as well as difficulty
in acquiring representative samples. Also, most existing feature extraction
techniques are hindered from automation due to involving human supervision.
Toward this end, we propose an underwater fish recognition framework that
consists of a fully unsupervised feature learning technique and an
error-resilient classifier. Object parts are initialized based on saliency and
relaxation labeling to match object parts correctly. A non-rigid part model is
then learned based on fitness, separation and discrimination criteria. For the
classifier, an unsupervised clustering approach generates a binary class
hierarchy, where each node is a classifier. To exploit information from
ambiguous images, the notion of partial classification is introduced to assign
coarse labels by optimizing the "benefit" of indecision made by the classifier.
Experiments show that the proposed framework achieves high accuracy on both
public and self-collected underwater fish images with high uncertainty and
class imbalance.
Underwater Fish Tracking for Moving Cameras based on Deformable Multiple Kernels
Authors: Meng-Che Chuang, Jenq-Neng Hwang, Jian-Hui Ye, Shih-Chia Huang, Kresimir Williams
Date: 2016-03-05 00:00:00

Fishery surveys that call for the use of single or multiple underwater
cameras have been an emerging technology as a non-extractive mean to estimate
the abundance of fish stocks. Tracking live fish in an open aquatic environment
posts challenges that are different from general pedestrian or vehicle tracking
in surveillance applications. In many rough habitats fish are monitored by
cameras installed on moving platforms, where tracking is even more challenging
due to inapplicability of background models. In this paper, a novel tracking
algorithm based on the deformable multiple kernels (DMK) is proposed to address
these challenges. Inspired by the deformable part model (DPM) technique, a set
of kernels is defined to represent the holistic object and several parts that
are arranged in a deformable configuration. Color histogram, texture histogram
and the histogram of oriented gradients (HOG) are extracted and serve as object
features. Kernel motion is efficiently estimated by the mean-shift algorithm on
color and texture features to realize tracking. Furthermore, the HOG-feature
deformation costs are adopted as soft constraints on kernel positions to
maintain the part configuration. Experimental results on practical video set
from underwater moving cameras show the reliable performance of the proposed
method with much less computational cost comparing with state-of-the-art
techniques.
Saliency Detection combining Multi-layer Integration algorithm with background prior and energy function
Authors: Hanling Zhang, Chenxing Xia
Date: 2016-03-05 00:00:00
Tasks: Saliency Detection

In this paper, we propose an improved mechanism for saliency detection.
Firstly,based on a neoteric background prior selecting four corners of an image
as background,we use color and spatial contrast with each superpixel to obtain
a salinecy map(CBP). Inspired by reverse-measurement methods to improve the
accuracy of measurement in Engineering,we employ the Objectness labels as
foreground prior based on part of information of CBP to construct a
map(OFP).Further,an original energy function is applied to optimize both of
them respectively and a single-layer saliency map(SLP)is formed by merging the
above twos.Finally,to deal with the scale problem,we obtain our multi-layer
map(MLP) by presenting an integration algorithm to take advantage of multiple
saliency maps. Quantitative and qualitative experiments on three datasets
demonstrate that our method performs favorably against the state-of-the-art
algorithm.