pwc-archive/papers-with-abstracts huggingface dataset:
The Utility of Abstaining in Binary Classification
Authors: Akshay Balsubramani
Date: 2015-12-26 00:00:00
Tasks: Active Learning, Binary Classification, Classification, General Classification, Medical Diagnosis

We explore the problem of binary classification in machine learning, with a
twist - the classifier is allowed to abstain on any datum, professing ignorance
about the true class label without committing to any prediction. This is
directly motivated by applications like medical diagnosis and fraud risk
assessment, in which incorrect predictions have potentially calamitous
consequences. We focus on a recent spate of theoretically driven work in this
area that characterizes how allowing abstentions can lead to fewer errors in
very general settings. Two areas are highlighted: the surprising possibility of
zero-error learning, and the fundamental tradeoff between predicting
sufficiently often and avoiding incorrect predictions. We review efficient
algorithms with provable guarantees for each of these areas. We also discuss
connections to other scenarios, notably active learning, as they suggest
promising directions of further inquiry in this emerging field.
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
Authors: Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic
Date: 2015-02-09 00:00:00

Complicated generative models often result in a situation where computing the
likelihood of observed data is intractable, while simulating from the
conditional density given a parameter value is relatively easy. Approximate
Bayesian Computation (ABC) is a paradigm that enables simulation-based
posterior inference in such cases by measuring the similarity between simulated
and observed data in terms of a chosen set of summary statistics. However,
there is no general rule to construct sufficient summary statistics for complex
models. Insufficient summary statistics will "leak" information, which leads to
ABC algorithms yielding samples from an incorrect (partial) posterior. In this
paper, we propose a fully nonparametric ABC paradigm which circumvents the need
for manually selecting summary statistics. Our approach, K2-ABC, uses maximum
mean discrepancy (MMD) as a dissimilarity measure between the distributions
over observed and simulated data. MMD is easily estimated as the squared
difference between their empirical kernel embeddings. Experiments on a
simulated scenario and a real-world biological problem illustrate the
effectiveness of the proposed algorithm.
Data Driven Robust Image Guided Depth Map Restoration
Authors: Wei Liu, Yun Gu, Chunhua Shen, Xiaogang Chen, Qiang Wu, Jie Yang
Date: 2015-12-26 00:00:00

Depth maps captured by modern depth cameras such as Kinect and Time-of-Flight
(ToF) are usually contaminated by missing data, noises and suffer from being of
low resolution. In this paper, we present a robust method for high-quality
restoration of a degraded depth map with the guidance of the corresponding
color image. We solve the problem in an energy optimization framework that
consists of a novel robust data term and smoothness term. To accommodate not
only the noise but also the inconsistency between depth discontinuities and the
color edges, we model both the data term and smoothness term with a robust
exponential error norm function. We propose to use Iteratively Re-weighted
Least Squares (IRLS) methods for efficiently solving the resulting highly
non-convex optimization problem. More importantly, we further develop a
data-driven adaptive parameter selection scheme to properly determine the
parameter in the model. We show that the proposed approach can preserve fine
details and sharp depth discontinuities even for a large upsampling factor
($8\times$ for example). Experimental results on both simulated and real
datasets demonstrate that the proposed method outperforms recent
state-of-the-art methods in coping with the heavy noise, preserving sharp depth
discontinuities and suppressing the texture copy artifacts.
Part-Stacked CNN for Fine-Grained Visual Categorization
Authors: Shaoli Huang, Zhe Xu, DaCheng Tao, Ya zhang
Date: 2015-12-26 00:00:00
Proceeding: CVPR 2016 6
Tasks: Classification, Fine-Grained Image Classification, Fine-Grained Visual Categorization, General Classification, Object

