pwc-archive/papers-with-abstracts huggingface dataset:
CRDT: Correlation Ratio Based Decision Tree Model for Healthcare Data Mining
Authors: Smita Roy, Samrat Mondal, Asif Ekbal
Date: 2015-09-24 00:00:00
Tasks: Attribute

The phenomenal growth in the healthcare data has inspired us in investigating
robust and scalable models for data mining. For classification problems
Information Gain(IG) based Decision Tree is one of the popular choices.
However, depending upon the nature of the dataset, IG based Decision Tree may
not always perform well as it prefers the attribute with more number of
distinct values as the splitting attribute. Healthcare datasets generally have
many attributes and each attribute generally has many distinct values. In this
paper, we have tried to focus on this characteristics of the datasets while
analysing the performance of our proposed approach which is a variant of
Decision Tree model and uses the concept of Correlation Ratio(CR). Unlike IG
based approach, this CR based approach has no biasness towards the attribute
with more number of distinct values. We have applied our model on some
benchmark healthcare datasets to show the effectiveness of the proposed
technique.
Sparsity-based Correction of Exponential Artifacts
Authors: Yin Ding, Ivan W. Selesnick
Date: 2015-09-24 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

This paper describes an exponential transient excision algorithm (ETEA). In
biomedical time series analysis, e.g., in vivo neural recording and
electrocorticography (ECoG), some measurement artifacts take the form of
piecewise exponential transients. The proposed method is formulated as an
unconstrained convex optimization problem, regularized by smoothed l1-norm
penalty function, which can be solved by majorization-minimization (MM) method.
With a slight modification of the regularizer, ETEA can also suppress more
irregular piecewise smooth artifacts, especially, ocular artifacts (OA) in
electroencephalog- raphy (EEG) data. Examples of synthetic signal, EEG data,
and ECoG data are presented to illustrate the proposed algorithms.
Automatic Concept Discovery from Parallel Text and Visual Corpora
Authors: Chen Sun, Chuang Gan, Ram Nevatia
Date: 2015-09-24 00:00:00
Proceeding: ICCV 2015 12
Tasks: Retrieval, Sentence, Sentence Retrieval

Humans connect language and vision to perceive the world. How to build a
similar connection for computers? One possible way is via visual concepts,
which are text terms that relate to visually discriminative entities. We
propose an automatic visual concept discovery algorithm using parallel text and
visual corpora; it filters text terms based on the visual discriminative power
of the associated images, and groups them into concepts using visual and
semantic similarities. We illustrate the applications of the discovered
concepts using bidirectional image and sentence retrieval task and image
tagging task, and show that the discovered concepts not only outperform several
large sets of manually selected concepts significantly, but also achieves the
state-of-the-art performance in the retrieval task.
Noise-Robust ASR for the third 'CHiME' Challenge Exploiting Time-Frequency Masking based Multi-Channel Speech Enhancement and Recurrent Neural Network
Authors: Zaihu Pang, Fengyun Zhu
Date: 2015-09-24 00:00:00
Tasks: Language Modeling, Language Modelling, Speech Enhancement, speech-recognition, Speech Recognition, Speech Separation

In this paper, the Lingban entry to the third 'CHiME' speech separation and
recognition challenge is presented. A time-frequency masking based speech
enhancement front-end is proposed to suppress the environmental noise utilizing
multi-channel coherence and spatial cues. The state-of-the-art speech
recognition techniques, namely recurrent neural network based acoustic and
language modeling, state space minimum Bayes risk based discriminative acoustic
modeling, and i-vector based acoustic condition modeling, are carefully
integrated into the speech recognition back-end. To further improve the system
performance by fully exploiting the advantages of different technologies, the
final recognition results are obtained by lattice combination and rescoring.
Evaluations carried out on the official dataset prove the effectiveness of the
proposed systems. Comparing with the best baseline result, the proposed system
obtains consistent improvements with over 57% relative word error rate
reduction on the real-data test set.
IllinoisSL: A JAVA Library for Structured Prediction
Authors: Kai-Wei Chang, Shyam Upadhyay, Ming-Wei Chang, Vivek Srikumar, Dan Roth
Date: 2015-09-23 00:00:00
Tasks: Prediction, Structured Prediction

