pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Generative Modeling Using Matrix Product States
Authors: Zhao-Yu Han, Jun Wang, Heng Fan, Lei Wang, Pan Zhang
Date: 2017-09-06 00:00:00
Tasks: BIG-bench Machine Learning

Generative modeling, which learns joint probability distribution from data
and generates samples according to it, is an important task in machine learning
and artificial intelligence. Inspired by probabilistic interpretation of
quantum physics, we propose a generative model using matrix product states,
which is a tensor network originally proposed for describing (particularly
one-dimensional) entangled quantum states. Our model enjoys efficient learning
analogous to the density matrix renormalization group method, which allows
dynamically adjusting dimensions of the tensors and offers an efficient direct
sampling approach for generative tasks. We apply our method to generative
modeling of several standard datasets including the Bars and Stripes, random
binary patterns and the MNIST handwritten digits to illustrate the abilities,
features and drawbacks of our model over popular generative models such as
Hopfield model, Boltzmann machines and generative adversarial networks. Our
work sheds light on many interesting directions of future exploration on the
development of quantum-inspired algorithms for unsupervised machine learning,
which are promisingly possible to be realized on quantum devices.
Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence
Authors: Dylan Campbell, Lars Petersson, Laurent Kneip, Hongdong Li
Date: 2017-09-27 00:00:00
Proceeding: ICCV 2017 10
Tasks: Camera Pose Estimation, Pose Estimation

Estimating the 6-DoF pose of a camera from a single image relative to a
pre-computed 3D point-set is an important task for many computer vision
applications. Perspective-n-Point (PnP) solvers are routinely used for camera
pose estimation, provided that a good quality set of 2D-3D feature
correspondences are known beforehand. However, finding optimal correspondences
between 2D key-points and a 3D point-set is non-trivial, especially when only
geometric (position) information is known. Existing approaches to the
simultaneous pose and correspondence problem use local optimisation, and are
therefore unlikely to find the optimal solution without a good pose
initialisation, or introduce restrictive assumptions. Since a large proportion
of outliers are common for this problem, we instead propose a globally-optimal
inlier set cardinality maximisation approach which jointly estimates optimal
camera pose and optimal correspondences. Our approach employs branch-and-bound
to search the 6D space of camera poses, guaranteeing global optimality without
requiring a pose prior. The geometry of SE(3) is used to find novel upper and
lower bounds for the number of inliers and local optimisation is integrated to
accelerate convergence. The evaluation empirically supports the optimality
proof and shows that the method performs much more robustly than existing
approaches, including on a large-scale outdoor data-set.
A Bimodal Network Approach to Model Topic Dynamics
Authors: Luigi Di Caro, Marco Guerzoni, Massimiliano Nuccio, Giovanni Siragusa
Date: 2017-09-27 00:00:00
Tasks: model

This paper presents an intertemporal bimodal network to analyze the evolution
of the semantic content of a scientific field within the framework of topic
modeling, namely using the Latent Dirichlet Allocation (LDA). The main
contribution is the conceptualization of the topic dynamics and its
formalization and codification into an algorithm. To benchmark the
effectiveness of this approach, we propose three indexes which track the
transformation of topics over time, their rate of birth and death, and the
novelty of their content. Applying the LDA, we test the algorithm both on a
controlled experiment and on a corpus of several thousands of scientific papers
over a period of more than 100 years which account for the history of the
economic thought.
Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification
Authors: Faxian Cao, Zhijing Yang, Jinchang Ren, Wing-Kuen Ling
Date: 2017-09-08 00:00:00
Tasks: Attribute, Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification, regression

