pwc-archive/papers-with-abstracts huggingface dataset:
Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder
Authors: Thanh-Le Ha, Jan Niehues, Alexander Waibel
Date: 2016-11-15 00:00:00
Proceeding: IWSLT 2016 12
Tasks: Decoder, Machine Translation, NMT, Translation

In this paper, we present our first attempts in building a multilingual
Neural Machine Translation framework under a unified approach. We are then able
to employ attention-based NMT for many-to-many multilingual translation tasks.
Our approach does not require any special treatment on the network architecture
and it allows us to learn minimal number of free parameters in a standard way
of training. Our approach has shown its effectiveness in an under-resourced
translation scenario with considerable improvements up to 2.6 BLEU points. In
addition, the approach has achieved interesting and promising results when
applied in the translation task that there is no direct parallel corpus between
source and target languages.
Improved Particle Filters for Vehicle Localisation
Authors: Kira Kempinska, John Shawe-Taylor
Date: 2016-11-15 00:00:00

The ability to track a moving vehicle is of crucial importance in numerous
applications. The task has often been approached by the importance sampling
technique of particle filters due to its ability to model non-linear and
non-Gaussian dynamics, of which a vehicle travelling on a road network is a
good example. Particle filters perform poorly when observations are highly
informative. In this paper, we address this problem by proposing particle
filters that sample around the most recent observation. The proposal leads to
an order of magnitude improvement in accuracy and efficiency over conventional
particle filters, especially when observations are infrequent but low-noise.
AdversariaLib: An Open-source Library for the Security Evaluation of Machine Learning Algorithms Under Attack
Authors: Igino Corona, Battista Biggio, Davide Maiorca
Date: 2016-11-15 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

We present AdversariaLib, an open-source python library for the security
evaluation of machine learning (ML) against carefully-targeted attacks. It
supports the implementation of several attacks proposed thus far in the
literature of adversarial learning, allows for the evaluation of a wide range
of ML algorithms, runs on multiple platforms, and has multi-processing enabled.
The library has a modular architecture that makes it easy to use and to extend
by implementing novel attacks and countermeasures. It relies on other
widely-used open-source ML libraries, including scikit-learn and FANN.
Classification algorithms are implemented and optimized in C/C++, allowing for
a fast evaluation of the simulated attacks. The package is distributed under
the GNU General Public License v3, and it is available for download at
http://sourceforge.net/projects/adversarialib.
Comparison of Brain Networks with Unknown Correspondences
Authors: Sofia Ira Ktena, Sarah Parisot, Jonathan Passerat-Palmbach, Daniel Rueckert
Date: 2016-11-15 00:00:00
Tasks: Graph Similarity

Graph theory has drawn a lot of attention in the field of Neuroscience during
the last decade, mainly due to the abundance of tools that it provides to
explore the interactions of elements in a complex network like the brain. The
local and global organization of a brain network can shed light on mechanisms
of complex cognitive functions, while disruptions within the network can be
linked to neurodevelopmental disorders. In this effort, the construction of a
representative brain network for each individual is critical for further
analysis. Additionally, graph comparison is an essential step for inference and
classification analyses on brain graphs. In this work we explore a method based
on graph edit distance for evaluating graph similarity, when correspondences
between network elements are unknown due to different underlying subdivisions
of the brain. We test this method on 30 unrelated subjects as well as 40 twin
pairs and show that this method can accurately reflect the higher similarity
between two related networks compared to unrelated ones, while identifying node
correspondences.
Feature Extraction and Soft Computing Methods for Aerospace Structure Defect Classification
Authors: Gianni D'Angelo, Salvatore Rampone
Date: 2016-11-15 00:00:00
Tasks: Content-Based Image Retrieval, General Classification, Image Retrieval, Retrieval, Specificity

