pwc-archive/papers-with-abstracts huggingface dataset:
Exemplar-Centered Supervised Shallow Parametric Data Embedding
Authors: Martin Renqiang Min, Hongyu Guo, Dongjin Song
Date: 2017-02-21 00:00:00
Tasks: Dimensionality Reduction, General Classification, Information Retrieval, Metric Learning, Retrieval

Metric learning methods for dimensionality reduction in combination with
k-Nearest Neighbors (kNN) have been extensively deployed in many
classification, data embedding, and information retrieval applications.
However, most of these approaches involve pairwise training data comparisons,
and thus have quadratic computational complexity with respect to the size of
training set, preventing them from scaling to fairly big datasets. Moreover,
during testing, comparing test data against all the training data points is
also expensive in terms of both computational cost and resources required.
Furthermore, previous metrics are either too constrained or too expressive to
be well learned. To effectively solve these issues, we present an
exemplar-centered supervised shallow parametric data embedding model, using a
Maximally Collapsing Metric Learning (MCML) objective. Our strategy learns a
shallow high-order parametric embedding function and compares training/test
data only with learned or precomputed exemplars, resulting in a cost function
with linear computational complexity for both training and testing. We also
empirically demonstrate, using several benchmark datasets, that for
classification in two-dimensional embedding space, our approach not only gains
speedup of kNN by hundreds of times, but also outperforms state-of-the-art
supervised embedding approaches.
On the Fusion of Compton Scatter and Attenuation Data for Limited-view X-ray Tomographic Applications
Authors: Hamideh Rezaee, Brian Tracey, Eric L. Miller
Date: 2017-07-05 00:00:00

In this paper we demonstrate the utility of fusing energy-resolved
observations of Compton scattered photons with traditional attenuation data for
the joint recovery of mass density and photoelectric absorption in the context
of limited view tomographic imaging applications. We begin with the development
of a physical and associated numerical model for the Compton scatter process.
Using this model, we propose a variational approach recovering these two
material properties. In addition to the typical data-fidelity terms, the
optimization functional includes regularization for both the mass density and
photoelectric coefficients. We consider a novel edge-preserving method in the
case of mass density. To aid in the recovery of the photoelectric information,
we draw on our recent method in \cite{r15} and employ a non-local
regularization scheme that builds on the fact that mass density is more stably
imaged. Simulation results demonstrate clear advantages associated with the use
of both scattered photon data and energy resolved information in mapping the
two material properties of interest. Specifically, comparing images obtained
using only conventional attenuation data with those where we employ only
Compton scatter photons and images formed from the combination of the two,
shows that taking advantage of both types of data for reconstruction provides
far more accurate results.
Context Aware Document Embedding
Authors: Zhaocheng Zhu, Junfeng Hu
Date: 2017-07-05 00:00:00
Tasks: Document Embedding

Recently, doc2vec has achieved excellent results in different tasks. In this
paper, we present a context aware variant of doc2vec. We introduce a novel
weight estimating mechanism that generates weights for each word occurrence
according to its contribution in the context, using deep neural networks. Our
context aware model can achieve similar results compared to doc2vec initialized
byWikipedia trained vectors, while being much more efficient and free from
heavy external corpus. Analysis of context aware weights shows they are a kind
of enhanced IDF weights that capture sub-topic level keywords in documents.
They might result from deep neural networks that learn hidden representations
with the least entropy.
Creative Robot Dance with Variational Encoder
Authors: Agnese Augello, Emanuele Cipolla, Ignazio Infantino, Adriano Manfre, Giovanni Pilato, Filippo Vella
Date: 2017-07-05 00:00:00
Tasks: Rhythm

What we appreciate in dance is the ability of people to sponta- neously
improvise new movements and choreographies, sur- rendering to the music rhythm,
being inspired by the cur- rent perceptions and sensations and by previous
experiences, deeply stored in their memory. Like other human abilities, this,
of course, is challenging to reproduce in an artificial entity such as a robot.
Recent generations of anthropomor- phic robots, the so-called humanoids,
however, exhibit more and more sophisticated skills and raised the interest in
robotic communities to design and experiment systems devoted to automatic dance
generation. In this work, we highlight the importance to model a computational
creativity behavior in dancing robots to avoid a mere execution of
preprogrammed dances. In particular, we exploit a deep learning approach that
allows a robot to generate in real time new dancing move- ments according to to
the listened music.
Straight to Shapes: Real-time Detection of Encoded Shapes
Authors: Saumya Jetley, Michael Sapienza, Stuart Golodetz, Philip H. S. Torr
Date: 2016-11-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Decoder, Denoising, object-detection, Object Detection

