pwc-archive/papers-with-abstracts huggingface dataset:
Neural Networks and Chaos: Construction, Evaluation of Chaotic Networks, and Prediction of Chaos with Multilayer Feedforward Networks
Authors: Jacques M. Bahi, Jean-François Couchot, Christophe Guyeux, Michel Salomon
Date: 2016-08-21 00:00:00

Many research works deal with chaotic neural networks for various fields of
application. Unfortunately, up to now these networks are usually claimed to be
chaotic without any mathematical proof. The purpose of this paper is to
establish, based on a rigorous theoretical framework, an equivalence between
chaotic iterations according to Devaney and a particular class of neural
networks. On the one hand we show how to build such a network, on the other
hand we provide a method to check if a neural network is a chaotic one.
Finally, the ability of classical feedforward multilayer perceptrons to learn
sets of data obtained from a dynamical system is regarded. Various Boolean
functions are iterated on finite states. Iterations of some of them are proven
to be chaotic as it is defined by Devaney. In that context, important
differences occur in the training process, establishing with various neural
networks that chaotic behaviors are far more difficult to learn.
Linear Readout of Object Manifolds
Authors: SueYeon Chung, Daniel D. Lee, Haim Sompolinsky
Date: 2015-12-06 00:00:00
Tasks: Object, Sensitivity

Objects are represented in sensory systems by continuous manifolds due to
sensitivity of neuronal responses to changes in physical features such as
location, orientation, and intensity. What makes certain sensory
representations better suited for invariant decoding of objects by downstream
networks? We present a theory that characterizes the ability of a linear
readout network, the perceptron, to classify objects from variable neural
responses. We show how the readout perceptron capacity depends on the
dimensionality, size, and shape of the object manifolds in its input neural
representation.
VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation
Authors: Hao Chen, Qi Dou, Lequan Yu, Pheng-Ann Heng
Date: 2016-08-21 00:00:00
Tasks: Brain Segmentation, Image Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

Recently deep residual learning with residual units for training very deep
neural networks advanced the state-of-the-art performance on 2D image
recognition tasks, e.g., object detection and segmentation. However, how to
fully leverage contextual representations for recognition tasks from volumetric
data has not been well studied, especially in the field of medical image
computing, where a majority of image modalities are in volumetric format. In
this paper we explore the deep residual learning on the task of volumetric
brain segmentation. There are at least two main contributions in our work.
First, we propose a deep voxelwise residual network, referred as VoxResNet,
which borrows the spirit of deep residual learning in 2D image recognition
tasks, and is extended into a 3D variant for handling volumetric data. Second,
an auto-context version of VoxResNet is proposed by seamlessly integrating the
low-level image appearance features, implicit shape information and high-level
context together for further improving the volumetric segmentation performance.
Extensive experiments on the challenging benchmark of brain segmentation from
magnetic resonance (MR) images corroborated the efficacy of our proposed method
in dealing with volumetric data. We believe this work unravels the potential of
3D deep learning to advance the recognition performance on volumetric image
segmentation.
Online Feature Selection with Group Structure Analysis
Authors: Jing Wang, Meng Wang, Pei-Pei Li, Luoqi Liu, Zhong-Qiu Zhao, Xuegang Hu, Xindong Wu
Date: 2016-08-21 00:00:00
Tasks: Face Verification, feature selection, image-classification, Image Classification

