pwc-archive/papers-with-abstracts huggingface dataset:
Inferring Coupling of Distributed Dynamical Systems via Transfer Entropy
Authors: Oliver M. Cliff, Mikhail Prokopenko, Robert Fitch
Date: 2016-11-02 00:00:00

In this work, we are interested in structure learning for a set of spatially
distributed dynamical systems, where individual subsystems are coupled via
latent variables and observed through a filter. We represent this model as a
directed acyclic graph (DAG) that characterises the unidirectional coupling
between subsystems. Standard approaches to structure learning are not
applicable in this framework due to the hidden variables, however we can
exploit the properties of certain dynamical systems to formulate exact methods
based on state space reconstruction. We approach the problem by using
reconstruction theorems to analytically derive a tractable expression for the
KL-divergence of a candidate DAG from the observed dataset. We show this
measure can be decomposed as a function of two information-theoretic measures,
transfer entropy and stochastic interaction. We then present two mathematically
robust scoring functions based on transfer entropy and statistical independence
tests. These results support the previously held conjecture that transfer
entropy can be used to infer effective connectivity in complex networks.
A nonparametric HMM for genetic imputation and coalescent inference
Authors: Lloyd T. Elliott, Yee Whye Teh
Date: 2016-11-02 00:00:00
Tasks: Imputation

Genetic sequence data are well described by hidden Markov models (HMMs) in
which latent states correspond to clusters of similar mutation patterns. Theory
from statistical genetics suggests that these HMMs are nonhomogeneous (their
transition probabilities vary along the chromosome) and have large support for
self transitions. We develop a new nonparametric model of genetic sequence
data, based on the hierarchical Dirichlet process, which supports these self
transitions and nonhomogeneity. Our model provides a parameterization of the
genetic process that is more parsimonious than other more general nonparametric
models which have previously been applied to population genetics. We provide
truncation-free MCMC inference for our model using a new auxiliary sampling
scheme for Bayesian nonparametric HMMs. In a series of experiments on male X
chromosome data from the Thousand Genomes Project and also on data simulated
from a population bottleneck we show the benefits of our model over the popular
finite model fastPHASE, which can itself be seen as a parametric truncation of
our model. We find that the number of HMM states found by our model is
correlated with the time to the most recent common ancestor in population
bottlenecks. This work demonstrates the flexibility of Bayesian nonparametrics
applied to large and complex genetic data.
An application of incomplete pairwise comparison matrices for ranking top tennis players
Authors: Sándor Bozóki, László Csató, József Temesi
Date: 2016-11-02 00:00:00
Tasks: Attribute, Decision Making

Pairwise comparison is an important tool in multi-attribute decision making.
Pairwise comparison matrices (PCM) have been applied for ranking criteria and
for scoring alternatives according to a given criterion. Our paper presents a
special application of incomplete PCMs: ranking of professional tennis players
based on their results against each other. The selected 25 players have been on
the top of the ATP rankings for a shorter or longer period in the last 40
years. Some of them have never met on the court. One of the aims of the paper
is to provide ranking of the selected players, however, the analysis of
incomplete pairwise comparison matrices is also in the focus. The eigenvector
method and the logarithmic least squares method were used to calculate weights
from incomplete PCMs. In our results the top three players of four decades were
Nadal, Federer and Sampras. Some questions have been raised on the properties
of incomplete PCMs and remains open for further investigation.
The Intelligent Voice 2016 Speaker Recognition System
Authors: Abbas Khosravani, Cornelius Glackin, Nazim Dugan, Gérard Chollet, Nigel Cannings
Date: 2016-11-02 00:00:00
Tasks: Speaker Recognition