Current object detection approaches predict bounding boxes, but these provide
little instance-specific information beyond location, scale and aspect ratio.
In this work, we propose to directly regress to objects' shapes in addition to
their bounding boxes and categories. It is crucial to find an appropriate shape
representation that is compact and decodable, and in which objects can be
compared for higher-order concepts such as view similarity, pose variation and
occlusion. To achieve this, we use a denoising convolutional auto-encoder to
establish an embedding space, and place the decoder after a fast end-to-end
network trained to regress directly to the encoded shape vectors. This yields
what to the best of our knowledge is the first real-time shape prediction
network, running at ~35 FPS on a high-end desktop. With higher-order shape
reasoning well-integrated into the network pipeline, the network shows the
useful practical quality of generalising to unseen categories similar to the
ones in the training set, something that most existing approaches fail to
handle.
Like trainer, like bot? Inheritance of bias in algorithmic content moderation
Authors: Reuben Binns, Michael Veale, Max Van Kleek, Nigel Shadbolt
Date: 2017-07-05 00:00:00
Tasks: Navigate

The internet has become a central medium through which `networked publics'
express their opinions and engage in debate. Offensive comments and personal
attacks can inhibit participation in these spaces. Automated content moderation
aims to overcome this problem using machine learning classifiers trained on
large corpora of texts manually annotated for offence. While such systems could
help encourage more civil debate, they must navigate inherently normatively
contestable boundaries, and are subject to the idiosyncratic norms of the human
raters who provide the training data. An important objective for platforms
implementing such measures might be to ensure that they are not unduly biased
towards or against particular norms of offence. This paper provides some
exploratory methods by which the normative biases of algorithmic content
moderation systems can be measured, by way of a case study using an existing
dataset of comments labelled for offence. We train classifiers on comments
labelled by different demographic subsets (men and women) to understand how
differences in conceptions of offence between these groups might affect the
performance of the resulting models on various test sets. We conclude by
discussing some of the ethical choices facing the implementers of algorithmic
moderation systems, given various desired levels of diversity of viewpoints
amongst discussion participants.
Machine-Learning Tests for Effects on Multiple Outcomes
Authors: Jens Ludwig, Sendhil Mullainathan, Jann Spiess
Date: 2017-07-05 00:00:00
Tasks: BIG-bench Machine Learning

In this paper we present tools for applied researchers that re-purpose off-the-shelf methods from the computer-science field of machine learning to create a "discovery engine" for data from randomized controlled trials (RCTs). The applied problem we seek to solve is that economists invest vast resources into carrying out RCTs, including the collection of a rich set of candidate outcome measures. But given concerns about inference in the presence of multiple testing, economists usually wind up exploring just a small subset of the hypotheses that the available data could be used to test. This prevents us from extracting as much information as possible from each RCT, which in turn impairs our ability to develop new theories or strengthen the design of policy interventions. Our proposed solution combines the basic intuition of reverse regression, where the dependent variable of interest now becomes treatment assignment itself, with methods from machine learning that use the data themselves to flexibly identify whether there is any function of the outcomes that predicts (or has signal about) treatment group status. This leads to correctly-sized tests with appropriate $p$-values, which also have the important virtue of being easy to implement in practice. One open challenge that remains with our work is how to meaningfully interpret the signal that these methods find.
The Complex Negotiation Dialogue Game
Authors: Romain Laroche
Date: 2017-07-05 00:00:00
Tasks: One-Shot Learning, Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

This position paper formalises an abstract model for complex negotiation
dialogue. This model is to be used for the benchmark of optimisation algorithms
ranging from Reinforcement Learning to Stochastic Games, through Transfer
Learning, One-Shot Learning or others.
Spectral Modes of Network Dynamics Reveal Increased Informational Complexity Near Criticality
Authors: Xerxes D. Arsiwalla, Pedro A. M. Mediano, Paul F. M. J. Verschure
Date: 2017-07-05 00:00:00

