pwc-archive/papers-with-abstracts huggingface dataset:
Improving Search through A3C Reinforcement Learning based Conversational Agent
Authors: Milan Aggarwal, Aarushi Arora, Shagun Sodhani, Balaji Krishnamurthy
Date: 2017-09-17 00:00:00
Proceeding: ICLR 2018 1
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We develop a reinforcement learning based search assistant which can assist
users through a set of actions and sequence of interactions to enable them
realize their intent. Our approach caters to subjective search where the user
is seeking digital assets such as images which is fundamentally different from
the tasks which have objective and limited search modalities. Labeled
conversational data is generally not available in such search tasks and
training the agent through human interactions can be time consuming. We propose
a stochastic virtual user which impersonates a real user and can be used to
sample user behavior efficiently to train the agent which accelerates the
bootstrapping of the agent. We develop A3C algorithm based context preserving
architecture which enables the agent to provide contextual assistance to the
user. We compare the A3C agent with Q-learning and evaluate its performance on
average rewards and state values it obtains with the virtual user in validation
episodes. Our experiments show that the agent learns to achieve higher rewards
and better states.
The Cafe Wall Illusion: Local and Global Perception from multiple scale to multiscale
Authors: Nasim Nematzadeh, David M. W. Powers
Date: 2017-09-17 00:00:00

Geometrical illusions are a subclass of optical illusions in which the
geometrical characteristics of patterns such as orientations and angles are
distorted and misperceived as the result of low- to high-level retinal/cortical
processing. Modelling the detection of tilt in these illusions and their
strengths as they are perceived is a challenging task computationally and leads
to development of techniques that match with human performance. In this study,
we present a predictive and quantitative approach for modeling foveal and
peripheral vision in the induced tilt in Caf\'e Wall illusion in which parallel
mortar lines between shifted rows of black and white tiles appear to converge
and diverge. A bioderived filtering model for the responses of retinal/cortical
simple cells to the stimulus using Difference of Gaussians is utilized with an
analytic processing pipeline introduced in our previous studies to quantify the
angle of tilt in the model. Here we have considered visual characteristics of
foveal and peripheral vision in the perceived tilt in the pattern to predict
different degrees of tilt in different areas of the fovea and periphery as the
eye saccades to different parts of the image. The tilt analysis results from
several sampling sizes and aspect ratios, modelling variant foveal views are
used from our previous investigations on the local tilt, and we specifically
investigate in this work, different configurations of the whole pattern
modelling variant Gestalt views across multiple scales in order to provide
confidence intervals around the predicted tilts. The foveal sample sets are
verified and quantified using two different sampling methods. We present here a
precise and quantified comparison contrasting local tilt detection in the
foveal sets with a global average across all of the Caf\'e Wall configurations
tested in this work.
Anisotropic-Scale Junction Detection and Matching for Indoor Images
Authors: Nan Xue, Gui-Song Xia, Xiang Bai, Liangpei Zhang, Weiming Shen
Date: 2017-03-16 00:00:00
Tasks: Junction Detection

Junctions play an important role in the characterization of local geometric
structures in images, the detection of which is a longstanding and challenging
task. Existing junction detectors usually focus on identifying the junction
locations and the orientations of the junction branches while ignoring their
scales; however, these scales also contain rich geometric information. This
paper presents a novel approach to junction detection and characterization that
exploits the locally anisotropic geometries of a junction and estimates the
scales of these geometries using an \emph{a contrario} model. The output
junctions have anisotropic scales --- i.e., each branch of a junction is
associated with an independent scale parameter --- and are thus termed
anisotropic-scale junctions (ASJs). We then apply the newly detected ASJs for
the matching of indoor images, in which there may be dramatic changes in
viewpoint and the detected local visual features, e.g., key-points, are usually
insufficiently distinctive. We propose to use the anisotropic geometries of our
junctions to improve the matching precision for indoor images. Matching results
obtained on sets of indoor images demonstrate that our approach achieves
state-of-the-art performance in indoor image matching.
Predicting the Gender of Indonesian Names
Authors: Ali Akbar Septiandri
Date: 2017-07-22 00:00:00
Tasks: BIG-bench Machine Learning, Cultural Vocal Bursts Intensity Prediction, regression

We investigated a way to predict the gender of a name using character-level
Long-Short Term Memory (char-LSTM). We compared our method with some
conventional machine learning methods, namely Naive Bayes, logistic regression,
and XGBoost with n-grams as the features. We evaluated the models on a dataset
consisting of the names of Indonesian people. It is not common to use a family
name as the surname in Indonesian culture, except in some ethnicities.
Therefore, we inferred the gender from both full names and first names. The
results show that we can achieve 92.25% accuracy from full names, while using
first names only yields 90.65% accuracy. These results are better than the ones
from applying the classical machine learning algorithms to n-grams.
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Authors: Luming Tang, Yexiang Xue, Di Chen, Carla P. Gomes
Date: 2017-09-17 00:00:00