Although the sparse multinomial logistic regression (SMLR) has provided a
useful tool for sparse classification, it suffers from inefficacy in dealing
with high dimensional features and manually set initial regressor values. This
has significantly constrained its applications for hyperspectral image (HSI)
classification. In order to tackle these two drawbacks, an extreme sparse
multinomial logistic regression (ESMLR) is proposed for effective
classification of HSI. First, the HSI dataset is projected to a new feature
space with randomly generated weight and bias. Second, an optimization model is
established by the Lagrange multiplier method and the dual principle to
automatically determine a good initial regressor for SMLR via minimizing the
training error and the regressor value. Furthermore, the extended
multi-attribute profiles (EMAPs) are utilized for extracting both the spectral
and spatial features. A combinational linear multiple features learning (MFL)
method is proposed to further enhance the features extracted by ESMLR and
EMAPs. Finally, the logistic regression via the variable splitting and the
augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing
the computational time. Experiments are conducted on two well-known HSI
datasets, namely the Indian Pines dataset and the Pavia University dataset,
which have shown the fast and robust performance of the proposed ESMLR
framework.
Research on several key technologies in practical speech emotion recognition
Authors: Chengwei Huang
Date: 2017-09-27 00:00:00
Tasks: Clustering, Emotion Recognition, Speech Emotion Recognition

In this dissertation the practical speech emotion recognition technology is
studied, including several cognitive related emotion types, namely fidgetiness,
confidence and tiredness. The high quality of naturalistic emotional speech
data is the basis of this research. The following techniques are used for
inducing practical emotional speech: cognitive task, computer game, noise
stimulation, sleep deprivation and movie clips.
  A practical speech emotion recognition system is studied based on Gaussian
mixture model. A two-class classifier set is adopted for performance
improvement under the small sample case. Considering the context information in
continuous emotional speech, a Gaussian mixture model embedded with Markov
networks is proposed.
  A further study is carried out for system robustness analysis. First, noise
reduction algorithm based on auditory masking properties is fist introduced to
the practical speech emotion recognition. Second, to deal with the complicated
unknown emotion types under real situation, an emotion recognition method with
rejection ability is proposed, which enhanced the system compatibility against
unknown emotion samples. Third, coping with the difficulties brought by a large
number of unknown speakers, an emotional feature normalization method based on
speaker-sensitive feature clustering is proposed. Fourth, by adding the
electrocardiogram channel, a bi-modal emotion recognition system based on
speech signals and electrocardiogram signals is first introduced.
  The speech emotion recognition methods studied in this dissertation may be
extended into the cross-language speech emotion recognition and the whispered
speech emotion recognition.
Generative Adversarial Networks with Inverse Transformation Unit
Authors: Zhifeng Kong, Shuo Ding
Date: 2017-09-27 00:00:00

In this paper we introduce a new structure to Generative Adversarial Networks
by adding an inverse transformation unit behind the generator. We present two
theorems to claim the convergence of the model, and two conjectures to nonideal
situations when the transformation is not bijection. A general survey on models
with different transformations was done on the MNIST dataset and the
Fashion-MNIST dataset, which shows the transformation does not necessarily need
to be bijection. Also, with certain transformations that blurs an image, our
model successfully learned to sharpen the images and recover blurred images,
which was additionally verified by our measurement of sharpness.
Signature Verification Approach using Fusion of Hybrid Texture Features
Authors: Ankan Kumar Bhunia, Alireza Alaei, Partha Pratim Roy
Date: 2017-09-27 00:00:00

In this paper, a writer-dependent signature verification method is proposed. Two different types of texture features, namely Wavelet and Local Quantized Patterns (LQP) features, are employed to extract two kinds of transform and statistical based information from signature images. For each writer two separate one-class support vector machines (SVMs) corresponding to each set of LQP and Wavelet features are trained to obtain two different authenticity scores for a given signature. Finally, a score level classifier fusion method is used to integrate the scores obtained from the two one-class SVMs to achieve the verification score. In the proposed method only genuine signatures are used to train the one-class SVMs. The proposed signature verification method has been tested using four different publicly available datasets and the results demonstrate the generality of the proposed method. The proposed system outperforms other existing systems in the literature.
Augmented Robust PCA For Foreground-Background Separation on Noisy, Moving Camera Video
Authors: Chen Gao, Brian E. Moore, Raj Rao Nadakuditi
Date: 2017-09-27 00:00:00
Tasks: Denoising