What does the informational complexity of dynamical networked systems tell us
about intrinsic mechanisms and functions of these complex systems? Recent
complexity measures such as integrated information have sought to
operationalize this problem taking a whole-versus-parts perspective, wherein
one explicitly computes the amount of information generated by a network as a
whole over and above that generated by the sum of its parts during state
transitions. While several numerical schemes for estimating network integrated
information exist, it is instructive to pursue an analytic approach that
computes integrated information as a function of network weights. Our
formulation of integrated information uses a Kullback-Leibler divergence
between the multi-variate distribution on the set of network states versus the
corresponding factorized distribution over its parts. Implementing stochastic
Gaussian dynamics, we perform computations for several prototypical network
topologies. Our findings show increased informational complexity near
criticality, which remains consistent across network topologies. Spectral
decomposition of the system's dynamics reveals how informational complexity is
governed by eigenmodes of both, the network's covariance and adjacency
matrices. We find that as the dynamics of the system approach criticality, high
integrated information is exclusively driven by the eigenmode corresponding to
the leading eigenvalue of the covariance matrix, while sub-leading modes get
suppressed. The implication of this result is that it might be favorable for
complex dynamical networked systems such as the human brain or communication
systems to operate near criticality so that efficient information integration
might be achieved.
Theory of the superposition principle for randomized connectionist representations in neural networks
Authors: E. Paxon Frady, Denis Kleyko, Friedrich T. Sommer
Date: 2017-07-05 00:00:00
Tasks: Retrieval

To understand cognitive reasoning in the brain, it has been proposed that
symbols and compositions of symbols are represented by activity patterns
(vectors) in a large population of neurons. Formal models implementing this
idea [Plate 2003], [Kanerva 2009], [Gayler 2003], [Eliasmith 2012] include a
reversible superposition operation for representing with a single vector an
entire set of symbols or an ordered sequence of symbols. If the representation
space is high-dimensional, large sets of symbols can be superposed and
individually retrieved. However, crosstalk noise limits the accuracy of
retrieval and information capacity. To understand information processing in the
brain and to design artificial neural systems for cognitive reasoning, a theory
of this superposition operation is essential. Here, such a theory is presented.
The superposition operations in different existing models are mapped to linear
neural networks with unitary recurrent matrices, in which retrieval accuracy
can be analyzed by a single equation. We show that networks representing
information in superposition can achieve a channel capacity of about half a bit
per neuron, a significant fraction of the total available entropy. Going beyond
existing models, superposition operations with recency effects are proposed
that avoid catastrophic forgetting when representing the history of infinite
data streams. These novel models correspond to recurrent networks with
non-unitary matrices or with nonlinear neurons, and can be analyzed and
optimized with an extension of our theory.
Determining sentiment in citation text and analyzing its impact on the proposed ranking index
Authors: Souvick Ghosh, Dipankar Das, Tanmoy Chakraborty
Date: 2017-07-05 00:00:00
Tasks: Articles, Opinion Mining, Sentiment Analysis

Whenever human beings interact with each other, they exchange or express
opinions, emotions, and sentiments. These opinions can be expressed in text,
speech or images. Analysis of these sentiments is one of the popular research
areas of present day researchers. Sentiment analysis, also known as opinion
mining tries to identify or classify these sentiments or opinions into two
broad categories - positive and negative. In recent years, the scientific
community has taken a lot of interest in analyzing sentiment in textual data
available in various social media platforms. Much work has been done on social
media conversations, blog posts, newspaper articles and various narrative
texts. However, when it comes to identifying emotions from scientific papers,
researchers have faced some difficulties due to the implicit and hidden nature
of opinion. By default, citation instances are considered inherently positive
in emotion. Popular ranking and indexing paradigms often neglect the opinion
present while citing. In this paper, we have tried to achieve three objectives.
First, we try to identify the major sentiment in the citation text and assign a
score to the instance. We have used a statistical classifier for this purpose.
Secondly, we have proposed a new index (we shall refer to it hereafter as
M-index) which takes into account both the quantitative and qualitative factors
while scoring a paper. Thirdly, we developed a ranking of research papers based
on the M-index. We also try to explain how the M-index impacts the ranking of
scientific papers.
Multiscale sequence modeling with a learned dictionary
Authors: Bart van Merriënboer, Amartya Sanyal, Hugo Larochelle, Yoshua Bengio
Date: 2017-07-03 00:00:00
Tasks: Language Modeling, Language Modelling

We propose a generalization of neural network sequence models. Instead of
predicting one symbol at a time, our multi-scale model makes predictions over
multiple, potentially overlapping multi-symbol tokens. A variation of the
byte-pair encoding (BPE) compression algorithm is used to learn the dictionary
of tokens that the model is trained with. When applied to language modelling,
our model has the flexibility of character-level models while maintaining many
of the performance benefits of word-level models. Our experiments show that
this model performs better than a regular LSTM on language modeling tasks,
especially for smaller models.
Model enumeration in propositional circumscription via unsatisfiable core analysis
Authors: Mario Alviano
Date: 2017-07-05 00:00:00