In the context of fine-grained visual categorization, the ability to
interpret models as human-understandable visual manuals is sometimes as
important as achieving high classification accuracy. In this paper, we propose
a novel Part-Stacked CNN architecture that explicitly explains the fine-grained
recognition process by modeling subtle differences from object parts. Based on
manually-labeled strong part annotations, the proposed architecture consists of
a fully convolutional network to locate multiple object parts and a two-stream
classification network that en- codes object-level and part-level cues
simultaneously. By adopting a set of sharing strategies between the computation
of multiple object parts, the proposed architecture is very efficient running
at 20 frames/sec during inference. Experimental results on the CUB-200-2011
dataset reveal the effectiveness of the proposed architecture, from both the
perspective of classification accuracy and model interpretability.
The Improvement of Negative Sentences Translation in English-to-Korean Machine Translation
Authors: Chung-Hyok Jang, Kwang-Hyok Kim
Date: 2015-12-26 00:00:00
Tasks: Machine Translation, Sentence, Translation

This paper describes the algorithm for translating English negative sentences
into Korean in English-Korean Machine Translation (EKMT). The proposed
algorithm is based on the comparative study of English and Korean negative
sentences. The earlier translation software cannot translate English negative
sentences into accurate Korean equivalents. We established a new algorithm for
the negative sentence translation and evaluated it.
A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours
Authors: Omar S. Al-Kadi
Date: 2015-12-25 00:00:00
Tasks: Classification, Diagnostic, General Classification, Histopathological Image Classification, image-classification, Image Classification

Tissue texture is known to exhibit a heterogeneous or non-stationary nature,
therefore using a single resolution approach for optimum classification might
not suffice. A clinical decision support system that exploits the subband
textural fractal characteristics for best bases selection of meningioma brain
histopathological image classification is proposed. Each subband is analysed
using its fractal dimension instead of energy, which has the advantage of being
less sensitive to image intensity and abrupt changes in tissue texture. The
most significant subband that best identifies texture discontinuities will be
chosen for further decomposition, and its fractal characteristics would
represent the optimal feature vector for classification. The performance was
tested using the support vector machine (SVM), Bayesian and k-nearest neighbour
(kNN) classifiers and a leave-one-patient-out method was employed for
validation. Our method outperformed the classical energy based selection
approaches, achieving for SVM, Bayesian and kNN classifiers an overall
classification accuracy of 94.12%, 92.50% and 79.70%, as compared to 86.31%,
83.19% and 51.63% for the co-occurrence matrix, and 76.01%, 73.50% and 50.69%
for the energy texture signatures, respectively. These results indicate the
potential usefulness as a decision support system that could complement
radiologists diagnostic capability to discriminate higher order statistical
textural information, for which it would be otherwise difficult via ordinary
human vision.
Texture measures combination for improved meningioma classification of histopathological images
Authors: Omar S. Al-Kadi
Date: 2015-12-25 00:00:00
Tasks: Classification, General Classification

Providing an improved technique which can assist pathologists in correctly
classifying meningioma tumours with a significant accuracy is our main
objective. The proposed technique, which is based on optimum texture measure
combination, inspects the separability of the RGB colour channels and selects
the channel which best segments the cell nuclei of the histopathological
images. The morphological gradient was applied to extract the region of
interest for each subtype and for elimination of possible noise (e.g. cracks)
which might occur during biopsy preparation. Meningioma texture features are
extracted by four different texture measures (two model-based and two
statistical-based) and then corresponding features are fused together in
different combinations after excluding highly correlated features, and a
Bayesian classifier was used for meningioma subtype discrimination. The
combined Gaussian Markov random field and run-length matrix texture measures
outperformed all other combinations in terms of quantitatively characterising
the meningioma tissue, achieving an overall classification accuracy of 92.50%,
improving from 83.75% which is the best accuracy achieved if the texture
measures are used individually.
Using Data Analytics to Detect Anomalous States in Vehicles
Authors: Sandeep Nair Narayanan, Sudip Mittal, Anupam Joshi
Date: 2015-12-25 00:00:00