Multi-Entity Dependence Learning (MEDL) explores conditional correlations
among multiple entities. The availability of rich contextual information
requires a nimble learning scheme that tightly integrates with deep neural
networks and has the ability to capture correlation structures among
exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional
multivariate distribution as a generating process. As a result, the variational
lower bound of the joint likelihood can be optimized via a conditional
variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was
motivated by two real-world applications in computational sustainability: one
studies the spatial correlation among multiple bird species using the eBird
data and the other models multi-dimensional landscape composition and human
footprint in the Amazon rainforest with satellite images. We show that
MEDL_CVAE captures rich dependency structures, scales better than previous
methods, and further improves on the joint likelihood taking advantage of very
large datasets that are beyond the capacity of previous methods.
Deep Ranking Model by Large Adaptive Margin Learning for Person Re-identification
Authors: Jiayun Wang, Sanping Zhou, Jinjun Wang, Qiqi Hou
Date: 2017-07-03 00:00:00
Tasks: Person Re-Identification

Person re-identification aims to match images of the same person across
disjoint camera views, which is a challenging problem in video surveillance.
The major challenge of this task lies in how to preserve the similarity of the
same person against large variations caused by complex backgrounds, mutual
occlusions and different illuminations, while discriminating the different
individuals. In this paper, we present a novel deep ranking model with feature
learning and fusion by learning a large adaptive margin between the intra-class
distance and inter-class distance to solve the person re-identification
problem. Specifically, we organize the training images into a batch of pairwise
samples. Treating these pairwise samples as inputs, we build a novel part-based
deep convolutional neural network (CNN) to learn the layered feature
representations by preserving a large adaptive margin. As a result, the final
learned model can effectively find out the matched target to the anchor image
among a number of candidates in the gallery image set by learning
discriminative and stable feature representations. Overcoming the weaknesses of
conventional fixed-margin loss functions, our adaptive margin loss function is
more appropriate for the dynamic feature space. On four benchmark datasets,
PRID2011, Market1501, CUHK01 and 3DPeS, we extensively conduct comparative
evaluations to demonstrate the advantages of the proposed method over the
state-of-the-art approaches in person re-identification.
Unsupervised, Efficient and Semantic Expertise Retrieval
Authors: Christophe Van Gysel, Maarten de Rijke, Marcel Worring
Date: 2016-08-23 00:00:00
Tasks: Feature Engineering, Retrieval

We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.
Characterization of Hemodynamic Signal by Learning Multi-View Relationships
Authors: Eric Lei, Kyle Miller, Michael R. Pinsky, Artur Dubrawski
Date: 2017-09-17 00:00:00
Tasks: Clustering

Multi-view data are increasingly prevalent in practice. It is often relevant to analyze the relationships between pairs of views by multi-view component analysis techniques such as Canonical Correlation Analysis (CCA). However, data may easily exhibit nonlinear relations, which CCA cannot reveal. We aim to investigate the usefulness of nonlinear multi-view relations to characterize multi-view data in an explainable manner. To address this challenge, we propose a method to characterize globally nonlinear multi-view relationships as a mixture of linear relationships. A clustering method, it identifies partitions of observations that exhibit the same relationships and learns those relationships simultaneously. It defines cluster variables by multi-view rather than spatial relationships, unlike almost all other clustering methods. Furthermore, we introduce a supervised classification method that builds on our clustering method by employing multi-view relationships as discriminative factors. The value of these methods resides in their capability to find useful structure in the data that single-view or current multi-view methods may struggle to find. We demonstrate the potential utility of the proposed approach using an application in clinical informatics to detect and characterize slow bleeding in patients whose central venous pressure (CVP) is monitored at the bedside. Presently, CVP is considered an insensitive measure of a subject's intravascular volume status or its change. However, we reason that features of CVP during inspiration and expiration should be informative in early identification of emerging changes of patient status. We empirically show how the proposed method can help discover and analyze multiple-to-multiple correlations, which could be nonlinear or vary throughout the population, by finding explainable structure of operational interest to practitioners.
Markov Brains: A Technical Introduction
Authors: Arend Hintze, Jeffrey A. Edlund, Randal S. Olson, David B. Knoester, Jory Schossau, Larissa Albantakis, Ali Tehrani-Saleh, Peter Kvam, Leigh Sheneman, Heather Goldsby, Clifford Bohm, Christoph Adami
Date: 2017-09-17 00:00:00

