pwc-archive/papers-with-abstracts huggingface dataset:
Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities
Authors: Subhabrata Mukherjee
Date: 2017-07-26 00:00:00
Tasks: Language Modeling, Language Modelling, Recommendation Systems, text-classification, Text Classification

One of the major hurdles preventing the full exploitation of information from
online communities is the widespread concern regarding the quality and
credibility of user-contributed content. Prior works in this domain operate on
a static snapshot of the community, making strong assumptions about the
structure of the data (e.g., relational tables), or consider only shallow
features for text classification.
  To address the above limitations, we propose probabilistic graphical models
that can leverage the joint interplay between multiple factors in online
communities --- like user interactions, community dynamics, and textual content
--- to automatically assess the credibility of user-contributed online content,
and the expertise of users and their evolution with user-interpretable
explanation. To this end, we devise new models based on Conditional Random
Fields for different settings like incorporating partial expert knowledge for
semi-supervised learning, and handling discrete labels as well as numeric
ratings for fine-grained analysis. This enables applications such as extracting
reliable side-effects of drugs from user-contributed posts in healthforums, and
identifying credible content in news communities.
  Online communities are dynamic, as users join and leave, adapt to evolving
trends, and mature over time. To capture this dynamics, we propose generative
models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian
Motion to trace the continuous evolution of user expertise and their language
model over time. This allows us to identify expert users and credible content
jointly over time, improving state-of-the-art recommender systems by explicitly
considering the maturity of users. This also enables applications such as
identifying helpful product reviews, and detecting fake and anomalous reviews
with limited information.
Graph-Based Classification of Omnidirectional Images
Authors: Renata Khasanova, Pascal Frossard
Date: 2017-07-26 00:00:00
Tasks: Classification, General Classification, graph construction, image-classification, Image Classification

Omnidirectional cameras are widely used in such areas as robotics and virtual
reality as they provide a wide field of view. Their images are often processed
with classical methods, which might unfortunately lead to non-optimal solutions
as these methods are designed for planar images that have different geometrical
properties than omnidirectional ones. In this paper we study image
classification task by taking into account the specific geometry of
omnidirectional cameras with graph-based representations. In particular, we
extend deep learning architectures to data on graphs; we propose a principled
way of graph construction such that convolutional filters respond similarly for
the same pattern on different positions of the image regardless of lens
distortions. Our experiments show that the proposed method outperforms current
techniques for the omnidirectional image classification problem.
Empirical Evaluation of Parallel Training Algorithms on Acoustic Modeling
Authors: Wenpeng Li, Bin-Bin Zhang, Lei Xie, Dong Yu
Date: 2017-03-17 00:00:00
Tasks: GPU, speech-recognition, Speech Recognition

Deep learning models (DLMs) are state-of-the-art techniques in speech
recognition. However, training good DLMs can be time consuming especially for
production-size models and corpora. Although several parallel training
algorithms have been proposed to improve training efficiency, there is no clear
guidance on which one to choose for the task in hand due to lack of systematic
and fair comparison among them. In this paper we aim at filling this gap by
comparing four popular parallel training algorithms in speech recognition,
namely asynchronous stochastic gradient descent (ASGD), blockwise model-update
filtering (BMUF), bulk synchronous parallel (BSP) and elastic averaging
stochastic gradient descent (EASGD), on 1000-hour LibriSpeech corpora using
feed-forward deep neural networks (DNNs) and convolutional, long short-term
memory, DNNs (CLDNNs). Based on our experiments, we recommend using BMUF as the
top choice to train acoustic models since it is most stable, scales well with
number of GPUs, can achieve reproducible results, and in many cases even
outperforms single-GPU SGD. ASGD can be used as a substitute in some cases.
Fast calculation of entropy with Zhang's estimator
Authors: Antoni Lozano, Bernardino Casas, Chris Bentz, Ramon Ferrer-i-Cancho
Date: 2017-07-26 00:00:00

