pwc-archive/papers-with-abstracts huggingface dataset:
Shamela: A Large-Scale Historical Arabic Corpus
Authors: Yonatan Belinkov, Alexander Magidow, Maxim Romanov, Avi Shmidman, Moshe Koppel
Date: 2016-12-28 00:00:00
Proceeding: WS 2016 12

Arabic is a widely-spoken language with a rich and long history spanning more
than fourteen centuries. Yet existing Arabic corpora largely focus on the
modern period or lack sufficient diachronic information. We develop a
large-scale, historical corpus of Arabic of about 1 billion words from diverse
periods of time. We clean this corpus, process it with a morphological
analyzer, and enhance it by detecting parallel passages and automatically
dating undated texts. We demonstrate its utility with selected case-studies in
which we show its application to the digital humanities.
ggRandomForests: Exploring Random Forest Survival
Authors: John Ehrlinger
Date: 2016-12-28 00:00:00
Tasks: Information Retrieval, Retrieval

Random forest (Leo Breiman 2001a) (RF) is a non-parametric statistical method
requiring no distributional assumptions on covariate relation to the response.
RF is a robust, nonlinear technique that optimizes predictive accuracy by
fitting an ensemble of trees to stabilize model estimates. Random survival
forests (RSF) (Ishwaran and Kogalur 2007; Ishwaran et al. 2008) are an
extension of Breimans RF techniques allowing efficient nonparametric analysis
of time to event data. The randomForestSRC package (Ishwaran and Kogalur 2014)
is a unified treatment of Breimans random forest for survival, regression and
classification problems.
  Predictive accuracy makes RF an attractive alternative to parametric models,
though complexity and interpretability of the forest hinder wider application
of the method. We introduce the ggRandomForests package, tools for visually
understand random forest models grown in R (R Core Team 2014) with the
randomForestSRC package. The ggRandomForests package is structured to extract
intermediate data objects from randomForestSRC objects and generate figures
using the ggplot2 (Wickham 2009) graphics package.
  This document is structured as a tutorial for building random forest for
survival with the randomForestSRC package and using the ggRandomForests package
for investigating how the forest is constructed. We analyse the Primary Biliary
Cirrhosis of the liver data from a clinical trial at the Mayo Clinic (Fleming
and Harrington 1991). Our aim is to demonstrate the strength of using Random
Forest methods for both prediction and information retrieval, specifically in
time to event data settings.
Tighter bounds lead to improved classifiers
Authors: Nicolas Le Roux
Date: 2016-06-29 00:00:00
Tasks: Classification, General Classification

The standard approach to supervised classification involves the minimization
of a log-loss as an upper bound to the classification error. While this is a
tight bound early on in the optimization, it overemphasizes the influence of
incorrectly classified examples far from the decision boundary. Updating the
upper bound during the optimization leads to improved classification rates
while transforming the learning into a sequence of minimization problems. In
addition, in the context where the classifier is part of a larger system, this
modification makes it possible to link the performance of the classifier to
that of the whole system, allowing the seamless introduction of external
constraints.
Efficient iterative policy optimization
Authors: Nicolas Le Roux
Date: 2016-12-28 00:00:00

We tackle the issue of finding a good policy when the number of policy
updates is limited. This is done by approximating the expected policy reward as
a sequence of concave lower bounds which can be efficiently maximized,
drastically reducing the number of policy updates required to achieve good
performance. We also extend existing methods to negative rewards, enabling the
use of control variates.
Partial Membership Latent Dirichlet Allocation
Authors: Chao Chen, Alina Zare, Huy Trinh, Gbeng Omotara, J. Tory Cobb, Timotius Lagaunne
Date: 2016-12-28 00:00:00
Tasks: parameter estimation, Sand, Topic Models

Topic models (e.g., pLSA, LDA, sLDA) have been widely used for segmenting
imagery. However, these models are confined to crisp segmentation, forcing a
visual word (i.e., an image patch) to belong to one and only one topic. Yet,
there are many images in which some regions cannot be assigned a crisp
categorical label (e.g., transition regions between a foggy sky and the ground
or between sand and water at a beach). In these cases, a visual word is best
represented with partial memberships across multiple topics. To address this,
we present a partial membership latent Dirichlet allocation (PM-LDA) model and
an associated parameter estimation algorithm. This model can be useful for
imagery where a visual word may be a mixture of multiple topics. Experimental
results on visual and sonar imagery show that PM-LDA can produce both crisp and
soft semantic image segmentations; a capability previous topic modeling methods
do not have.
Optimal bandwidth estimation for a fast manifold learning algorithm to detect circular structure in high-dimensional data
Authors: Susovan Pal, Praneeth Vepakomma
Date: 2016-12-28 00:00:00