Markov Brains are a class of evolvable artificial neural networks (ANN). They
differ from conventional ANNs in many aspects, but the key difference is that
instead of a layered architecture, with each node performing the same function,
Markov Brains are networks built from individual computational components.
These computational components interact with each other, receive inputs from
sensors, and control motor outputs. The function of the computational
components, their connections to each other, as well as connections to sensors
and motors are all subject to evolutionary optimization. Here we describe in
detail how a Markov Brain works, what techniques can be used to study them, and
how they can be evolved.
Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering
Authors: Wei Li, Yunfang Wu
Date: 2017-09-17 00:00:00
Tasks: Question Answering

In this paper, we focus on the problem of answer triggering ad-dressed by
Yang et al. (2015), which is a critical component for a real-world question
answering system. We employ a hierarchical gated recurrent neural tensor
(HGRNT) model to capture both the context information and the deep
in-teractions between the candidate answers and the question. Our result on F
val-ue achieves 42.6%, which surpasses the baseline by over 10 %.
Building a Neural Machine Translation System Using Only Synthetic Parallel Data
Authors: Jae-hong Park, Jongyoon Song, Sungroh Yoon
Date: 2017-04-02 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

Recent works have shown that synthetic parallel data automatically generated
by translation models can be effective for various neural machine translation
(NMT) issues. In this study, we build NMT systems using only synthetic parallel
data. As an efficient alternative to real parallel data, we also present a new
type of synthetic parallel corpus. The proposed pseudo parallel data are
distinct from previous works in that ground truth and synthetic examples are
mixed on both sides of sentence pairs. Experiments on Czech-German and
French-German translations demonstrate the efficacy of the proposed pseudo
parallel corpus, which shows not only enhanced results for bidirectional
translation tasks but also substantial improvement with the aid of a ground
truth real parallel corpus.
Character Distributions of Classical Chinese Literary Texts: Zipf's Law, Genres, and Epochs
Authors: Chao-Lin Liu, Shuhua Zhang, Yuanli Geng, Huei-ling Lai, Hongsu Wang
Date: 2017-09-17 00:00:00

We collect 14 representative corpora for major periods in Chinese history in
this study. These corpora include poetic works produced in several dynasties,
novels of the Ming and Qing dynasties, and essays and news reports written in
modern Chinese. The time span of these corpora ranges between 1046 BCE and 2007
CE. We analyze their character and word distributions from the viewpoint of the
Zipf's law, and look for factors that affect the deviations and similarities
between their Zipfian curves. Genres and epochs demonstrated their influences
in our analyses. Specifically, the character distributions for poetic works of
between 618 CE and 1644 CE exhibit striking similarity. In addition, although
texts of the same dynasty may tend to use the same set of characters, their
character distributions still deviate from each other.
Universality laws for randomized dimension reduction, with applications
Authors: Samet Oymak, Joel A. Tropp
Date: 2015-11-30 00:00:00
Tasks: compressed sensing, Dimensionality Reduction

Dimension reduction is the process of embedding high-dimensional data into a
lower dimensional space to facilitate its analysis. In the Euclidean setting,
one fundamental technique for dimension reduction is to apply a random linear
map to the data. This dimension reduction procedure succeeds when it preserves
certain geometric features of the set.
  The question is how large the embedding dimension must be to ensure that
randomized dimension reduction succeeds with high probability.
  This paper studies a natural family of randomized dimension reduction maps
and a large class of data sets. It proves that there is a phase transition in
the success probability of the dimension reduction map as the embedding
dimension increases. For a given data set, the location of the phase transition
is the same for all maps in this family. Furthermore, each map has the same
stability properties, as quantified through the restricted minimum singular
value. These results can be viewed as new universality laws in high-dimensional
stochastic geometry.
  Universality laws for randomized dimension reduction have many applications
in applied mathematics, signal processing, and statistics. They yield design
principles for numerical linear algebra algorithms, for compressed sensing
measurement ensembles, and for random linear codes. Furthermore, these results
have implications for the performance of statistical estimation methods under a
large class of random experimental designs.
SKOS Concepts and Natural Language Concepts: an Analysis of Latent Relationships in KOSs
Authors: Anna Mastora, Manolis Peponakis, Sarantos Kapidakis
Date: 2017-09-16 00:00:00
Tasks: General Classification