Entropy is a fundamental property of a repertoire. Here, we present an
efficient algorithm to estimate the entropy of types with the help of Zhang's
estimator. The algorithm takes advantage of the fact that the number of
different frequencies in a text is in general much smaller than the number of
types. We justify the convenience of the algorithm by means of an analysis of
the statistical properties of texts from more than 1000 languages. Our work
opens up various possibilities for future research.
Fast Deep Matting for Portrait Animation on Mobile Phone
Authors: Bingke Zhu, Yingying Chen, Jinqiao Wang, Si Liu, Bo Zhang, Ming Tang
Date: 2017-07-26 00:00:00
Tasks: Image Matting, Portrait Animation, Video Editing

Image matting plays an important role in image and video editing. However,
the formulation of image matting is inherently ill-posed. Traditional methods
usually employ interaction to deal with the image matting problem with trimaps
and strokes, and cannot run on the mobile phone in real-time. In this paper, we
propose a real-time automatic deep matting approach for mobile devices. By
leveraging the densely connected blocks and the dilated convolution, a light
full convolutional network is designed to predict a coarse binary mask for
portrait images. And a feathering block, which is edge-preserving and matting
adaptive, is further developed to learn the guided filter and transform the
binary mask into alpha matte. Finally, an automatic portrait animation system
based on fast deep matting is built on mobile devices, which does not need any
interaction and can realize real-time matting with 15 fps. The experiments show
that the proposed approach achieves comparable results with the
state-of-the-art matting solvers.
Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data
Authors: Yuning Zhang, Maysam Haghdan, Kevin S. Xu
Date: 2017-07-26 00:00:00
Tasks: Artifact Detection, Small Data Image Classification

One of the main benefits of a wrist-worn computer is its ability to collect a
variety of physiological data in a minimally intrusive manner. Among these
data, electrodermal activity (EDA) is readily collected and provides a window
into a person's emotional and sympathetic responses. EDA data collected using a
wearable wristband are easily influenced by motion artifacts (MAs) that may
significantly distort the data and degrade the quality of analyses performed on
the data if not identified and removed. Prior work has demonstrated that MAs
can be successfully detected using supervised machine learning algorithms on a
small data set collected in a lab setting. In this paper, we demonstrate that
unsupervised learning algorithms perform competitively with supervised
algorithms for detecting MAs on EDA data collected in both a lab-based setting
and a real-world setting comprising about 23 hours of data. We also find,
somewhat surprisingly, that incorporating accelerometer data as well as EDA
improves detection accuracy only slightly for supervised algorithms and
significantly degrades the accuracy of unsupervised algorithms.
Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks
Authors: Nikolaos Passalis, Anastasios Tefas
Date: 2017-07-25 00:00:00
Proceeding: ICCV 2017 10
Tasks: General Classification, Quantization

Convolutional Neural Networks (CNNs) are well established models capable of
achieving state-of-the-art classification accuracy for various computer vision
tasks. However, they are becoming increasingly larger, using millions of
parameters, while they are restricted to handling images of fixed size. In this
paper, a quantization-based approach, inspired from the well-known
Bag-of-Features model, is proposed to overcome these limitations. The proposed
approach, called Convolutional BoF (CBoF), uses RBF neurons to quantize the
information extracted from the convolutional layers and it is able to natively
classify images of various sizes as well as to significantly reduce the number
of parameters in the network. In contrast to other global pooling operators and
CNN compression techniques the proposed method utilizes a trainable pooling
layer that it is end-to-end differentiable, allowing the network to be trained
using regular back-propagation and to achieve greater distribution shift
invariance than competitive methods. The ability of the proposed method to
reduce the parameters of the network and increase the classification accuracy
over other state-of-the-art techniques is demonstrated using three image
datasets.
Dragon: A Computation Graph Virtual Machine Based Deep Learning Framework
Authors: Ting Pan
Date: 2017-07-26 00:00:00
Tasks: Deep Learning