This paper presents the Intelligent Voice (IV) system submitted to the NIST
2016 Speaker Recognition Evaluation (SRE). The primary emphasis of SRE this
year was on developing speaker recognition technology which is robust for novel
languages that are much more heterogeneous than those used in the current
state-of-the-art, using significantly less training data, that does not contain
meta-data from those languages. The system is based on the state-of-the-art
i-vector/PLDA which is developed on the fixed training condition, and the
results are reported on the protocol defined on the development set of the
challenge.
Path-based vs. Distributional Information in Recognizing Lexical Semantic Relations
Authors: Vered Shwartz, Ido Dagan
Date: 2016-08-17 00:00:00
Proceeding: WS 2016 12
Tasks: General Classification

Recognizing various semantic relations between terms is beneficial for many
NLP tasks. While path-based and distributional information sources are
considered complementary for this task, the superior results the latter showed
recently suggested that the former's contribution might have become obsolete.
We follow the recent success of an integrated neural method for hypernymy
detection (Shwartz et al., 2016) and extend it to recognize multiple relations.
The empirical results show that this method is effective in the multiclass
setting as well. We further show that the path-based information source always
contributes to the classification, and analyze the cases in which it mostly
complements the distributional information.
When coding meets ranking: A joint framework based on local learning
Authors: Jim Jing-Yan Wang, Xuefeng Cui, Ge Yu, Lili Guo, Xin Gao
Date: 2014-09-08 00:00:00
Tasks: Retrieval

Sparse coding, which represents a data point as a sparse reconstruction code
with regard to a dictionary, has been a popular data representation method.
Meanwhile, in database retrieval problems, learning the ranking scores from
data points plays an important role. Up to now, these two problems have always
been considered separately, assuming that data coding and ranking are two
independent and irrelevant problems. However, is there any internal
relationship between sparse coding and ranking score learning? If yes, how to
explore and make use of this internal relationship? In this paper, we try to
answer these questions by developing the first joint sparse coding and ranking
score learning algorithm. To explore the local distribution in the sparse code
space, and also to bridge coding and ranking problems, we assume that in the
neighborhood of each data point, the ranking scores can be approximated from
the corresponding sparse codes by a local linear function. By considering the
local approximation error of ranking scores, the reconstruction error and
sparsity of sparse coding, and the query information provided by the user, we
construct a unified objective function for learning of sparse codes, the
dictionary and ranking scores. We further develop an iterative algorithm to
solve this optimization problem.
Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text
Authors: Ameya Prabhu, Aditya Joshi, Manish Shrivastava, Vasudeva Varma
Date: 2016-11-02 00:00:00
Proceeding: COLING 2016 12
Tasks: Opinion Mining, Sentiment Analysis

Sentiment analysis (SA) using code-mixed data from social media has several
applications in opinion mining ranging from customer satisfaction to social
campaign analysis in multilingual societies. Advances in this area are impeded
by the lack of a suitable annotated dataset. We introduce a Hindi-English
(Hi-En) code-mixed dataset for sentiment analysis and perform empirical
analysis comparing the suitability and performance of various state-of-the-art
SA methods in social media.
  In this paper, we introduce learning sub-word level representations in LSTM
(Subword-LSTM) architecture instead of character-level or word-level
representations. This linguistic prior in our architecture enables us to learn
the information about sentiment value of important morphemes. This also seems
to work well in highly noisy text containing misspellings as shown in our
experiments which is demonstrated in morpheme-level feature maps learned by our
model. Also, we hypothesize that encoding this linguistic prior in the
Subword-LSTM architecture leads to the superior performance. Our system attains
accuracy 4-5% greater than traditional approaches on our dataset, and also
outperforms the available system for sentiment analysis in Hi-En code-mixed
text by 18%.
CRF-CNN: Modeling Structured Information in Human Pose Estimation
Authors: Xiao Chu, Wanli Ouyang, Hongsheng Li, Xiaogang Wang
Date: 2016-11-02 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Pose Estimation