IllinoisSL is a Java library for learning structured prediction models. It
supports structured Support Vector Machines and structured Perceptron. The
library consists of a core learning module and several applications, which can
be executed from command-lines. Documentation is provided to guide users. In
Comparison to other structured learning libraries, IllinoisSL is efficient,
general, and easy to use.
Object detection via a multi-region & semantic segmentation-aware CNN model
Authors: Spyros Gidaris, Nikos Komodakis
Date: 2015-05-07 00:00:00
Tasks: Object, object-detection, Object Detection, Object Localization, regression, Semantic Segmentation

We propose an object detection system that relies on a multi-region deep
convolutional neural network (CNN) that also encodes semantic
segmentation-aware features. The resulting CNN-based representation aims at
capturing a diverse set of discriminative appearance factors and exhibits
localization sensitivity that is essential for accurate object localization. We
exploit the above properties of our recognition module by integrating it on an
iterative localization mechanism that alternates between scoring a box proposal
and refining its location with a deep CNN regression model. Thanks to the
efficient use of our modules, we detect objects with very high localization
accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we
achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published
work by a significant margin.
A Nonparametric Bayesian Approach Toward Stacked Convolutional Independent Component Analysis
Authors: Sotirios P. Chatzis
Date: 2014-11-17 00:00:00
Proceeding: ICCV 2015 12
Tasks: Action Recognition, Temporal Action Localization, Variational Inference

Unsupervised feature learning algorithms based on convolutional formulations
of independent components analysis (ICA) have been demonstrated to yield
state-of-the-art results in several action recognition benchmarks. However,
existing approaches do not allow for the number of latent components (features)
to be automatically inferred from the data in an unsupervised manner. This is a
significant disadvantage of the state-of-the-art, as it results in considerable
burden imposed on researchers and practitioners, who must resort to tedious
cross-validation procedures to obtain the optimal number of latent features. To
resolve these issues, in this paper we introduce a convolutional nonparametric
Bayesian sparse ICA architecture for overcomplete feature learning from
high-dimensional data. Our method utilizes an Indian buffet process prior to
facilitate inference of the appropriate number of latent features under a
hybrid variational inference algorithm, scalable to massive datasets. As we
show, our model can be naturally used to obtain deep unsupervised hierarchical
feature extractors, by greedily stacking successive model layers, similar to
existing approaches. In addition, inference for this model is completely
heuristics-free; thus, it obviates the need of tedious parameter tuning, which
is a major challenge most deep learning approaches are faced with. We evaluate
our method on several action recognition benchmarks, and exhibit its advantages
over the state-of-the-art.
A review of learning vector quantization classifiers
Authors: David Nova, Pablo A. Estevez
Date: 2015-09-23 00:00:00
Tasks: Quantization

In this work we present a review of the state of the art of Learning Vector
Quantization (LVQ) classifiers. A taxonomy is proposed which integrates the
most relevant LVQ approaches to date. The main concepts associated with modern
LVQ approaches are defined. A comparison is made among eleven LVQ classifiers
using one real-world and two artificial datasets.
Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Authors: Zhe Gan, Chunyuan Li, Ricardo Henao, David Carlson, Lawrence Carin
Date: 2015-09-23 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Time Series, Time Series Analysis