Deep Learning has made a great progress for these years. However, it is still
difficult to master the implement of various models because different
researchers may release their code based on different frameworks or interfaces.
In this paper, we proposed a computation graph based framework which only aims
to introduce well-known interfaces. It will help a lot when reproducing a newly
model or transplanting models that were implemented by other frameworks.
Additionally, we implement numerous recent models covering both Computer Vision
and Nature Language Processing. We demonstrate that our framework will not
suffer from model-starving because it is much easier to make full use of the
works that are already done.
Conditional generation of multi-modal data using constrained embedding space mapping
Authors: Subhajit Chaudhury, Sakyasingha Dasgupta, Asim Munawar, Md. A. Salam Khan, Ryuki Tachibana
Date: 2017-07-04 00:00:00

We present a conditional generative model that maps low-dimensional
embeddings of multiple modalities of data to a common latent space hence
extracting semantic relationships between them. The embedding specific to a
modality is first extracted and subsequently a constrained optimization
procedure is performed to project the two embedding spaces to a common
manifold. The individual embeddings are generated back from this common latent
space. However, in order to enable independent conditional inference for
separately extracting the corresponding embeddings from the common latent space
representation, we deploy a proxy variable trick - wherein, the single shared
latent space is replaced by the respective separate latent spaces of each
modality. We design an objective function, such that, during training we can
force these separate spaces to lie close to each other, by minimizing the
distance between their probability distribution functions. Experimental results
demonstrate that the learned joint model can generalize to learning concepts of
double MNIST digits with additional attributes of colors,from both textual and
speech input.
Associating Grasp Configurations with Hierarchical Features in Convolutional Neural Networks
Authors: Li Yang Ku, Erik Learned-Miller, Rod Grupen
Date: 2016-09-13 00:00:00
Tasks: image-classification, Image Classification

In this work, we provide a solution for posturing the anthropomorphic
Robonaut-2 hand and arm for grasping based on visual information. A mapping
from visual features extracted from a convolutional neural network (CNN) to
grasp points is learned. We demonstrate that a CNN pre-trained for image
classification can be applied to a grasping task based on a small set of
grasping examples. Our approach takes advantage of the hierarchical nature of
the CNN by identifying features that capture the hierarchical support relations
between filters in different CNN layers and locating their 3D positions by
tracing activations backwards in the CNN. When this backward trace terminates
in the RGB-D image, important manipulable structures are thereby localized.
These features that reside in different layers of the CNN are then associated
with controllers that engage different kinematic subchains in the hand/arm
system for grasping. A grasping dataset is collected using demonstrated
hand/object relationships for Robonaut-2 to evaluate the proposed approach in
terms of the precision of the resulting preshape postures. We demonstrate that
this approach outperforms baseline approaches in cluttered scenarios on the
grasping dataset and a point cloud based approach on a grasping task using
Robonaut-2.
SLEEPNET: Automated Sleep Staging System via Deep Learning
Authors: Siddharth Biswal, Joshua Kulas, Haoqi Sun, Balaji Goparaju, M. Brandon Westover, Matt T. Bianchi, Jimeng Sun
Date: 2017-07-26 00:00:00
Tasks: Deep Learning, Diagnostic, EEG, Electroencephalogram (EEG), Sleep Staging

Sleep disorders, such as sleep apnea, parasomnias, and hypersomnia, affect
50-70 million adults in the United States (Hillman et al., 2006). Overnight
polysomnography (PSG), including brain monitoring using electroencephalography
(EEG), is a central component of the diagnostic evaluation for sleep disorders.
While PSG is conventionally performed by trained technologists, the recent rise
of powerful neural network learning algorithms combined with large
physiological datasets offers the possibility of automation, potentially making
expert-level sleep analysis more widely available. We propose SLEEPNET (Sleep
EEG neural network), a deployed annotation tool for sleep staging. SLEEPNET
uses a deep recurrent neural network trained on the largest sleep physiology
database assembled to date, consisting of PSGs from over 10,000 patients from
the Massachusetts General Hospital (MGH) Sleep Laboratory. SLEEPNET achieves
human-level annotation performance on an independent test set of 1,000 EEGs,
with an average accuracy of 85.76% and algorithm-expert inter-rater agreement
(IRA) of kappa = 79.46%, comparable to expert-expert IRA.
Using Thought-Provoking Children's Questions to Drive Artificial Intelligence Research
Authors: Erik T. Mueller, Henry Minsky
Date: 2015-08-27 00:00:00