Deep convolutional neural networks (CNN) have achieved great success. On the
other hand, modeling structural information has been proved critical in many
vision problems. It is of great interest to integrate them effectively. In a
classical neural network, there is no message passing between neurons in the
same layer. In this paper, we propose a CRF-CNN framework which can
simultaneously model structural information in both output and hidden feature
layers in a probabilistic way, and it is applied to human pose estimation. A
message passing scheme is proposed, so that in various layers each body joint
receives messages from all the others in an efficient way. Such message passing
can be implemented with convolution between features maps in the same layer,
and it is also integrated with feedforward propagation in neural networks.
Finally, a neural network implementation of end-to-end learning CRF-CNN is
provided. Its effectiveness is demonstrated through experiments on two
benchmark datasets.
Quantum Laplacian Eigenmap
Authors: Yiming Huang, Xiaoyu Li
Date: 2016-11-02 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction

Laplacian eigenmap algorithm is a typical nonlinear model for dimensionality
reduction in classical machine learning. We propose an efficient quantum
Laplacian eigenmap algorithm to exponentially speed up the original
counterparts. In our work, we demonstrate that the Hermitian chain product
proposed in quantum linear discriminant analysis (arXiv:1510.00113,2015) can be
applied to implement quantum Laplacian eigenmap algorithm. While classical
Laplacian eigenmap algorithm requires polynomial time to solve the eigenvector
problem, our algorithm is able to exponentially speed up nonlinear
dimensionality reduction.
Structure vs. Language: Investigating the Multi-factors of Asymmetric Opinions on Online Social Interrelationship with a Case Study
Authors: Bo Wang, Yingjun Sun, Yu-An Wang
Date: 2016-11-02 00:00:00

Though current researches often study the properties of online social
relationship from an objective view, we also need to understand individuals'
subjective opinions on their interrelationships in social computing studies.
Inspired by the theories from sociolinguistics, the latest work indicates that
interactive language can reveal individuals' asymmetric opinions on their
interrelationship. In this work, in order to explain the opinions' asymmetry on
interrelationship with more latent factors, we extend the investigation from
single relationship to the structural context in online social network. We
analyze the correlation between interactive language features and the
structural context of interrelationships. The structural context of vertex,
edges and triangles in social network are considered. With statistical analysis
on Enron email dataset, we find that individuals' opinions (measured by
interactive language features) on their interrelationship are related to some
of their important structural context in social network. This result can help
us to understand and measure the individuals' opinions on their
interrelationship with more intrinsic information.
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
Authors: Hao Wang, Xingjian Shi, Dit-yan Yeung
Date: 2016-11-02 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Collaborative Filtering, Denoising, Recommendation Systems

Hybrid methods that utilize both content and rating information are commonly
used in many recommender systems. However, most of them use either handcrafted
features or the bag-of-words representation as a surrogate for the content
information but they are neither effective nor natural enough. To address this
problem, we develop a collaborative recurrent autoencoder (CRAE) which is a
denoising recurrent autoencoder (DRAE) that models the generation of content
sequences in the collaborative filtering (CF) setting. The model generalizes
recent advances in recurrent deep learning from i.i.d. input to non-i.i.d.
(CF-based) input and provides a new denoising scheme along with a novel
learnable pooling scheme for the recurrent autoencoder. To do this, we first
develop a hierarchical Bayesian model for the DRAE and then generalize it to
the CF setting. The synergy between denoising and CF enables CRAE to make
accurate recommendations while learning to fill in the blanks in sequences.
Experiments on real-world datasets from different domains (CiteULike and
Netflix) show that, by jointly modeling the order-aware generation of sequences
for the content information and performing CF for the ratings, CRAE is able to
significantly outperform the state of the art on both the recommendation task
based on ratings and the sequence generation task based on content information.
Deep Learning Approximation for Stochastic Control Problems
Authors: Jiequn Han, Weinan E
Date: 2016-11-02 00:00:00
Tasks: Deep Learning