We provide a way to infer about existence of topological circularity in
high-dimensional data sets in $\mathbb{R}^d$ from its projection in
$\mathbb{R}^2$ obtained through a fast manifold learning map as a function of
the high-dimensional dataset $\mathbb{X}$ and a particular choice of a positive
real $\sigma$ known as bandwidth parameter. At the same time we also provide a
way to estimate the optimal bandwidth for fast manifold learning in this
setting through minimization of these functions of bandwidth. We also provide
limit theorems to characterize the behavior of our proposed functions of
bandwidth.
Fast color transfer from multiple images
Authors: Asad Khan, Luo Jiang, Wei Li, Ligang Liu
Date: 2016-12-28 00:00:00

Color transfer between images uses the statistics information of image
effectively. We present a novel approach of local color transfer between images
based on the simple statistics and locally linear embedding. A sketching
interface is proposed for quickly and easily specifying the color
correspondences between target and source image. The user can specify the
correspondences of local region using scribes, which more accurately transfers
the target color to the source image while smoothly preserving the boundaries,
and exhibits more natural output results. Our algorithm is not restricted to
one-to-one image color transfer and can make use of more than one target images
to transfer the color in different regions in the source image. Moreover, our
algorithm does not require to choose the same color style and image size
between source and target images. We propose the sub-sampling to reduce the
computational load. Comparing with other approaches, our algorithm is much
better in color blending in the input data. Our approach preserves the other
color details in the source image. Various experimental results show that our
approach specifies the correspondences of local color region in source and
target images. And it expresses the intention of users and generates more
actual and natural results of visual effect.
Modeling Trolling in Social Media Conversations
Authors: Luis Gerardo Mojica
Date: 2016-12-15 00:00:00

Social media websites, electronic newspapers and Internet forums allow
visitors to leave comments for others to read and interact. This exchange is
not free from participants with malicious intentions, who troll others by
positing messages that are intended to be provocative, offensive, or menacing.
With the goal of facilitating the computational modeling of trolling, we
propose a trolling categorization that is novel in the sense that it allows
comment-based analysis from both the trolls' and the responders' perspectives,
characterizing these two perspectives using four aspects, namely, the troll's
intention and his intention disclosure, as well as the responder's
interpretation of the troll's intention and her response strategy. Using this
categorization, we annotate and release a dataset containing excerpts of Reddit
conversations involving suspected trolls and their interactions with other
users. Finally, we identify the difficult-to-classify cases in our corpus and
suggest potential solutions for them.
Bayesian Optimization with Shape Constraints
Authors: Michael Jauch, Víctor Peña
Date: 2016-12-28 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning

In typical applications of Bayesian optimization, minimal assumptions are
made about the objective function being optimized. This is true even when
researchers have prior information about the shape of the function with respect
to one or more argument. We make the case that shape constraints are often
appropriate in at least two important application areas of Bayesian
optimization: (1) hyperparameter tuning of machine learning algorithms and (2)
decision analysis with utility functions. We describe a methodology for
incorporating a variety of shape constraints within the usual Bayesian
optimization framework and present positive results from simple applications
which suggest that Bayesian optimization with shape constraints is a promising
topic for further research.
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
Authors: Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig, Virginia F. J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Aditya Nori, Antonio Criminisi, Daniel Rueckert, Ben Glocker
Date: 2016-12-28 00:00:00
Tasks: Domain Adaptation, Lesion Segmentation, Segmentation, Unsupervised Domain Adaptation

Significant advances have been made towards building accurate automatic
segmentation systems for a variety of biomedical applications using machine
learning. However, the performance of these systems often degrades when they
are applied on new data that differ from the training data, for example, due to
variations in imaging protocols. Manually annotating new data for each test
domain is not a feasible solution. In this work we investigate unsupervised
domain adaptation using adversarial neural networks to train a segmentation
method which is more invariant to differences in the input data, and which does
not require any annotations on the test domain. Specifically, we learn
domain-invariant features by learning to counter an adversarial network, which
attempts to classify the domain of the input data by observing the activations
of the segmentation network. Furthermore, we propose a multi-connected domain
discriminator for improved adversarial training. Our system is evaluated using
two MR databases of subjects with traumatic brain injuries, acquired using
different scanners and imaging protocols. Using our unsupervised approach, we
obtain segmentation accuracies which are close to the upper bound of supervised
domain adaptation.
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Authors: Jesse H. Krijthe, Marco Loog
Date: 2016-12-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: General Classification

Consider a classification problem where we have both labeled and unlabeled
data available. We show that for linear classifiers defined by convex
margin-based surrogate losses that are decreasing, it is impossible to
construct any semi-supervised approach that is able to guarantee an improvement
over the supervised classifier measured by this surrogate loss on the labeled
and unlabeled data. For convex margin-based loss functions that also increase,
we demonstrate safe improvements are possible.
The formal-logical characterisation of lies, deception, and associated notions
Authors: Toni Heidenreich
Date: 2016-12-28 00:00:00