This work presents a novel approach for robust PCA with total variation
regularization for foreground-background separation and denoising on noisy,
moving camera video. Our proposed algorithm registers the raw (possibly
corrupted) frames of a video and then jointly processes the registered frames
to produce a decomposition of the scene into a low-rank background component
that captures the static components of the scene, a smooth foreground component
that captures the dynamic components of the scene, and a sparse component that
can isolate corruptions and other non-idealities. Unlike existing methods, our
proposed algorithm produces a panoramic low-rank component that spans the
entire field of view, automatically stitching together corrupted data from
partially overlapping scenes. The low-rank portion of our robust PCA model is
based on a recently discovered optimal low-rank matrix estimator (OptShrink)
that requires no parameter tuning. We demonstrate the performance of our
algorithm on both static and moving camera videos corrupted by noise and
outliers.
A Simple Reinforcement Learning Mechanism for Resource Allocation in LTE-A Networks with Markov Decision Process and Q-Learning
Authors: Einar Cesar Santos
Date: 2017-09-27 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Resource allocation is still a difficult issue to deal with in wireless
networks. The unstable channel condition and traffic demand for Quality of
Service (QoS) raise some barriers that interfere with the process. It is
significant that an optimal policy takes into account some resources available
to each traffic class while considering the spectral efficiency and other
related channel issues. Reinforcement learning is a dynamic and effective
method to support the accomplishment of resource allocation properly
maintaining QoS levels for applications. The technique can track the system
state as feedback to enhance the performance of a given task. Herein, it is
proposed a simple reinforcement learning mechanism introduced in LTE-A networks
and aimed to choose and limit the number of resources allocated for each
traffic class, regarding the QoS Class Identifier (QCI), at each Transmission
Time Interval (TTI) along the scheduling procedure. The proposed mechanism
implements a Markov Decision Process (MDP) solved by the Q-Learning algorithm
to find an optimal action-state decision policy. The results obtained from
simulation exhibit good performance, especially for the real-time Video
application.
Effective Image Retrieval via Multilinear Multi-index Fusion
Authors: Zhizhong Zhang, Yuan Xie, Wensheng Zhang, Qi Tian
Date: 2017-09-27 00:00:00
Tasks: Image Retrieval, Retrieval

Multi-index fusion has demonstrated impressive performances in retrieval task
by integrating different visual representations in a unified framework.
However, previous works mainly consider propagating similarities via neighbor
structure, ignoring the high order information among different visual
representations. In this paper, we propose a new multi-index fusion scheme for
image retrieval. By formulating this procedure as a multilinear based
optimization problem, the complementary information hidden in different indexes
can be explored more thoroughly. Specially, we first build our multiple indexes
from various visual representations. Then a so-called index-specific functional
matrix, which aims to propagate similarities, is introduced for updating the
original index. The functional matrices are then optimized in a unified tensor
space to achieve a refinement, such that the relevant images can be pushed more
closer. The optimization problem can be efficiently solved by the augmented
Lagrangian method with theoretical convergence guarantee. Unlike the
traditional multi-index fusion scheme, our approach embeds the multi-index
subspace structure into the new indexes with sparse constraint, thus it has
little additional memory consumption in online query stage. Experimental
evaluation on three benchmark datasets reveals that the proposed approach
achieves the state-of-the-art performance, i.e., N-score 3.94 on UKBench, mAP
94.1\% on Holiday and 62.39\% on Market-1501.
Multi-way Interacting Regression via Factorization Machines
Authors: Mikhail Yurochkin, XuanLong Nguyen, Nikolaos Vasiloglou
Date: 2017-09-27 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Demand Forecasting, regression