Vehicles are becoming more and more connected, this opens up a larger attack
surface which not only affects the passengers inside vehicles, but also people
around them. These vulnerabilities exist because modern systems are built on
the comparatively less secure and old CAN bus framework which lacks even basic
authentication. Since a new protocol can only help future vehicles and not
older vehicles, our approach tries to solve the issue as a data analytics
problem and use machine learning techniques to secure cars. We develop a Hidden
Markov Model to detect anomalous states from real data collected from vehicles.
Using this model, while a vehicle is in operation, we are able to detect and
issue alerts. Our model could be integrated as a plug-n-play device in all new
and old cars.
Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images
Authors: Omar Sultan Al-Kadi
Date: 2015-12-25 00:00:00
Tasks: Computed Tomography (CT), Texture Classification

Noise is one of the major problems that hinder an effective texture analysis
of disease in medical images, which may cause variability in the reported
diagnosis. In this paper seven texture measurement methods (two wavelet, two
model and three statistical based) were applied to investigate their
susceptibility to subtle noise caused by acquisition and reconstruction
deficiencies in computed tomography (CT) images. Features of lung tumours were
extracted from two different conventional and contrast enhanced CT image
data-sets under filtered and noisy conditions. When measuring the noise in the
background open-air region of the analysed CT images, noise of Gaussian and
Rayleigh distributions with varying mean and variance was encountered, and
Fisher distance was used to differentiate between an original extracted lung
tumour region of interest (ROI) with the filtered and noisy reconstructed
versions. It was determined that the wavelet packet (WP) and fractal dimension
measures were the least affected, while the Gaussian Markov random field,
run-length and co-occurrence matrices were the most affected by noise.
Depending on the selected ROI size, it was concluded that texture measures with
fewer extracted features can decrease susceptibility to noise, with the WP and
the Gabor filter having a stable performance in both filtered and noisy CT
versions and for both data-sets. Knowing how robust each texture measure under
noise presence is can assist physicians using an automated lung texture
classification system in choosing the appropriate feature extraction algorithm
for a more accurate diagnosis.
Larger-Context Language Modelling
Authors: Tian Wang, Kyunghyun Cho
Date: 2015-11-11 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence

In this work, we propose a novel method to incorporate corpus-level discourse
information into language modelling. We call this larger-context language
model. We introduce a late fusion approach to a recurrent language model based
on long short-term memory units (LSTM), which helps the LSTM unit keep
intra-sentence dependencies and inter-sentence dependencies separate from each
other. Through the evaluation on three corpora (IMDB, BBC, and PennTree Bank),
we demon- strate that the proposed model improves perplexity significantly. In
the experi- ments, we evaluate the proposed approach while varying the number
of context sentences and observe that the proposed late fusion is superior to
the usual way of incorporating additional inputs to the LSTM. By analyzing the
trained larger- context language model, we discover that content words,
including nouns, adjec- tives and verbs, benefit most from an increasing number
of context sentences. This analysis suggests that larger-context language model
improves the unconditional language model by capturing the theme of a document
better and more easily.
Discovering topic structures of a temporally evolving document corpus
Authors: Adham Beykikhoshk, Ognjen Arandjelovic, Dinh Phung, Svetha Venkatesh
Date: 2015-12-25 00:00:00

In this paper we describe a novel framework for the discovery of the topical
content of a data corpus, and the tracking of its complex structural changes
across the temporal dimension. In contrast to previous work our model does not
impose a prior on the rate at which documents are added to the corpus nor does
it adopt the Markovian assumption which overly restricts the type of changes
that the model can capture. Our key technical contribution is a framework based
on (i) discretization of time into epochs, (ii) epoch-wise topic discovery
using a hierarchical Dirichlet process-based model, and (iii) a temporal
similarity graph which allows for the modelling of complex topic changes:
emergence and disappearance, evolution, splitting, and merging. The power of
the proposed framework is demonstrated on two medical literature corpora
concerned with the autism spectrum disorder (ASD) and the metabolic syndrome
(MetS) -- both increasingly important research subjects with significant social
and healthcare consequences. In addition to the collected ASD and metabolic
syndrome literature corpora which we made freely available, our contribution
also includes an extensive empirical analysis of the proposed framework. We
describe a detailed and careful examination of the effects that our
algorithms's free parameters have on its output, and discuss the significance
of the findings both in the context of the practical application of our
algorithm as well as in the context of the existing body of work on temporal
topic analysis. Our quantitative analysis is followed by several qualitative
case studies highly relevant to the current research on ASD and MetS, on which
our algorithm is shown to capture well the actual developments in these fields.
Inducing Generalized Multi-Label Rules with Learning Classifier Systems
Authors: Fani A. Tzima, Miltiadis Allamanis, Alexandros Filotheou, Pericles A. Mitkas
Date: 2015-12-25 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, text-classification, Text Classification