Deep dynamic generative models are developed to learn sequential dependencies
in time-series data. The multi-layered model is designed by constructing a
hierarchy of temporal sigmoid belief networks (TSBNs), defined as a sequential
stack of sigmoid belief networks (SBNs). Each SBN has a contextual hidden
state, inherited from the previous SBNs in the sequence, and is used to
regulate its hidden bias. Scalable learning and inference algorithms are
derived by introducing a recognition model that yields fast sampling from the
variational posterior. This recognition model is trained jointly with the
generative model, by maximizing its variational lower bound on the
log-likelihood. Experimental results on bouncing balls, polyphonic music,
motion capture, and text streams show that the proposed approach achieves
state-of-the-art predictive performance, and has the capacity to synthesize
various sequences.
Characterization of graphs for protein structure modeling and recognition of solubility
Authors: Lorenzo Livi, Alessandro Giuliani, Alireza Sadeghian
Date: 2014-07-30 00:00:00
Tasks: One-class classifier

This paper deals with the relations among structural, topological, and
chemical properties of the E.Coli proteome from the vantage point of the
solubility/aggregation propensity of proteins. Each E.Coli protein is initially
represented according to its known folded 3D shape. This step consists in
representing the available E.Coli proteins in terms of graphs. We first analyze
those graphs by considering pure topological characterizations, i.e., by
analyzing the mass fractal dimension and the distribution underlying both
shortest paths and vertex degrees. Results confirm the general architectural
principles of proteins. Successively, we focus on the statistical properties of
a representation of such graphs in terms of vectors composed of several
numerical features, which we extracted from their structural representation. We
found that protein size is the main discriminator for the solubility, while
however there are other factors that help explaining the solubility degree. We
finally analyze such data through a novel one-class classifier, with the aim of
discriminating among very and poorly soluble proteins. Results are encouraging
and consolidate the potential of pattern recognition techniques when employed
to describe complex biological systems.
Fractionally-Supervised Classification
Authors: Irene Vrbik, Paul D. McNicholas
Date: 2013-07-13 00:00:00
Tasks: Classification, Clustering, General Classification

Traditionally, there are three species of classification: unsupervised,
supervised, and semi-supervised. Supervised and semi-supervised classification
differ by whether or not weight is given to unlabelled observations in the
classification procedure. In unsupervised classification, or clustering, all
observations are unlabeled and hence full weight is given to unlabelled
observations. When some observations are unlabelled, it can be very difficult
to \textit{a~priori} choose the optimal level of supervision, and the
consequences of a sub-optimal choice can be non-trivial. A flexible
fractionally-supervised approach to classification is introduced, where any
level of supervision --- ranging from unsupervised to supervised --- can be
attained. Our approach uses a weighted likelihood, wherein weights control the
relative role that labelled and unlabelled data have in building a classifier.
A comparison between our approach and the traditional species is presented
using simulated and real data. Gaussian mixture models are used as a vehicle to
illustrate our fractionally-supervised classification approach; however, it is
broadly applicable and variations on the postulated model can be easily made.
Well Tops Guided Prediction of Reservoir Properties using Modular Neural Network Concept A Case Study from Western Onshore, India
Authors: Soumi Chaki, Akhilesh K Verma, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
Date: 2015-09-23 00:00:00
Tasks: Sand

This paper proposes a complete framework consisting pre-processing, modeling,
and post-processing stages to carry out well tops guided prediction of a
reservoir property (sand fraction) from three seismic attributes (seismic
impedance, instantaneous amplitude, and instantaneous frequency) using the
concept of modular artificial neural network (MANN). The data set used in this
study comprising three seismic attributes and well log data from eight wells,
is acquired from a western onshore hydrocarbon field of India. Firstly, the
acquired data set is integrated and normalized. Then, well log analysis and
segmentation of the total depth range into three different units (zones)
separated by well tops are carried out. Secondly, three different networks are
trained corresponding to three different zones using combined data set of seven
wells and then trained networks are validated using the remaining test well.
The target property of the test well is predicted using three different tuned
networks corresponding to three zones; and then the estimated values obtained
from three different networks are concatenated to represent the predicted log
along the complete depth range of the testing well. The application of multiple
simpler networks instead of a single one improves the prediction accuracy in
terms of performance metrics such as correlation coefficient, root mean square
error, absolute error mean and program execution time.
3D Scan Registration using Curvelet Features in Planetary Environments
Authors: Siddhant Ahuja, Peter Iles, Steven L. Waslander
Date: 2015-09-23 00:00:00