This study concerns the effectiveness of several techniques and methods of
signals processing and data interpretation for the diagnosis of aerospace
structure defects. This is done by applying different known feature extraction
methods, in addition to a new CBIR-based one; and some soft computing
techniques including a recent HPC parallel implementation of the U-BRAIN
learning algorithm on Non Destructive Testing data. The performance of the
resulting detection systems are measured in terms of Accuracy, Sensitivity,
Specificity, and Precision. Their effectiveness is evaluated by the Matthews
correlation, the Area Under Curve (AUC), and the F-Measure. Several experiments
are performed on a standard dataset of eddy current signal samples for aircraft
structures. Our experimental results evidence that the key to a successful
defect classifier is the feature extraction method - namely the novel
CBIR-based one outperforms all the competitors - and they illustrate the
greater effectiveness of the U-BRAIN algorithm and the MLP neural network among
the soft computing methods in this kind of application.
  Keywords- Non-destructive testing (NDT); Soft Computing; Feature Extraction;
Classification Algorithms; Content-Based Image Retrieval (CBIR); Eddy Currents
(EC).
Prediction of Seasonal Temperature Using Soft Computing Techniques: Application in Benevento (Southern Italy) Area
Authors: Salvatore Rampone, Alessio Valente
Date: 2016-11-15 00:00:00

In this work two soft computing methods, Artificial Neural Networks and
Genetic Programming, are proposed in order to forecast the mean temperature
that will occur in future seasons. The area in which the soft computing
techniques were applied is that of the surroundings of the town of Benevento,
in the south of Italy, having geographic coordinates (lat. 41{\deg}07'50"N;
long.14{\deg}47'13"E). This area is not affected by maritime influences as well
as by winds coming from the west. The methods are fed by data recorded in the
meteorological stations of Benevento and Castelvenere, located in the hilly
area, which characterizes the territory surrounding this city, at 144 m a.s.l.
Both the applied methods show low error rates, while the Genetic Programming
offers an explicit rule representation (a formula) explaining the prevision.
  Keywords Seasonal Temperature Forecasting; Soft Computing; Artificial Neural
Networks; Genetic Programming; Southern Italy.
Differentiable Genetic Programming
Authors: Dario Izzo, Francesco Biscani, Alessio Mereta
Date: 2016-11-15 00:00:00
Tasks: Symbolic Regression

We introduce the use of high order automatic differentiation, implemented via
the algebra of truncated Taylor polynomials, in genetic programming. Using the
Cartesian Genetic Programming encoding we obtain a high-order Taylor
representation of the program output that is then used to back-propagate errors
during learning. The resulting machine learning framework is called
differentiable Cartesian Genetic Programming (dCGP). In the context of symbolic
regression, dCGP offers a new approach to the long unsolved problem of constant
representation in GP expressions. On several problems of increasing complexity
we find that dCGP is able to find the exact form of the symbolic expression as
well as the constants values. We also demonstrate the use of dCGP to solve a
large class of differential equations and to find prime integrals of dynamical
systems, presenting, in both cases, results that confirm the efficacy of our
approach.
Convolutional Regression for Visual Tracking
Authors: Kai Chen, Wenbing Tao
Date: 2016-11-14 00:00:00
Tasks: Object Tracking, regression, Visual Object Tracking, Visual Tracking

Recently, discriminatively learned correlation filters (DCF) has drawn much
attention in visual object tracking community. The success of DCF is
potentially attributed to the fact that a large amount of samples are utilized
to train the ridge regression model and predict the location of object. To
solve the regression problem in an efficient way, these samples are all
generated by circularly shifting from a search patch. However, these synthetic
samples also induce some negative effects which weaken the robustness of DCF
based trackers.
  In this paper, we propose a Convolutional Regression framework for visual
tracking (CRT). Instead of learning the linear regression model in a closed
form, we try to solve the regression problem by optimizing a one-channel-output
convolution layer with Gradient Descent (GD). In particular, the receptive
field size of the convolution layer is set to the size of object. Contrary to
DCF, it is possible to incorporate all "real" samples clipped from the whole
image. A critical issue of the GD approach is that most of the convolutional
samples are negative and the contribution of positive samples will be
suppressed. To address this problem, we propose a novel Automatic Hard Negative
Mining method to eliminate easy negatives and enhance positives. Extensive
experiments are conducted on a widely-used benchmark with 100 sequences. The
results show that the proposed algorithm achieves outstanding performance and
outperforms almost all the existing DCF based algorithms.
Recoverability of Joint Distribution from Missing Data
Authors: Jin Tian
Date: 2016-11-15 00:00:00
Tasks: Missing Values