Online selection of dynamic features has attracted intensive interest in
recent years. However, existing online feature selection methods evaluate
features individually and ignore the underlying structure of feature stream.
For instance, in image analysis, features are generated in groups which
represent color, texture and other visual information. Simply breaking the
group structure in feature selection may degrade performance. Motivated by this
fact, we formulate the problem as an online group feature selection. The
problem assumes that features are generated individually but there are group
structure in the feature stream. To the best of our knowledge, this is the
first time that the correlation among feature stream has been considered in the
online feature selection process. To solve this problem, we develop a novel
online group feature selection method named OGFS. Our proposed approach
consists of two stages: online intra-group selection and online inter-group
selection. In the intra-group selection, we design a criterion based on
spectral analysis to select discriminative features in each group. In the
inter-group selection, we utilize a linear regression model to select an
optimal subset. This two-stage procedure continues until there are no more
features arriving or some predefined stopping conditions are met. %Our method
has been applied Finally, we apply our method to multiple tasks including image
classification %, face verification and face verification. Extensive empirical
studies performed on real-world and benchmark data sets demonstrate that our
method outperforms other state-of-the-art online feature selection %method
methods.
A Hybrid, PDE-ODE Control Strategy for Intercepting an Intelligent, well-informed Target in a Stationary, Cluttered Environment
Authors: Ahmad A. Masoud
Date: 2016-08-20 00:00:00

In [1,2] a new class of intelligent controllers that can semantically embed
an agent in a spatial context constraining its behavior in a goal-oriented
manner was suggested. A controller of such a class can guide an agent in a
stationary unknown environment to a fixed target zone along an obstacle-free
trajectory. Here, an extension is suggested that would enable the interception
of an intelligent target that is maneuvering to evade capture amidst stationary
clutter (i.e. the target zone is moving). This is achieved by forcing the
differential properties of the potential field used to induce the control
action to satisfy the wave equation. Background of the problem, theoretical
developments, as well as, proofs of the ability of the modified control to
intercept the target along an obstacle-free trajectory are supplied. Simulation
results are also provided.
Visual Processing by a Unified Schatten-$p$ Norm and $\ell_q$ Norm Regularized Principal Component Pursuit
Authors: Jing Wang, Meng Wang, Xuegang Hu, Shuicheng Yan
Date: 2016-08-20 00:00:00

In this paper, we propose a non-convex formulation to recover the authentic
structure from the corrupted real data. Typically, the specific structure is
assumed to be low rank, which holds for a wide range of data, such as images
and videos. Meanwhile, the corruption is assumed to be sparse. In the
literature, such a problem is known as Robust Principal Component Analysis
(RPCA), which usually recovers the low rank structure by approximating the rank
function with a nuclear norm and penalizing the error by an $\ell_1$-norm.
Although RPCA is a convex formulation and can be solved effectively, the
introduced norms are not tight approximations, which may cause the solution to
deviate from the authentic one. Therefore, we consider here a non-convex
relaxation, consisting of a Schatten-$p$ norm and an $\ell_q$-norm that promote
low rank and sparsity respectively. We derive a proximal iteratively reweighted
algorithm (PIRA) to solve the problem. Our algorithm is based on an alternating
direction method of multipliers, where in each iteration we linearize the
underlying objective function that allows us to have a closed form solution. We
demonstrate that solutions produced by the linearized approximation always
converge and have a tighter approximation than the convex counterpart.
Experimental results on benchmarks show encouraging results of our approach.
Learning Word Embeddings from Intrinsic and Extrinsic Views
Authors: Jifan Chen, Kan Chen, Xipeng Qiu, Qi Zhang, Xuanjing Huang, Zheng Zhang
Date: 2016-08-20 00:00:00
Tasks: Descriptive, Document Classification, General Classification, Learning Word Embeddings, Link Prediction, Word Embeddings, Word Similarity

While word embeddings are currently predominant for natural language
processing, most of existing models learn them solely from their contexts.
However, these context-based word embeddings are limited since not all words'
meaning can be learned based on only context. Moreover, it is also difficult to
learn the representation of the rare words due to data sparsity problem. In
this work, we address these issues by learning the representations of words by
integrating their intrinsic (descriptive) and extrinsic (contextual)
information. To prove the effectiveness of our model, we evaluate it on four
tasks, including word similarity, reverse dictionaries,Wiki link prediction,
and document classification. Experiment results show that our model is powerful
in both word and document modeling.
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness
Authors: Jason J. Yu, Adam W. Harley, Konstantinos G. Derpanis
Date: 2016-08-20 00:00:00
Tasks: Optical Flow Estimation