Topographic mapping in planetary environments relies on accurate 3D scan
registration methods. However, most global registration algorithms relying on
features such as FPFH and Harris-3D show poor alignment accuracy in these
settings due to the poor structure of the Mars-like terrain and variable
resolution, occluded, sparse range data that is hard to register without some
a-priori knowledge of the environment. In this paper, we propose an alternative
approach to 3D scan registration using the curvelet transform that performs
multi-resolution geometric analysis to obtain a set of coefficients indexed by
scale (coarsest to finest), angle and spatial position. Features are detected
in the curvelet domain to take advantage of the directional selectivity of the
transform. A descriptor is computed for each feature by calculating the 3D
spatial histogram of the image gradients, and nearest neighbor based matching
is used to calculate the feature correspondences. Correspondence rejection
using Random Sample Consensus identifies inliers, and a locally optimal
Singular Value Decomposition-based estimation of the rigid-body transformation
aligns the laser scans given the re-projected correspondences in the metric
space. Experimental results on a publicly available data-set of planetary
analogue indoor facility, as well as simulated and real-world scans from Neptec
Design Group's IVIGMS 3D laser rangefinder at the outdoor CSA Mars yard
demonstrates improved performance over existing methods in the challenging
sparse Mars-like terrain.
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
Authors: Akhilesh K Verma, Soumi Chaki, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
Date: 2015-09-23 00:00:00
Tasks: Sand

In this paper, we illustrate the modeling of a reservoir property (sand
fraction) from seismic attributes namely seismic impedance, seismic amplitude,
and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset
includes 3D post-stacked seismic attributes and six well logs acquired from a
hydrocarbon field located in the western coast of India. Presence of thin sand
and shale layers in the basin area makes the modeling of reservoir
characteristic a challenging task. Though seismic data is helpful in
extrapolation of reservoir properties away from boreholes; yet, it could be
challenging to delineate thin sand and shale reservoirs using seismic data due
to its limited resolvability. Therefore, it is important to develop
state-of-art intelligent methods for calibrating a nonlinear mapping between
seismic data and target reservoir variables. Neural networks have shown its
potential to model such nonlinear mappings; however, uncertainties associated
with the model and datasets are still a concern. Hence, introduction of Fuzzy
Logic (FL) is beneficial for handling these uncertainties. More specifically,
hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF
methods, are capable for the modeling reservoir characteristics by integrating
the explicit knowledge representation power of FL with the learning ability of
neural networks. The documented results in this study demonstrate acceptable
resemblance between target and predicted variables, and hence, encourage the
application of integrated machine learning approaches such as Neuro-Fuzzy in
reservoir characterization domain. Furthermore, visualization of the variation
of sand probability in the study area would assist in identifying placement of
potential wells for future drilling operations.
A Novel Pre-processing Scheme to Improve the Prediction of Sand Fraction from Seismic Attributes using Neural Networks
Authors: Soumi Chaki, Aurobinda Routray, William K. Mohanty
Date: 2015-09-23 00:00:00
Tasks: BIG-bench Machine Learning, Sand