The vehicle to represent Knowledge Organization Systems (KOSs) in the
environment of the Semantic Web and linked data is the Simple Knowledge
Organization System (SKOS). SKOS provides a way to assign a URI to each
concept, and this URI functions as a surrogate for the concept. This fact makes
of main concern the need to clarify the URIs' ontological meaning. The aim of
this study is to investigate the relation between the ontological substance of
KOS concepts and concepts revealed through the grammatical and syntactic
formalisms of natural language. For this purpose, we examined the dividableness
of concepts in specific KOSs (i.e. a thesaurus, a subject headings system and a
classification scheme) by applying Natural Language Processing (NLP) techniques
(i.e. morphosyntactic analysis) to the lexical representations (i.e. RDF
literals) of SKOS concepts. The results of the comparative analysis reveal
that, despite the use of multi-word units, thesauri tend to represent concepts
in a way that can hardly be further divided conceptually, while Subject
Headings and Classification Schemes - to a certain extent - comprise terms that
can be decomposed into more conceptual constituents. Consequently, SKOS
concepts deriving from thesauri are more likely to represent atomic conceptual
units and thus be more appropriate tools for inference and reasoning. Since
identifiers represent the meaning of a concept, complex concepts are neither
the most appropriate nor the most efficient way of modelling a KOS for the
Semantic Web.
Data Innovation for International Development: An overview of natural language processing for qualitative data analysis
Authors: Philipp Broniecki, Anna Hanchar, Slava J. Mikhaylov
Date: 2017-09-16 00:00:00
Tasks: Decision Making

Availability, collection and access to quantitative data, as well as its
limitations, often make qualitative data the resource upon which development
programs heavily rely. Both traditional interview data and social media
analysis can provide rich contextual information and are essential for
research, appraisal, monitoring and evaluation. These data may be difficult to
process and analyze both systematically and at scale. This, in turn, limits the
ability of timely data driven decision-making which is essential in fast
evolving complex social systems. In this paper, we discuss the potential of
using natural language processing to systematize analysis of qualitative data,
and to inform quick decision-making in the development context. We illustrate
this with interview data generated in a format of micro-narratives for the UNDP
Fragments of Impact project.
Nonnegative HMM for Babble Noise Derived from Speech HMM: Application to Speech Enhancement
Authors: Nasser Mohammadiha, Arne Leijon
Date: 2017-09-16 00:00:00
Tasks: Speech Enhancement

Deriving a good model for multitalker babble noise can facilitate different
speech processing algorithms, e.g. noise reduction, to reduce the so-called
cocktail party difficulty. In the available systems, the fact that the babble
waveform is generated as a sum of N different speech waveforms is not exploited
explicitly. In this paper, first we develop a gamma hidden Markov model for
power spectra of the speech signal, and then formulate it as a sparse
nonnegative matrix factorization (NMF). Second, the sparse NMF is extended by
relaxing the sparsity constraint, and a novel model for babble noise (gamma
nonnegative HMM) is proposed in which the babble basis matrix is the same as
the speech basis matrix, and only the activation factors (weights) of the basis
vectors are different for the two signals over time. Finally, a noise reduction
algorithm is proposed using the derived speech and babble models. All of the
stationary model parameters are estimated using the expectation-maximization
(EM) algorithm, whereas the time-varying parameters, i.e. the gain parameters
of speech and babble signals, are estimated using a recursive EM algorithm. The
objective and subjective listening evaluations show that the proposed babble
model and the final noise reduction algorithm significantly outperform the
conventional methods.
Speech Dereverberation Using Nonnegative Convolutive Transfer Function and Spectro temporal Modeling
Authors: Nasser Mohammadiha, Simon Doclo
Date: 2017-09-16 00:00:00
Tasks: Speech Dereverberation

This paper presents two single channel speech dereverberation methods to
enhance the quality of speech signals that have been recorded in an enclosed
space. For both methods, the room acoustics are modeled using a nonnegative
approximation of the convolutive transfer function (NCTF), and to additionally
exploit the spectral properties of the speech signal, such as the low rank
nature of the speech spectrogram, the speech spectrogram is modeled using
nonnegative matrix factorization (NMF). Two methods are described to combine
the NCTF and NMF models. In the first method, referred to as the integrated
method, a cost function is constructed by directly integrating the speech NMF
model into the NCTF model, while in the second method, referred to as the
weighted method, the NCTF and NMF based cost functions are weighted and summed.
Efficient update rules are derived to solve both optimization problems. In
addition, an extension of the integrated method is presented, which exploits
the temporal dependencies of the speech signal. Several experiments are
performed on reverberant speech signals with and without background noise,
where the integrated method yields a considerably higher speech quality than
the baseline NCTF method and a state of the art spectral enhancement method.
Moreover, the experimental results indicate that the weighted method can even
lead to a better performance in terms of instrumental quality measures, but
that the optimal weighting parameter depends on the room acoustics and the
utilized NMF model. Modeling the temporal dependencies in the integrated method
was found to be useful only for highly reverberant conditions.