Recently, convolutional networks (convnets) have proven useful for predicting
optical flow. Much of this success is predicated on the availability of large
datasets that require expensive and involved data acquisition and laborious la-
beling. To bypass these challenges, we propose an unsuper- vised approach
(i.e., without leveraging groundtruth flow) to train a convnet end-to-end for
predicting optical flow be- tween two images. We use a loss function that
combines a data term that measures photometric constancy over time with a
spatial term that models the expected variation of flow across the image.
Together these losses form a proxy measure for losses based on the groundtruth
flow. Empiri- cally, we show that a strong convnet baseline trained with the
proposed unsupervised approach outperforms the same network trained with
supervision on the KITTI dataset.
Analysis of Bayesian Classification based Approaches for Android Malware Detection
Authors: Suleiman Y. Yerima, Sakir Sezer, Gavin McWilliams
Date: 2016-08-20 00:00:00
Tasks: Android Malware Detection, Classification, General Classification, Malware Detection

Mobile malware has been growing in scale and complexity spurred by the
unabated uptake of smartphones worldwide. Android is fast becoming the most
popular mobile platform resulting in sharp increase in malware targeting the
platform. Additionally, Android malware is evolving rapidly to evade detection
by traditional signature-based scanning. Despite current detection measures in
place, timely discovery of new malware is still a critical issue. This calls
for novel approaches to mitigate the growing threat of zero-day Android
malware. Hence, in this paper we develop and analyze proactive Machine Learning
approaches based on Bayesian classification aimed at uncovering unknown Android
malware via static analysis. The study, which is based on a large malware
sample set of majority of the existing families, demonstrates detection
capabilities with high accuracy. Empirical results and comparative analysis are
presented offering useful insight towards development of effective
static-analytic Bayesian classification based solutions for detecting unknown
Android malware.
Solving General Arithmetic Word Problems
Authors: Subhro Roy, Dan Roth
Date: 2016-08-04 00:00:00
Proceeding: EMNLP 2015 9
Tasks: General Classification, World Knowledge

This paper presents a novel approach to automatically solving arithmetic word
problems. This is the first algorithmic approach that can handle arithmetic
problems with multiple steps and operations, without depending on additional
annotations or predefined templates. We develop a theory for expression trees
that can be used to represent and evaluate the target arithmetic expressions;
we use it to uniquely decompose the target arithmetic problem to multiple
classification problems; we then compose an expression tree, combining these
with world knowledge through a constrained inference framework. Our classifiers
gain from the use of {\em quantity schemas} that supports better extraction of
features. Experimental results show that our method outperforms existing
systems, achieving state of the art performance on benchmark datasets of
arithmetic word problems.
Private Causal Inference
Authors: Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger
Date: 2015-12-17 00:00:00
Tasks: Causal Inference

Causal inference deals with identifying which random variables "cause" or
control other random variables. Recent advances on the topic of causal
inference based on tools from statistical estimation and machine learning have
resulted in practical algorithms for causal inference. Causal inference has the
potential to have significant impact on medical research, prevention and
control of diseases, and identifying factors that impact economic changes to
name just a few. However, these promising applications for causal inference are
often ones that involve sensitive or personal data of users that need to be
kept private (e.g., medical records, personal finances, etc). Therefore, there
is a need for the development of causal inference methods that preserve data
privacy. We study the problem of inferring causality using the current, popular
causal inference framework, the additive noise model (ANM) while simultaneously
ensuring privacy of the users. Our framework provides differential privacy
guarantees for a variety of ANM variants. We run extensive experiments, and
demonstrate that our techniques are practical and easy to implement.
Reweighting with Boosted Decision Trees
Authors: A. Rogozhnikov
Date: 2016-08-20 00:00:00
Tasks: General Classification