We propose a Bayesian regression method that accounts for multi-way
interactions of arbitrary orders among the predictor variables. Our model makes
use of a factorization mechanism for representing the regression coefficients
of interactions among the predictors, while the interaction selection is guided
by a prior distribution on random hypergraphs, a construction which generalizes
the Finite Feature Model. We present a posterior inference algorithm based on
Gibbs sampling, and establish posterior consistency of our regression model.
Our method is evaluated with extensive experiments on simulated data and
demonstrated to be able to identify meaningful interactions in applications in
genetics and retail demand forecasting.
Dynamic Label Graph Matching for Unsupervised Video Re-Identification
Authors: Mang Ye, Andy J. Ma, Liang Zheng, Jiawei Li, P C Yuen
Date: 2017-09-27 00:00:00
Proceeding: ICCV 2017 10
Tasks: Graph Matching, Person Re-Identification, Unsupervised Person Re-Identification

Label estimation is an important component in an unsupervised person
re-identification (re-ID) system. This paper focuses on cross-camera label
estimation, which can be subsequently used in feature learning to learn robust
re-ID models. Specifically, we propose to construct a graph for samples in each
camera, and then graph matching scheme is introduced for cross-camera labeling
association. While labels directly output from existing graph matching methods
may be noisy and inaccurate due to significant cross-camera variations, this
paper proposes a dynamic graph matching (DGM) method. DGM iteratively updates
the image graph and the label estimation process by learning a better feature
space with intermediate estimated labels. DGM is advantageous in two aspects:
1) the accuracy of estimated labels is improved significantly with the
iterations; 2) DGM is robust to noisy initial training data. Extensive
experiments conducted on three benchmarks including the large-scale MARS
dataset show that DGM yields competitive performance to fully supervised
baselines, and outperforms competing unsupervised learning methods.
SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine
Authors: Milad Zafar Nezhad, Dongxiao Zhu, Najibesadat Sadati, Kai Yang, Phillip Levy
Date: 2017-09-26 00:00:00
Tasks: Clustering

Traditional medicine typically applies one-size-fits-all treatment for the
entire patient population whereas precision medicine develops tailored
treatment schemes for different patient subgroups. The fact that some factors
may be more significant for a specific patient subgroup motivates clinicians
and medical researchers to develop new approaches to subgroup detection and
analysis, which is an effective strategy to personalize treatment. In this
study, we propose a novel patient subgroup detection method, called Supervised
Biclustring (SUBIC) using convex optimization and apply our approach to detect
patient subgroups and prioritize risk factors for hypertension (HTN) in a
vulnerable demographic subgroup (African-American). Our approach not only finds
patient subgroups with guidance of a clinically relevant target variable but
also identifies and prioritizes risk factors by pursuing sparsity of the input
variables and encouraging similarity among the input variables and between the
input and target variables
A Deep Neural Network for Chinese Zero Pronoun Resolution
Authors: Qingyu Yin, Wei-Nan Zhang, Yu Zhang, Ting Liu
Date: 2016-04-20 00:00:00
Tasks: Chinese Zero Pronoun Resolution, Descriptive

Existing approaches for Chinese zero pronoun resolution overlook semantic
information. This is because zero pronouns have no descriptive information,
which results in difficulty in explicitly capturing their semantic similarities
with antecedents. Moreover, when dealing with candidate antecedents,
traditional systems simply take advantage of the local information of a single
candidate antecedent while failing to consider the underlying information
provided by the other candidates from a global perspective. To address these
weaknesses, we propose a novel zero pronoun-specific neural network, which is
capable of representing zero pronouns by utilizing the contextual information
at the semantic level. In addition, when dealing with candidate antecedents, a
two-level candidate encoder is employed to explicitly capture both the local
and global information of candidate antecedents. We conduct experiments on the
Chinese portion of the OntoNotes 5.0 corpus. Experimental results show that our
approach substantially outperforms the state-of-the-art method in various
experimental settings.
Symbolic Analysis-based Reduced Order Markov Modeling of Time Series Data
Authors: Devesh K. Jha, Nurali Virani, Jan Reimann, Abhishek Srivastav, Asok Ray
Date: 2017-09-26 00:00:00
Tasks: Bayesian Inference, Clustering, Time Series, Time Series Analysis