Many real world stochastic control problems suffer from the "curse of
dimensionality". To overcome this difficulty, we develop a deep learning
approach that directly solves high-dimensional stochastic control problems
based on Monte-Carlo sampling. We approximate the time-dependent controls as
feedforward neural networks and stack these networks together through model
dynamics. The objective function for the control problem plays the role of the
loss function for the deep neural network. We test this approach using examples
from the areas of optimal trading and energy storage. Our results suggest that
the algorithm presented here achieves satisfactory accuracy and at the same
time, can handle rather high dimensional problems.
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
Authors: Hao Wang, Xingjian Shi, Dit-yan Yeung
Date: 2016-11-02 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Decision Making Under Uncertainty, Link Prediction

Neural networks (NN) have achieved state-of-the-art performance in various
applications. Unfortunately in applications where training data is
insufficient, they are often prone to overfitting. One effective way to
alleviate this problem is to exploit the Bayesian approach by using Bayesian
neural networks (BNN). Another shortcoming of NN is the lack of flexibility to
customize different distributions for the weights and neurons according to the
data, as is often done in probabilistic graphical models. To address these
problems, we propose a class of probabilistic neural networks, dubbed
natural-parameter networks (NPN), as a novel and lightweight Bayesian treatment
of NN. NPN allows the usage of arbitrary exponential-family distributions to
model the weights and neurons. Different from traditional NN and BNN, NPN takes
distributions as input and goes through layers of transformation before
producing distributions to match the target output distributions. As a Bayesian
treatment, efficient backpropagation (BP) is performed to learn the natural
parameters for the distributions over both the weights and neurons. The output
distributions of each layer, as byproducts, may be used as second-order
representations for the associated tasks such as link prediction. Experiments
on real-world datasets show that NPN can achieve state-of-the-art performance.
Bots as Virtual Confederates: Design and Ethics
Authors: Peter M. Krafft, Michael Macy, Alex Pentland
Date: 2016-11-02 00:00:00
Tasks: Ethics

The use of bots as virtual confederates in online field experiments holds
extreme promise as a new methodological tool in computational social science.
However, this potential tool comes with inherent ethical challenges. Informed
consent can be difficult to obtain in many cases, and the use of confederates
necessarily implies the use of deception. In this work we outline a design
space for bots as virtual confederates, and we propose a set of guidelines for
meeting the status quo for ethical experimentation. We draw upon examples from
prior work in the CSCW community and the broader social science literature for
illustration. While a handful of prior researchers have used bots in online
experimentation, our work is meant to inspire future work in this area and
raise awareness of the associated ethical issues.
And the Winner is ...: Bayesian Twitter-based Prediction on 2016 U.S. Presidential Election
Authors: Elvyna Tunggawan, Yustinus Eko Soelistio
Date: 2016-11-02 00:00:00

This paper describes a Naive-Bayesian predictive model for 2016 U.S.
Presidential Election based on Twitter data. We use 33,708 tweets gathered
since December 16, 2015 until February 29, 2016. We introduce a simpler data
preprocessing method to label the data and train the model. The model achieves
95.8% accuracy on 10-fold cross validation and predicts Ted Cruz and Bernie
Sanders as Republican and Democratic nominee respectively. It achieves a
comparable result to those in its competitor methods.
Flood-Filling Networks
Authors: Michał Januszewski, Jeremy Maitin-Shepard, Peter Li, Jörgen Kornfeld, Winfried Denk, Viren Jain
Date: 2016-11-01 00:00:00
Tasks: Boundary Detection, Clustering, Image Segmentation, Segmentation, Semantic Segmentation