Machine learning tools are commonly used in modern high energy physics (HEP)
experiments. Different models, such as boosted decision trees (BDT) and
artificial neural networks (ANN), are widely used in analyses and even in the
software triggers.
  In most cases, these are classification models used to select the "signal"
events from data. Monte Carlo simulated events typically take part in training
of these models. While the results of the simulation are expected to be close
to real data, in practical cases there is notable disagreement between
simulated and observed data. In order to use available simulation in training,
corrections must be introduced to generated data. One common approach is
reweighting - assigning weights to the simulated events. We present a novel
method of event reweighting based on boosted decision trees. The problem of
checking the quality of reweighting step in analyses is also discussed.
Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function
Authors: Ru-Ze Liang, Lihui Shi, Haoxiang Wang, Jiandong Meng, Jim Jing-Yan Wang, Qingquan Sun, Yi Gu
Date: 2016-04-22 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

In this paper we study the problem of content-based image retrieval. In this
problem, the most popular performance measure is the top precision measure, and
the most important component of a retrieval system is the similarity function
used to compare a query image against a database image. However, up to now,
there is no existing similarity learning method proposed to optimize the top
precision measure. To fill this gap, in this paper, we propose a novel
similarity learning method to maximize the top precision measure. We model this
problem as a minimization problem with an objective function as the combination
of the losses of the relevant images ranked behind the top-ranked irrelevant
image, and the squared Frobenius norm of the similarity function parameter.
This minimization problem is solved as a quadratic programming problem. The
experiments over two benchmark data sets show the advantages of the proposed
method over other similarity learning methods when the top precision is used as
the performance measure.
3DUNDERWORLD-SLS: An Open-Source Structured-Light Scanning System for Rapid Geometry Acquisition
Authors: Qing Gu, Kyriakos Herakleous, Charalambos Poullis
Date: 2014-06-25 00:00:00
Tasks: CPU, GPU

Recently, there has been an increase in the demand of virtual 3D objects
representing real-life objects. A plethora of methods and systems have already
been proposed for the acquisition of the geometry of real-life objects ranging
from those which employ active sensor technology, passive sensor technology or
a combination of various techniques.
  In this paper we present the development of a 3D scanning system which is
based on the principle of structured-light, without having particular
requirements for specialized equipment. We discuss the intrinsic details and
inherent difficulties of structured-light scanning techniques and present our
solutions. Finally, we introduce our open-source scanning software system
"3DUNDERWORLD-SLS" which implements the proposed techniques both in CPU and
GPU. We have performed extensive testing with a wide range of models and report
the results. Furthermore, we present a comprehensive evaluation of the system
and a comparison with a high-end commercial 3D scanner.
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
Authors: Yunjin Chen, Thomas Pock
Date: 2015-08-12 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, Image Super-Resolution, Super-Resolution

Image restoration is a long-standing problem in low-level computer vision
with many interesting applications. We describe a flexible learning framework
based on the concept of nonlinear reaction diffusion models for various image
restoration problems. By embodying recent improvements in nonlinear diffusion
models, we propose a dynamic nonlinear reaction diffusion model with
time-dependent parameters (\ie, linear filters and influence functions). In
contrast to previous nonlinear diffusion models, all the parameters, including
the filters and the influence functions, are simultaneously learned from
training data through a loss based approach. We call this approach TNRD --
\textit{Trainable Nonlinear Reaction Diffusion}. The TNRD approach is
applicable for a variety of image restoration tasks by incorporating
appropriate reaction force. We demonstrate its capabilities with three
representative applications, Gaussian image denoising, single image super
resolution and JPEG deblocking. Experiments show that our trained nonlinear
diffusion models largely benefit from the training of the parameters and
finally lead to the best reported performance on common test datasets for the
tested applications. Our trained models preserve the structural simplicity of
diffusion models and take only a small number of diffusion steps, thus are
highly efficient. Moreover, they are also well-suited for parallel computation
on GPUs, which makes the inference procedure extremely fast.
Model Interpolation with Trans-dimensional Random Field Language Models for Speech Recognition
Authors: Bin Wang, Zhijian Ou, Yong He, Akinori Kawamura
Date: 2016-03-30 00:00:00
Tasks: Sentence, speech-recognition, Speech Recognition