A probabilistic query may not be estimable from observed data corrupted by
missing values if the data are not missing at random (MAR). It is therefore of
theoretical interest and practical importance to determine in principle whether
a probabilistic query is estimable from missing data or not when the data are
not MAR. We present an algorithm that systematically determines whether the
joint probability is estimable from observed data with missing values, assuming
that the data-generation model is represented as a Bayesian network containing
unobserved latent variables that not only encodes the dependencies among the
variables but also explicitly portrays the mechanisms responsible for the
missingness process. The result significantly advances the existing work.
The Benefits of Word Embeddings Features for Active Learning in Clinical Information Extraction
Authors: Mahnoosh Kholghi, Lance De Vine, Laurianne Sitbon, Guido Zuccon, Anthony Nguyen
Date: 2016-07-11 00:00:00
Proceeding: ALTA 2016 12
Tasks: Active Learning, Diversity, Informativeness, Word Embeddings

This study investigates the use of unsupervised word embeddings and sequence
features for sample representation in an active learning framework built to
extract clinical concepts from clinical free text. The objective is to further
reduce the manual annotation effort while achieving higher effectiveness
compared to a set of baseline features. Unsupervised features are derived from
skip-gram word embeddings and a sequence representation approach. The
comparative performance of unsupervised features and baseline hand-crafted
features in an active learning framework are investigated using a wide range of
selection criteria including least confidence, information diversity,
information density and diversity, and domain knowledge informativeness. Two
clinical datasets are used for evaluation: the i2b2/VA 2010 NLP challenge and
the ShARe/CLEF 2013 eHealth Evaluation Lab. Our results demonstrate significant
improvements in terms of effectiveness as well as annotation effort savings
across both datasets. Using unsupervised features along with baseline features
for sample representation lead to further savings of up to 9% and 10% of the
token and concept annotation rates, respectively.
Measuring Asymmetric Opinions on Online Social Interrelationship with Language and Network Features
Authors: Bo Wang, Yanshu Yu, Yu-An Wang
Date: 2016-11-02 00:00:00

Instead of studying the properties of social relationship from an objective
view, in this paper, we focus on individuals' subjective and asymmetric
opinions on their interrelationships. Inspired by the theories from
sociolinguistics, we investigate two individuals' opinions on their
interrelationship with their interactive language features. Eliminating the
difference of personal language style, we clarify that the asymmetry of
interactive language feature values can indicate individuals' asymmetric
opinions on their interrelationship. We also discuss how the degree of
opinions' asymmetry is related to the individuals' personality traits.
Furthermore, to measure the individuals' asymmetric opinions on
interrelationship concretely, we develop a novel model synthetizing interactive
language and social network features. The experimental results with Enron email
dataset provide multiple evidences of the asymmetric opinions on
interrelationship, and also verify the effectiveness of the proposed model in
measuring the degree of opinions' asymmetry.
Deep Restricted Boltzmann Networks
Authors: Hengyuan Hu, Lisheng Gao, Quanbin Ma
Date: 2016-11-15 00:00:00
Tasks: Form