State-of-the-art image segmentation algorithms generally consist of at least
two successive and distinct computations: a boundary detection process that
uses local image information to classify image locations as boundaries between
objects, followed by a pixel grouping step such as watershed or connected
components that clusters pixels into segments. Prior work has varied the
complexity and approach employed in these two steps, including the
incorporation of multi-layer neural networks to perform boundary prediction,
and the use of global optimizations during pixel clustering. We propose a
unified and end-to-end trainable machine learning approach, flood-filling
networks, in which a recurrent 3d convolutional network directly produces
individual segments from a raw image. The proposed approach robustly segments
images with an unknown and variable number of objects as well as highly
variable object sizes. We demonstrate the approach on a challenging 3d image
segmentation task, connectomic reconstruction from volume electron microscopy
data, on which flood-filling neural networks substantially improve accuracy
over other state-of-the-art methods. The proposed approach can replace complex
multi-step segmentation pipelines with a single neural network that is learned
end-to-end.
CB2CF: A Neural Multiview Content-to-Collaborative Filtering Model for Completely Cold Item Recommendations
Authors: Oren Barkan, Noam Koenigstein, Eylon Yogev, Ori Katz
Date: 2016-11-01 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

In Recommender Systems research, algorithms are often characterized as either Collaborative Filtering (CF) or Content Based (CB). CF algorithms are trained using a dataset of user preferences while CB algorithms are typically based on item profiles. These approaches harness different data sources and therefore the resulting recommended items are generally very different. This paper presents the CB2CF, a deep neural multiview model that serves as a bridge from items content into their CF representations. CB2CF is a real-world algorithm designed for Microsoft Store services that handle around a billion users worldwide. CB2CF is demonstrated on movies and apps recommendations, where it is shown to outperform an alternative CB model on completely cold items.
The Machine Learning Algorithm as Creative Musical Tool
Authors: Rebecca Fiebrink, Baptiste Caramiaux
Date: 2016-11-01 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning is the capacity of a computational system to learn
structures from datasets in order to make predictions on newly seen data. Such
an approach offers a significant advantage in music scenarios in which
musicians can teach the system to learn an idiosyncratic style, or can break
the rules to explore the system's capacity in unexpected ways. In this chapter
we draw on music, machine learning, and human-computer interaction to elucidate
an understanding of machine learning algorithms as creative tools for music and
the sonic arts. We motivate a new understanding of learning algorithms as
human-computer interfaces. We show that, like other interfaces, learning
algorithms can be characterised by the ways their affordances intersect with
goals of human users. We also argue that the nature of interaction between
users and algorithms impacts the usability and usefulness of those algorithms
in profound ways. This human-centred view of machine learning motivates our
concluding discussion of what it means to employ machine learning as a creative
tool.
Using Artificial Intelligence to Identify State Secrets
Authors: Renato Rocha Souza, Flavio Codeco Coelho, Rohan Shah, Matthew Connelly
Date: 2016-11-01 00:00:00
Tasks: Recommendation Systems

Whether officials can be trusted to protect national security information has
become a matter of great public controversy, reigniting a long-standing debate
about the scope and nature of official secrecy. The declassification of
millions of electronic records has made it possible to analyze these issues
with greater rigor and precision. Using machine-learning methods, we examined
nearly a million State Department cables from the 1970s to identify features of
records that are more likely to be classified, such as international
negotiations, military operations, and high-level communications. Even with
incomplete data, algorithms can use such features to identify 90% of classified
cables with <11% false positives. But our results also show that there are
longstanding problems in the identification of sensitive information. Error
analysis reveals many examples of both overclassification and
underclassification. This indicates both the need for research on inter-coder
reliability among officials as to what constitutes classified material and the
opportunity to develop recommender systems to better manage both classification
and declassification.
Faster decoding for subword level Phrase-based SMT between related languages
Authors: Anoop Kunchukuttan, Pushpak Bhattacharyya
Date: 2016-11-01 00:00:00
Proceeding: WS 2016 12
Tasks: Decoder, Translation

A common and effective way to train translation systems between related
languages is to consider sub-word level basic units. However, this increases
the length of the sentences resulting in increased decoding time. The increase
in length is also impacted by the specific choice of data format for
representing the sentences as subwords. In a phrase-based SMT framework, we
investigate different choices of decoder parameters as well as data format and
their impact on decoding time and translation accuracy. We suggest best options
for these settings that significantly improve decoding time with little impact
on the translation accuracy.