The dominant language models (LMs) such as n-gram and neural network (NN)
models represent sentence probabilities in terms of conditionals. In contrast,
a new trans-dimensional random field (TRF) LM has been recently introduced to
show superior performances, where the whole sentence is modeled as a random
field. In this paper, we examine how the TRF models can be interpolated with
the NN models, and obtain 12.1\% and 17.9\% relative error rate reductions over
6-gram LMs for English and Chinese speech recognition respectively through
log-linear combination.
Topic Sensitive Neural Headline Generation
Authors: Lei Xu, ZiYun Wang, Ayana, Zhiyuan Liu, Maosong Sun
Date: 2016-08-20 00:00:00
Tasks: Headline Generation, Text Summarization

Neural models have recently been used in text summarization including
headline generation. The model can be trained using a set of document-headline
pairs. However, the model does not explicitly consider topical similarities and
differences of documents. We suggest to categorizing documents into various
topics so that documents within the same topic are similar in content and share
similar summarization patterns. Taking advantage of topic information of
documents, we propose topic sensitive neural headline generation model. Our
model can generate more accurate summaries guided by document topics. We test
our model on LCSTS dataset, and experiments show that our method outperforms
other baselines on each topic and achieves the state-of-art performance.
Inference in Probabilistic Logic Programs using Lifted Explanations
Authors: Arun Nampally, C. R. Ramakrishnan
Date: 2016-08-20 00:00:00

In this paper, we consider the problem of lifted inference in the context of
Prism-like probabilistic logic programming languages. Traditional inference in
such languages involves the construction of an explanation graph for the query
and computing probabilities over this graph. When evaluating queries over
probabilistic logic programs with a large number of instances of random
variables, traditional methods treat each instance separately. For many
programs and queries, we observe that explanations can be summarized into
substantially more compact structures, which we call lifted explanation graphs.
In this paper, we define lifted explanation graphs and operations over them. In
contrast to existing lifted inference techniques, our method for constructing
lifted explanations naturally generalizes existing methods for constructing
explanation graphs. To compute probability of query answers, we solve
recurrences generated from the lifted graphs. We show examples where the use of
our technique reduces the asymptotic complexity of inference.
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions
Authors: Qingyang Li, Tao Yang, Liang Zhan, Derrek Paul Hibar, Neda Jahanshad, Yalin Wang, Jieping Ye, Paul M. Thompson, Jie Wang
Date: 2016-08-19 00:00:00
Tasks: Model Selection

Genome-wide association studies (GWAS) offer new opportunities to identify
genetic risk factors for Alzheimer's disease (AD). Recently, collaborative
efforts across different institutions emerged that enhance the power of many
existing techniques on individual institution data. However, a major barrier to
collaborative studies of GWAS is that many institutions need to preserve
individual data privacy. To address this challenge, we propose a novel
distributed framework, termed Local Query Model (LQM) to detect risk SNPs for
AD across multiple research institutions. To accelerate the learning process,
we propose a Distributed Enhanced Dual Polytope Projection (D-EDPP) screening
rule to identify irrelevant features and remove them from the optimization. To
the best of our knowledge, this is the first successful run of the
computationally intensive model selection procedure to learn a consistent model
across different institutions without compromising their privacy while ranking
the SNPs that may collectively affect AD. Empirical studies are conducted on
809 subjects with 5.9 million SNP features which are distributed across three
individual institutions. D-EDPP achieved a 66-fold speed-up by effectively
identifying irrelevant features.