We propose to use thought-provoking children's questions (TPCQs), namely
Highlights BrainPlay questions, as a new method to drive artificial
intelligence research and to evaluate the capabilities of general-purpose AI
systems. These questions are designed to stimulate thought and learning in
children, and they can be used to do the same thing in AI systems, while
demonstrating the system's reasoning capabilities to the evaluator. We
introduce the TPCQ task, which which takes a TPCQ question as input and
produces as output (1) answers to the question and (2) learned generalizations.
We discuss how BrainPlay questions stimulate learning. We analyze 244 BrainPlay
questions, and we report statistics on question type, question class, answer
cardinality, answer class, types of knowledge needed, and types of reasoning
needed. We find that BrainPlay questions span many aspects of intelligence.
Because the answers to BrainPlay questions and the generalizations learned from
them are often highly open-ended, we suggest using human judges for evaluation.
Navigability with Imperfect Information
Authors: Kaya Deuser, Pavel Naumov
Date: 2017-07-26 00:00:00

The article studies navigability of an autonomous agent in a maze where some
rooms may be indistinguishable. In a previous work the authors have shown that
the properties of navigability in such a setting depend on whether an agent has
perfect recall. Navigability by an agent with perfect recall is a transitive
relation and without is not transitive.
  This article introduces a notion of restricted navigability and shows that a
certain form of transitivity holds for restricted navigability, even for an
agent without perfect recall. The main technical result is a sound and complete
logical system describing the properties of restricted navigability.
Proceedings Sixteenth Conference on Theoretical Aspects of Rationality and Knowledge
Authors: Jérôme Lang
Date: 2017-07-25 00:00:00
Tasks: Distributed Computing, Philosophy

This volume consists of papers presented at the Sixteenth Conference on
Theoretical Aspects of Rationality and Knowledge (TARK) held at the University
of Liverpool, UK, from July 24 to 26, 2017.
  TARK conferences bring together researchers from a wide variety of fields,
including Computer Science (especially, Artificial Intelligence, Cryptography,
Distributed Computing), Economics (especially, Decision Theory, Game Theory,
Social Choice Theory), Linguistics, Philosophy (especially, Philosophical
Logic), and Cognitive Psychology, in order to further understand the issues
involving reasoning about rationality and knowledge.
Proxy Non-Discrimination in Data-Driven Systems
Authors: Anupam Datta, Matt Fredrikson, Gihyuk Ko, Piotr Mardziel, Shayak Sen
Date: 2017-07-25 00:00:00

Machine learnt systems inherit biases against protected classes, historically
disparaged groups, from training data. Usually, these biases are not explicit,
they rely on subtle correlations discovered by training algorithms, and are
therefore difficult to detect. We formalize proxy discrimination in data-driven
systems, a class of properties indicative of bias, as the presence of protected
class correlates that have causal influence on the system's output. We evaluate
an implementation on a corpus of social datasets, demonstrating how to validate
systems against these properties and to repair violations where they occur.
Parameter identification in Markov chain choice models
Authors: Arushi Gupta, Daniel Hsu
Date: 2017-06-02 00:00:00

This work studies the parameter identification problem for the Markov chain
choice model of Blanchet, Gallego, and Goyal used in assortment planning. In
this model, the product selected by a customer is determined by a Markov chain
over the products, where the products in the offered assortment are absorbing
states. The underlying parameters of the model were previously shown to be
identifiable from the choice probabilities for the all-products assortment,
together with choice probabilities for assortments of all-but-one products.
Obtaining and estimating choice probabilities for such large assortments is not
desirable in many settings. The main result of this work is that the parameters
may be identified from assortments of sizes two and three, regardless of the
total number of products. The result is obtained via a simple and efficient
parameter recovery algorithm.
A Short Survey of Biomedical Relation Extraction Techniques
Authors: Elham Shahab
Date: 2017-07-18 00:00:00
Tasks: Relation, Relation Extraction, Survey