This paper presents a novel pre-processing scheme to improve the prediction
of sand fraction from multiple seismic attributes such as seismic impedance,
amplitude and frequency using machine learning and information filtering. The
available well logs along with the 3-D seismic data have been used to benchmark
the proposed pre-processing stage using a methodology which primarily consists
of three steps: pre-processing, training and post-processing. An Artificial
Neural Network (ANN) with conjugate-gradient learning algorithm has been used
to model the sand fraction. The available sand fraction data from the high
resolution well logs has far more information content than the low resolution
seismic attributes. Therefore, regularization schemes based on Fourier
Transform (FT), Wavelet Decomposition (WD) and Empirical Mode Decomposition
(EMD) have been proposed to shape the high resolution sand fraction data for
effective machine learning. The input data sets have been segregated into
training, testing and validation sets. The test results are primarily used to
check different network structures and activation function performances. Once
the network passes the testing phase with an acceptable performance in terms of
the selected evaluators, the validation phase follows. In the validation stage,
the prediction model is tested against unseen data. The network yielding
satisfactory performance in the validation stage is used to predict
lithological properties from seismic attributes throughout a given volume.
Finally, a post-processing scheme using 3-D spatial filtering is implemented
for smoothing the sand fraction in the volume. Prediction of lithological
properties using this framework is helpful for Reservoir Characterization.
Boolean Hedonic Games
Authors: Haris Aziz, Paul Harrenstein, Jérôme Lang, Michael Wooldridge
Date: 2015-09-23 00:00:00
Tasks: Relation

We study hedonic games with dichotomous preferences. Hedonic games are
cooperative games in which players desire to form coalitions, but only care
about the makeup of the coalitions of which they are members; they are
indifferent about the makeup of other coalitions. The assumption of dichotomous
preferences means that, additionally, each player's preference relation
partitions the set of coalitions of which that player is a member into just two
equivalence classes: satisfactory and unsatisfactory. A player is indifferent
between satisfactory coalitions, and is indifferent between unsatisfactory
coalitions, but strictly prefers any satisfactory coalition over any
unsatisfactory coalition. We develop a succinct representation for such games,
in which each player's preference relation is represented by a propositional
formula. We show how solution concepts for hedonic games with dichotomous
preferences are characterised by propositional formulas.
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Authors: Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer
Date: 2015-06-09 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Constituency Parsing, Image Captioning, Prediction, Speech Recognition, Translation

Recurrent Neural Networks can be trained to produce sequences of tokens given
some input, as exemplified by recent results in machine translation and image
captioning. The current approach to training them consists of maximizing the
likelihood of each token in the sequence given the current (recurrent) state
and the previous token. At inference, the unknown previous token is then
replaced by a token generated by the model itself. This discrepancy between
training and inference can yield errors that can accumulate quickly along the
generated sequence. We propose a curriculum learning strategy to gently change
the training process from a fully guided scheme using the true previous token,
towards a less guided scheme which mostly uses the generated token instead.
Experiments on several sequence prediction tasks show that this approach yields
significant improvements. Moreover, it was used successfully in our winning
entry to the MSCOCO image captioning challenge, 2015.
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives
Authors: Giulia Pasquale, Tanis Mar, Carlo Ciliberto, Lorenzo Rosasco, Lorenzo Natale
Date: 2015-09-23 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

The importance of depth perception in the interactions that humans have
within their nearby space is a well established fact. Consequently, it is also
well known that the possibility of exploiting good stereo information would
ease and, in many cases, enable, a large variety of attentional and interactive
behaviors on humanoid robotic platforms. However, the difficulty of computing
real-time and robust binocular disparity maps from moving stereo cameras often
prevents from relying on this kind of cue to visually guide robots' attention
and actions in real-world scenarios. The contribution of this paper is
two-fold: first, we show that the Efficient Large-scale Stereo Matching
algorithm (ELAS) by A. Geiger et al. 2010 for computation of the disparity map
is well suited to be used on a humanoid robotic platform as the iCub robot;
second, we show how, provided with a fast and reliable stereo system,
implementing relatively challenging visual behaviors in natural settings can
require much less effort. As a case of study we consider the common situation
where the robot is asked to focus the attention on one object close in the
scene, showing how a simple but effective disparity-based segmentation solves
the problem in this case. Indeed this example paves the way to a variety of
other similar applications.