This paper presents a technique for reduced-order Markov modeling for compact
representation of time-series data. In this work, symbolic dynamics-based tools
have been used to infer an approximate generative Markov model. The time-series
data are first symbolized by partitioning the continuous measurement space of
the signal and then, the discrete sequential data are modeled using symbolic
dynamics. In the proposed approach, the size of temporal memory of the symbol
sequence is estimated from spectral properties of the resulting stochastic
matrix corresponding to a first-order Markov model of the symbol sequence.
Then, hierarchical clustering is used to represent the states of the
corresponding full-state Markov model to construct a reduced-order or size
Markov model with a non-deterministic algebraic structure. Subsequently, the
parameters of the reduced-order Markov model are identified from the original
model by making use of a Bayesian inference rule. The final model is selected
using information-theoretic criteria. The proposed concept is elucidated and
validated on two different data sets as examples. The first example analyzes a
set of pressure data from a swirl-stabilized combustor, where controlled
protocols are used to induce flame instabilities. Variations in the complexity
of the derived Markov model represent how the system operating condition
changes from a stable to an unstable combustion regime. In the second example,
the data set is taken from NASA's data repository for prognostics of bearings
on rotating shafts. We show that, even with a very small state-space, the
reduced-order models are able to achieve comparable performance and that the
proposed approach provides flexibility in the selection of a final model for
representation and learning.
Learning to Explain Non-Standard English Words and Phrases
Authors: Ke Ni, William Yang Wang
Date: 2017-09-26 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Sentence

We describe a data-driven approach for automatically explaining new,
non-standard English expressions in a given sentence, building on a large
dataset that includes 15 years of crowdsourced examples from
UrbanDictionary.com. Unlike prior studies that focus on matching keywords from
a slang dictionary, we investigate the possibility of learning a neural
sequence-to-sequence model that generates explanations of unseen non-standard
English expressions given context. We propose a dual encoder approach---a
word-level encoder learns the representation of context, and a second
character-level encoder to learn the hidden representation of the target
non-standard expression. Our model can produce reasonable definitions of new
non-standard English expressions given their context with certain confidence.
Lexical Disambiguation in Natural Language Questions (NLQs)
Authors: Omar Al-Harbi, Shaidah Jusoh, Norita Md Norwawi
Date: 2017-09-26 00:00:00
Tasks: Question Answering

Question processing is a fundamental step in a question answering (QA)
application, and its quality impacts the performance of QA application. The
major challenging issue in processing question is how to extract semantic of
natural language questions (NLQs). A human language is ambiguous. Ambiguity may
occur at two levels; lexical and syntactic. In this paper, we propose a new
approach for resolving lexical ambiguity problem by integrating context
knowledge and concepts knowledge of a domain, into shallow natural language
processing (SNLP) techniques. Concepts knowledge is modeled using ontology,
while context knowledge is obtained from WordNet, and it is determined based on
neighborhood words in a question. The approach will be applied to a university
QA system.
Predicting Disease-Gene Associations using Cross-Document Graph-based Features
Authors: Hendrik ter Horst, Matthias Hartung, Roman Klinger, Matthias Zwick, Philipp Cimiano
Date: 2017-09-26 00:00:00
Tasks: valid

In the context of personalized medicine, text mining methods pose an
interesting option for identifying disease-gene associations, as they can be
used to generate novel links between diseases and genes which may complement
knowledge from structured databases. The most straightforward approach to
extract such links from text is to rely on a simple assumption postulating an
association between all genes and diseases that co-occur within the same
document. However, this approach (i) tends to yield a number of spurious
associations, (ii) does not capture different relevant types of associations,
and (iii) is incapable of aggregating knowledge that is spread across
documents. Thus, we propose an approach in which disease-gene co-occurrences
and gene-gene interactions are represented in an RDF graph. A machine
learning-based classifier is trained that incorporates features extracted from
the graph to separate disease-gene pairs into valid disease-gene associations
and spurious ones. On the manually curated Genetic Testing Registry, our
approach yields a 30 points increase in F1 score over a plain co-occurrence
baseline.