Biomedical information is growing rapidly in the recent years and retrieving
useful data through information extraction system is getting more attention. In
the current research, we focus on different aspects of relation extraction
techniques in biomedical domain and briefly describe the state-of-the-art for
relation extraction between a variety of biological elements.
The 2D Tree Sliding Window Discrete Fourier Transform
Authors: Lee F. Richardson, William F. Eddy
Date: 2017-07-25 00:00:00

We present a new algorithm for the 2D Sliding Window Discrete Fourier
Transform (SWDFT). Our algorithm avoids repeating calculations in overlapping
windows by storing them in a tree data-structure based on the ideas of the
Cooley- Tukey Fast Fourier Transform (FFT). For an $N_0 \times N_1$ array and
$n_0 \times n_1$ windows, our algorithm takes $O(N_0 N_1 n_0 n_1)$ operations.
We provide a C implementation of our algorithm for the Radix-2 case, compare
ours with existing algorithms, and show how our algorithm easily extends to
higher dimensions.
Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints
Authors: Ilker Yildirim, Tobias Gerstenberg, Basil Saeed, Marc Toussaint, Josh Tenenbaum
Date: 2017-07-25 00:00:00

In this paper, we present a new task that investigates how people interact
with and make judgments about towers of blocks. In Experiment~1, participants
in the lab solved a series of problems in which they had to re-configure three
blocks from an initial to a final configuration. We recorded whether they used
one hand or two hands to do so. In Experiment~2, we asked participants online
to judge whether they think the person in the lab used one or two hands. The
results revealed a close correspondence between participants' actions in the
lab, and the mental simulations of participants online. To explain
participants' actions and mental simulations, we develop a model that plans
over a symbolic representation of the situation, executes the plan using a
geometric solver, and checks the plan's feasibility by taking into account the
physical constraints of the scene. Our model explains participants' actions and
judgments to a high degree of quantitative accuracy.
Representing Hybrid Automata by Action Language Modulo Theories
Authors: Joohyung Lee, Nikhil Loney, Yunsong Meng
Date: 2017-07-20 00:00:00
Tasks: Translation

Both hybrid automata and action languages are formalisms for describing the
evolution of dynamic systems. This paper establishes a formal relationship
between them. We show how to succinctly represent hybrid automata in an action
language which in turn is defined as a high-level notation for answer set
programming modulo theories (ASPMT) --- an extension of answer set programs to
the first-order level similar to the way satisfiability modulo theories (SMT)
extends propositional satisfiability (SAT). We first show how to represent
linear hybrid automata with convex invariants by an action language modulo
theories. A further translation into SMT allows for computing them using SMT
solvers that support arithmetic over reals. Next, we extend the representation
to the general class of non-linear hybrid automata allowing even non-convex
invariants. We represent them by an action language modulo ODE (Ordinary
Differential Equations), which can be compiled into satisfiability modulo ODE.
We developed a prototype system cplus2aspmt based on these translations, which
allows for a succinct representation of hybrid transition systems that can be
computed effectively by the state-of-the-art SMT solver dReal.
A Unified Joint Matrix Factorization Framework for Data Integration
Authors: Lihua Zhang, Shihua Zhang
Date: 2017-07-25 00:00:00
Tasks: Data Integration

Nonnegative matrix factorization (NMF) is a powerful tool in data exploratory
analysis by discovering the hidden features and part-based patterns from
high-dimensional data. NMF and its variants have been successfully applied into
diverse fields such as pattern recognition, signal processing, data mining,
bioinformatics and so on. Recently, NMF has been extended to analyze multiple
matrices simultaneously. However, a unified framework is still lacking. In this
paper, we introduce a sparse multiple relationship data regularized joint
matrix factorization (JMF) framework and two adapted prediction models for
pattern recognition and data integration. Next, we present four update
algorithms to solve this framework. The merits and demerits of these algorithms
are systematically explored. Furthermore, extensive computational experiments
using both synthetic data and real data demonstrate the effectiveness of JMF
framework and related algorithms on pattern recognition and data mining.