In recent years, multi-label classification has attracted a significant body
of research, motivated by real-life applications, such as text classification
and medical diagnoses. Although sparsely studied in this context, Learning
Classifier Systems are naturally well-suited to multi-label classification
problems, whose search space typically involves multiple highly specific
niches. This is the motivation behind our current work that introduces a
generalized multi-label rule format -- allowing for flexible label-dependency
modeling, with no need for explicit knowledge of which correlations to search
for -- and uses it as a guide for further adapting the general Michigan-style
supervised Learning Classifier System framework. The integration of the
aforementioned rule format and framework adaptations results in a novel
algorithm for multi-label classification whose behavior is studied through a
set of properly defined artificial problems. The proposed algorithm is also
thoroughly evaluated on a set of multi-label datasets and found competitive to
other state-of-the-art multi-label classification methods.
Histogram Meets Topic Model: Density Estimation by Mixture of Histograms
Authors: Hideaki Kim, Hiroshi Sawada
Date: 2015-12-25 00:00:00
Tasks: Density Estimation

The histogram method is a powerful non-parametric approach for estimating the
probability density function of a continuous variable. But the construction of
a histogram, compared to the parametric approaches, demands a large number of
observations to capture the underlying density function. Thus it is not
suitable for analyzing a sparse data set, a collection of units with a small
size of data. In this paper, by employing the probabilistic topic model, we
develop a novel Bayesian approach to alleviating the sparsity problem in the
conventional histogram estimation. Our method estimates a unit's density
function as a mixture of basis histograms, in which the number of bins for each
basis, as well as their heights, is determined automatically. The estimation
procedure is performed by using the fast and easy-to-implement collapsed Gibbs
sampling. We apply the proposed method to synthetic data, showing that it
performs well.
Relation Classification via Recurrent Neural Network
Authors: Dongxu Zhang, Dong Wang
Date: 2015-08-05 00:00:00
Tasks: Classification, Feature Engineering, General Classification, Relation, Relation Classification, Sentence

Deep learning has gained much success in sentence-level relation
classification. For example, convolutional neural networks (CNN) have delivered
competitive performance without much effort on feature engineering as the
conventional pattern-based methods. Thus a lot of works have been produced
based on CNN structures. However, a key issue that has not been well addressed
by the CNN-based method is the lack of capability to learn temporal features,
especially long-distance dependency between nominal pairs. In this paper, we
propose a simple framework based on recurrent neural networks (RNN) and compare
it with CNN-based model. To show the limitation of popular used SemEval-2010
Task 8 dataset, we introduce another dataset refined from MIMLRE(Angeli et al.,
2014). Experiments on two different datasets strongly indicates that the
RNN-based model can deliver better performance on relation classification, and
it is particularly capable of learning long-distance relation patterns. This
makes it suitable for real-world applications where complicated expressions are
often involved.
A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI
Authors: M. R. Avendi, A. Kheradvar, H. Jafarkhani
Date: 2015-12-25 00:00:00
Tasks: LV Segmentation, Segmentation