Building a good generative model for image has long been an important topic
in computer vision and machine learning. Restricted Boltzmann machine (RBM) is
one of such models that is simple but powerful. However, its restricted form
also has placed heavy constraints on the models representation power and
scalability. Many extensions have been invented based on RBM in order to
produce deeper architectures with greater power. The most famous ones among
them are deep belief network, which stacks multiple layer-wise pretrained RBMs
to form a hybrid model, and deep Boltzmann machine, which allows connections
between hidden units to form a multi-layer structure. In this paper, we present
a new method to compose RBMs to form a multi-layer network style architecture
and a training method that trains all layers jointly. We call the resulted
structure deep restricted Boltzmann network. We further explore the combination
of convolutional RBM with the normal fully connected RBM, which is made trivial
under our composition framework. Experiments show that our model can generate
descent images and outperform the normal RBM significantly in terms of image
quality and feature quality, without losing much efficiency for training.
Robust Matrix Regression
Authors: Hang Zhang, Fengyuan Zhu, Shixin Li
Date: 2016-11-15 00:00:00
Tasks: regression

Modern technologies are producing datasets with complex intrinsic structures,
and they can be naturally represented as matrices instead of vectors. To
preserve the latent data structures during processing, modern regression
approaches incorporate the low-rank property to the model and achieve
satisfactory performance for certain applications. These approaches all assume
that both predictors and labels for each pair of data within the training set
are accurate. However, in real-world applications, it is common to see the
training data contaminated by noises, which can affect the robustness of these
matrix regression methods. In this paper, we address this issue by introducing
a novel robust matrix regression method. We also derive efficient proximal
algorithms for model training. To evaluate the performance of our methods, we
apply it to real world applications with comparative studies. Our method
achieves the state-of-the-art performance, which shows the effectiveness and
the practical value of our method.
Knowledge Enhanced Hybrid Neural Network for Text Matching
Authors: Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou
Date: 2016-11-15 00:00:00
Tasks: Question Answering, Text Matching

Long text brings a big challenge to semantic matching due to their
complicated semantic and syntactic structures. To tackle the challenge, we
consider using prior knowledge to help identify useful information and filter
out noise to matching in long text. To this end, we propose a knowledge
enhanced hybrid neural network (KEHNN). The model fuses prior knowledge into
word representations by knowledge gates and establishes three matching channels
with words, sequential structures of sentences given by Gated Recurrent Units
(GRU), and knowledge enhanced representations. The three channels are processed
by a convolutional neural network to generate high level features for matching,
and the features are synthesized as a matching score by a multilayer
perceptron. The model extends the existing methods by conducting matching on
words, local structures of sentences, and global context of sentences.
Evaluation results from extensive experiments on public data sets for question
answering and conversation show that KEHNN can significantly outperform
the-state-of-the-art matching models and particularly improve the performance
on pairs with long text.
Entropic Causal Inference
Authors: Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath, Babak Hassibi
Date: 2016-11-12 00:00:00
Tasks: Causal Inference

We consider the problem of identifying the causal direction between two
discrete random variables using observational data. Unlike previous work, we
keep the most general functional model but make an assumption on the unobserved
exogenous variable: Inspired by Occam's razor, we assume that the exogenous
variable is simple in the true causal direction. We quantify simplicity using
R\'enyi entropy. Our main result is that, under natural assumptions, if the
exogenous variable has low $H_0$ entropy (cardinality) in the true direction,
it must have high $H_0$ entropy in the wrong direction. We establish several
algorithmic hardness results about estimating the minimum entropy exogenous
variable. We show that the problem of finding the exogenous variable with
minimum entropy is equivalent to the problem of finding minimum joint entropy
given $n$ marginal distributions, also known as minimum entropy coupling
problem. We propose an efficient greedy algorithm for the minimum entropy
coupling problem, that for $n=2$ provably finds a local optimum. This gives a
greedy algorithm for finding the exogenous variable with minimum $H_1$ (Shannon
Entropy). Our greedy entropy-based causal inference algorithm has similar
performance to the state of the art additive noise models in real datasets. One
advantage of our approach is that we make no use of the values of random
variables but only their distributions. Our method can therefore be used for
causal inference for both ordinal and also categorical data, unlike additive
noise models.
A Generic Coordinate Descent Framework for Learning from Implicit Feedback
Authors: Immanuel Bayer, Xiangnan He, Bhargav Kanagal, Steffen Rendle
Date: 2016-11-15 00:00:00
Tasks: Diversity