Defining various dishonest notions in a formal way is a key step to enable
intelligent agents to act in untrustworthy environments. This review evaluates
the literature for this topic by looking at formal definitions based on modal
logic as well as other formal approaches. Criteria from philosophical
groundwork is used to assess the definitions for correctness and completeness.
The key contribution of this review is to show that only a few definitions
fully comply with this gold standard and to point out the missing steps towards
a successful application of these definitions in an actual agent environment.
Accelerated Convolutions for Efficient Multi-Scale Time to Contact Computation in Julia
Authors: Alexander Amini, Berthold Horn, Alan Edelman
Date: 2016-12-28 00:00:00

Convolutions have long been regarded as fundamental to applied mathematics,
physics and engineering. Their mathematical elegance allows for common tasks
such as numerical differentiation to be computed efficiently on large data
sets. Efficient computation of convolutions is critical to artificial
intelligence in real-time applications, like machine vision, where convolutions
must be continuously and efficiently computed on tens to hundreds of kilobytes
per second. In this paper, we explore how convolutions are used in fundamental
machine vision applications. We present an accelerated n-dimensional
convolution package in the high performance computing language, Julia, and
demonstrate its efficacy in solving the time to contact problem for machine
vision. Results are measured against synthetically generated videos and
quantitatively assessed according to their mean squared error from the ground
truth. We achieve over an order of magnitude decrease in compute time and
allocated memory for comparable machine vision applications. All code is
packaged and integrated into the official Julia Package Manager to be used in
various other scenarios.
Multivariate mixture model for myocardium segmentation combining multi-source images
Authors: Xiahai Zhuang
Date: 2016-12-28 00:00:00
Tasks: Myocardium Segmentation, Segmentation, Texture Classification

This paper proposes a method for simultaneous segmentation of multi-source
images, using the multivariate mixture model (MvMM) and maximum of
log-likelihood (LL) framework. The segmentation is a procedure of texture
classification, and the MvMM is used to model the joint intensity distribution
of the images. Specifically, the method is applied to the myocardial
segmentation combining the complementary texture information from
multi-sequence (MS) cardiac magnetic resonance (CMR) images. Furthermore, there
exist inter-image mis-registration and intra-image misalignment of slices in
the MS CMR images. Hence, the MvMM is formulated with transformations, which
are embedded into the LL framework and optimized simultaneously with the
segmentation parameters. The proposed method is able to correct the inter- and
intra-image misalignment by registering each slice of the MS CMR to a virtual
common space, as well as to delineate the indistinguishable boundaries of
myocardium consisting of pathologies. Results have shown statistically
significant improvement in the segmentation performance of the proposed method
with respect to the conventional approaches which can solely segment each image
separately. The proposed method has also demonstrated better robustness in the
incongruent data, where some images may not fully cover the region of interest
and the full coverage can only be reconstructed combining the images from
multiple sources.
Optimization of Test Case Generation using Genetic Algorithm (GA)
Authors: Ahmed Mateen, Marriam Nazir, Salman Afsar Awan
Date: 2016-12-28 00:00:00
Tasks: Evolutionary Algorithms, software testing

Testing provides means pertaining to assuring software performance. The total
aim of software industry is actually to make a certain start associated with
high quality software for the end user. However, associated with software
testing has quite a few underlying concerns, which are very important and need
to pay attention on these issues. These issues are effectively generating,
prioritization of test cases, etc. These issues can be overcome by paying
attention and focus. Solitary of the greatest Problems in the software testing
area is usually how to acquire a great proper set associated with cases to
confirm software. Some other strategies and also methodologies are proposed
pertaining to shipping care of most of these issues. Genetic Algorithm (GA)
belongs to evolutionary algorithms. Evolutionary algorithms have a significant
role in the automatic test generation and many researchers are focusing on it.
In this study explored software testing related issues by using the GA
approach. In addition to right after applying some analysis, better solution
produced, that is feasible and reliable. The particular research presents the
implementation of GAs because of its generation of optimized test cases. Along
these lines, this paper gives proficient system for the optimization of test
case generation using genetic algorithm.
Piecewise convexity of artificial neural networks
Authors: Blaine Rister, Daniel L. Rubin
Date: 2016-07-17 00:00:00
Tasks: global-optimization, speech-recognition, Speech Recognition