Segmentation of the left ventricle (LV) from cardiac magnetic resonance
imaging (MRI) datasets is an essential step for calculation of clinical indices
such as ventricular volume and ejection fraction. In this work, we employ deep
learning algorithms combined with deformable models to develop and evaluate a
fully automatic segmentation tool for the LV from short-axis cardiac MRI
datasets. The method employs deep learning algorithms to learn the segmentation
task from the ground true data. Convolutional networks are employed to
automatically detect the LV chamber in MRI dataset. Stacked autoencoders are
utilized to infer the shape of the LV. The inferred shape is incorporated into
deformable models to improve the accuracy and robustness of the segmentation.
We validated our method using 45 cardiac MR datasets taken from the MICCAI 2009
LV segmentation challenge and showed that it outperforms the state-of-the art
methods. Excellent agreement with the ground truth was achieved. Validation
metrics, percentage of good contours, Dice metric, average perpendicular
distance and conformity, were computed as 96.69%, 0.94, 1.81mm and 0.86, versus
those of 79.2%-95.62%, 0.87-0.9, 1.76-2.97mm and 0.67-0.78, obtained by other
methods, respectively.
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields
Authors: Edward Li, Farzad Khalvati, Mohammad Javad Shafiee, Masoom A. Haider, Alexander Wong
Date: 2015-12-25 00:00:00
Tasks: Compressive Sensing, MRI Reconstruction

Magnetic Resonance Imaging (MRI) is a crucial medical imaging technology for
the screening and diagnosis of frequently occurring cancers. However image
quality may suffer by long acquisition times for MRIs due to patient motion, as
well as result in great patient discomfort. Reducing MRI acquisition time can
reduce patient discomfort and as a result reduces motion artifacts from the
acquisition process. Compressive sensing strategies, when applied to MRI, have
been demonstrated to be effective at decreasing acquisition times significantly
by sparsely sampling the \emph{k}-space during the acquisition process.
However, such a strategy requires advanced reconstruction algorithms to produce
high quality and reliable images from compressive sensing MRI. This paper
proposes a new reconstruction approach based on cross-domain stochastically
fully connected conditional random fields (CD-SFCRF) for compressive sensing
MRI. The CD-SFCRF introduces constraints in both \emph{k}-space and spatial
domains within a stochastically fully connected graphical model to produce
improved MRI reconstruction. Experimental results using T2-weighted (T2w)
imaging and diffusion-weighted imaging (DWI) of the prostate show strong
performance in preserving fine details and tissue structures in the
reconstructed images when compared to other tested methods even at low sampling
rates.
Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization
Authors: Yang Song, Jun Zhu
Date: 2015-12-03 00:00:00
Tasks: Bayesian Inference, Collaborative Filtering, Matrix Completion

Bayesian matrix completion has been studied based on a low-rank matrix
factorization formulation with promising results. However, little work has been
done on Bayesian matrix completion based on the more direct spectral
regularization formulation. We fill this gap by presenting a novel Bayesian
matrix completion method based on spectral regularization. In order to
circumvent the difficulties of dealing with the orthonormality constraints of
singular vectors, we derive a new equivalent form with relaxed constraints,
which then leads us to design an adaptive version of spectral regularization
feasible for Bayesian inference. Our Bayesian method requires no parameter
tuning and can infer the number of latent factors automatically. Experiments on
synthetic and real datasets demonstrate encouraging results on rank recovery
and collaborative filtering, with notably good results for very sparse
matrices.
Toward a Research Agenda in Adversarial Reasoning: Computational Approaches to Anticipating the Opponent's Intent and Actions
Authors: Alexander Kott, Michael Ownby
Date: 2015-12-25 00:00:00

This paper defines adversarial reasoning as computational approaches to
inferring and anticipating an enemy's perceptions, intents and actions. It
argues that adversarial reasoning transcends the boundaries of game theory and
must also leverage such disciplines as cognitive modeling, control theory, AI
planning and others. To illustrate the challenges of applying adversarial
reasoning to real-world problems, the paper explores the lessons learned in the
CADET - a battle planning system that focuses on brigade-level ground
operations and involves adversarial reasoning. From this example of current
capabilities, the paper proceeds to describe RAID - a DARPA program that aims
to build capabilities in adversarial reasoning, and how such capabilities would
address practical requirements in Defense and other application areas.