In recent years, interest in recommender research has shifted from explicit
feedback towards implicit feedback data. A diversity of complex models has been
proposed for a wide variety of applications. Despite this, learning from
implicit feedback is still computationally challenging. So far, most work
relies on stochastic gradient descent (SGD) solvers which are easy to derive,
but in practice challenging to apply, especially for tasks with many items. For
the simple matrix factorization model, an efficient coordinate descent (CD)
solver has been previously proposed. However, efficient CD approaches have not
been derived for more complex models.
  In this paper, we provide a new framework for deriving efficient CD
algorithms for complex recommender models. We identify and introduce the
property of k-separable models. We show that k-separability is a sufficient
property to allow efficient optimization of implicit recommender problems with
CD. We illustrate this framework on a variety of state-of-the-art models
including factorization machines and Tucker decomposition. To summarize, our
work provides the theory and building blocks to derive efficient implicit CD
algorithms for complex recommender models.
Causal Inference in Observational Data
Authors: Pranjul Yadav, Lisiane Prunelli, Alexander Hoff, Michael Steinbach, Bonnie Westra, Vipin Kumar, Gyorgy Simon
Date: 2016-11-15 00:00:00
Tasks: Causal Inference

Our aging population increasingly suffers from multiple chronic diseases
simultaneously, necessitating the comprehensive treatment of these conditions.
Finding the optimal set of drugs for a combinatorial set of diseases is a
combinatorial pattern exploration problem. Association rule mining is a popular
tool for such problems, but the requirement of health care for finding causal,
rather than associative, patterns renders association rule mining unsuitable.
To address this issue, we propose a novel framework based on the Rubin-Neyman
causal model for extracting causal rules from observational data, correcting
for a number of common biases. Specifically, given a set of interventions and a
set of items that define subpopulations (e.g., diseases), we wish to find all
subpopulations in which effective intervention combinations exist and in each
such subpopulation, we wish to find all intervention combinations such that
dropping any intervention from this combination will reduce the efficacy of the
treatment. A key aspect of our framework is the concept of closed intervention
sets which extend the concept of quantifying the effect of a single
intervention to a set of concurrent interventions. We also evaluated our causal
rule mining framework on the Electronic Health Records (EHR) data of a large
cohort of patients from Mayo Clinic and showed that the patterns we extracted
are sufficiently rich to explain the controversial findings in the medical
literature regarding the effect of a class of cholesterol drugs on Type-II
Diabetes Mellitus (T2DM).
Motion Estimated-Compensated Reconstruction with Preserved-Features in Free-Breathing Cardiac MRI
Authors: Aurelien Bustin, Anne Menini, Martin A. Janich, Darius Burschka, Jacques Felblinger, Anja C. S. Brau, Freddy Odille
Date: 2016-11-15 00:00:00
Tasks: Denoising, Image Reconstruction

To develop an efficient motion-compensated reconstruction technique for
free-breathing cardiac magnetic resonance imaging (MRI) that allows
high-quality images to be reconstructed from multiple undersampled single-shot
acquisitions. The proposed method is a joint image reconstruction and motion
correction method consisting of several steps, including a non-rigid motion
extraction and a motion-compensated reconstruction. The reconstruction includes
a denoising with the Beltrami regularization, which offers an ideal compromise
between feature preservation and staircasing reduction. Results were assessed
in simulation, phantom and volunteer experiments. The proposed joint image
reconstruction and motion correction method exhibits visible quality
improvement over previous methods while reconstructing sharper edges. Moreover,
when the acceleration factor increases, standard methods show blurry results
while the proposed method preserves image quality. The method was applied to
free-breathing single-shot cardiac MRI, successfully achieving high image
quality and higher spatial resolution than conventional segmented methods, with
the potential to offer high-quality delayed enhancement scans in challenging
patients.