Although artificial neural networks have shown great promise in applications
including computer vision and speech recognition, there remains considerable
practical and theoretical difficulty in optimizing their parameters. The
seemingly unreasonable success of gradient descent methods in minimizing these
non-convex functions remains poorly understood. In this work we offer some
theoretical guarantees for networks with piecewise affine activation functions,
which have in recent years become the norm. We prove three main results.
Firstly, that the network is piecewise convex as a function of the input data.
Secondly, that the network, considered as a function of the parameters in a
single layer, all others held constant, is again piecewise convex. Finally,
that the network as a function of all its parameters is piecewise multi-convex,
a generalization of biconvexity. From here we characterize the local minima and
stationary points of the training objective, showing that they minimize certain
subsets of the parameter space. We then analyze the performance of two
optimization algorithms on multi-convex problems: gradient descent, and a
method which repeatedly solves a number of convex sub-problems. We prove
necessary convergence conditions for the first algorithm and both necessary and
sufficient conditions for the second, after introducing regularization to the
objective. Finally, we remark on the remaining difficulty of the global
optimization problem. Under the squared error objective, we show that by
varying the training data, a single rectifier neuron admits local minima
arbitrarily far apart, both in objective value and parameter space.
Symbolic Representation and Classification of Logos
Authors: D. S. Guru, N. Vinay Kumar
Date: 2016-12-28 00:00:00
Tasks: Classification, Clustering, General Classification

In this paper, a model for classification of logos based on symbolic
representation of features is presented. The proposed model makes use of global
features of logo images such as color, texture, and shape features for
classification. The logo images are broadly classified into three different
classes, viz., logo image containing only text, an image with only symbol, and
an image with both text and a symbol. In each class, the similar looking logo
images are clustered using K-means clustering algorithm. The intra-cluster
variations present in each cluster corresponding to each class are then
preserved using symbolic interval data. Thus referenced logo images are
represented in the form of interval data. A sample logo image is then
classified using suitable symbolic classifier. For experimentation purpose,
relatively large amount of color logo images is created consisting of 5044 logo
images. The classification results are validated with the help of accuracy,
precision, recall, F-measure, and time. To check the efficacy of the proposed
model, the comparative analyses are given against the other models. The results
show that the proposed model outperforms the other models with respect to time
and F-measure.
Provable learning of Noisy-or Networks
Authors: Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski
Date: 2016-12-28 00:00:00
Tasks: Tensor Decomposition, Topic Models

Many machine learning applications use latent variable models to explain
structure in data, whereby visible variables (= coordinates of the given
datapoint) are explained as a probabilistic function of some hidden variables.
Finding parameters with the maximum likelihood is NP-hard even in very simple
settings. In recent years, provably efficient algorithms were nevertheless
developed for models with linear structures: topic models, mixture models,
hidden markov models, etc. These algorithms use matrix or tensor decomposition,
and make some reasonable assumptions about the parameters of the underlying
model.
  But matrix or tensor decomposition seems of little use when the latent
variable model has nonlinearities. The current paper shows how to make
progress: tensor decomposition is applied for learning the single-layer {\em
noisy or} network, which is a textbook example of a Bayes net, and used for
example in the classic QMR-DT software for diagnosing which disease(s) a
patient may have by observing the symptoms he/she exhibits.
  The technical novelty here, which should be useful in other settings in
future, is analysis of tensor decomposition in presence of systematic error
(i.e., where the noise/error is correlated with the signal, and doesn't
decrease as number of samples goes to infinity). This requires rethinking all
steps of tensor decomposition methods from the ground up.
  For simplicity our analysis is stated assuming that the network parameters
were chosen from a probability distribution but the method seems more generally
applicable.
Automatic Composition and Optimization of Multicomponent Predictive Systems With an Extended Auto-WEKA
Authors: Manuel Martin Salvador, Marcin Budka, Bogdan Gabrys
Date: 2016-12-28 00:00:00

Composition and parameterization of multicomponent predictive systems (MCPSs)
consisting of chains of data transformation steps are a challenging task.
Auto-WEKA is a tool to automate the combined algorithm selection and
hyperparameter (CASH) optimization problem. In this paper, we extend the CASH
problem and Auto-WEKA to support the MCPS, including preprocessing steps for
both classification and regression tasks. We define the optimization problem in
which the search space consists of suitably parameterized Petri nets forming
the sought MCPS solutions. In the experimental analysis, we focus on examining
the impact of considerably extending the search space (from approximately
22,000 to 812 billion possible combinations of methods and categorical
hyperparameters). In a range of extensive experiments, three different
optimization strategies are used to automatically compose MCPSs for 21 publicly
available data sets. The diversity of the composed MCPSs found is an indication
that fully and automatically exploiting different combinations of data cleaning
and preprocessing techniques is possible and highly beneficial for different
predictive models. We also present the results on seven data sets from real
chemical production processes. Our findings can have a major impact on the
development of high-quality predictive models as well as their maintenance and
scalability aspects needed in modern applications and deployment scenarios.