Many practical problems are characterized by a preference relation over
admissible solutions, where preferred solutions are minimal in some sense. For
example, a preferred diagnosis usually comprises a minimal set of reasons that
is sufficient to cause the observed anomaly. Alternatively, a minimal
correction subset comprises a minimal set of reasons whose deletion is
sufficient to eliminate the observed anomaly. Circumscription formalizes such
preference relations by associating propositional theories with minimal models.
The resulting enumeration problem is addressed here by means of a new algorithm
taking advantage of unsatisfiable core analysis. Empirical evidence of the
efficiency of the algorithm is given by comparing the performance of the
resulting solver, CIRCUMSCRIPTINO, with HCLASP, CAMUS MCS, LBX and MCSLS on the
enumeration of minimal models for problems originating from practical
applications.
  This paper is under consideration for acceptance in TPLP.
Machine Learning, Deepest Learning: Statistical Data Assimilation Problems
Authors: Henry Abarbanel, Paul Rozdeba, Sasha Shirman
Date: 2017-07-05 00:00:00
Tasks: BIG-bench Machine Learning

We formulate a strong equivalence between machine learning, artificial
intelligence methods and the formulation of statistical data assimilation as
used widely in physical and biological sciences. The correspondence is that
layer number in the artificial network setting is the analog of time in the
data assimilation setting. Within the discussion of this equivalence we show
that adding more layers (making the network deeper) is analogous to adding
temporal resolution in a data assimilation framework.
  How one can find a candidate for the global minimum of the cost functions in
the machine learning context using a method from data assimilation is
discussed. Calculations on simple models from each side of the equivalence are
reported.
  Also discussed is a framework in which the time or layer label is taken to be
continuous, providing a differential equation, the Euler-Lagrange equation,
which shows that the problem being solved is a two point boundary value problem
familiar in the discussion of variational methods. The use of continuous layers
is denoted "deepest learning". These problems respect a symplectic symmetry in
continuous time/layer phase space. Both Lagrangian versions and Hamiltonian
versions of these problems are presented. Their well-studied implementation in
a discrete time/layer, while respected the symplectic structure, is addressed.
The Hamiltonian version provides a direct rationale for back propagation as a
solution method for the canonical momentum.
AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks
Authors: Xudong Mao, Qing Li, Haoran Xie
Date: 2017-07-05 00:00:00
Tasks: Generative Adversarial Network

Recently, several methods based on generative adversarial network (GAN) have
been proposed for the task of aligning cross-domain images or learning a joint
distribution of cross-domain images. One of the methods is to use conditional
GAN for alignment. However, previous attempts of adopting conditional GAN do
not perform as well as other methods. In this work we present an approach for
improving the capability of the methods which are based on conditional GAN. We
evaluate the proposed method on numerous tasks and the experimental results
show that it is able to align the cross-domain images successfully in absence
of paired samples. Furthermore, we also propose another model which conditions
on multiple information such as domain information and label information.
Conditioning on domain information and label information, we are able to
conduct label propagation from the source domain to the target domain. A 2-step
alternating training algorithm is proposed to learn this model.
Development & Implementation of the Trigger for a Short-baseline Reactor Antineutrino Experiment (SoLid)
Authors: Lukas On Arnold
Date: 2017-07-05 00:00:00

SoLid, located at SCK-CEN in Mol, Belgium, is a reactor antineutrino
experiment at a very short baseline of 5.5 - 10m aiming at the search for
sterile neutrinos and for high precision measurement of the neutrino energy
spectrum of Uranium-235. It uses a novel approach using Lithium-6 sheets and
PVT cubes as scintillators for tagging the Inverse Beta-Decay products (neutron
and positron). Being located overground and close to the BR2 research reactor,
the experiment faces a large amount of backgrounds. Efficient real-time
background and noise rejection is essential in order to increase the
signal-background ratio for precise oscillation measurement and decrease data
production to a rate which can be handled by the online software. Therefore, a
reliable distinction between the neutrons and background signals is crucial.
This can be performed online with a dedicated firmware trigger. A peak counting
algorithm and an algorithm measuring time over threshold have been identified
as performing well both in terms of efficiency and fake rate, and have been
implemented onto an FPGA. After having introduced the experimental and
theoretical background of neutrino oscillation physics, as well as SoLid's
detector technology, read-out system and trigger scheme, the thesis presents
the design of the firmware neutron trigger implemented by applying machine
learning methods.
Employee turnover prediction and retention policies design: a case study
Authors: Edouard Ribes, Karim Touahri, Benoît Perthame
Date: 2017-07-05 00:00:00
Tasks: BIG-bench Machine Learning

This paper illustrates the similarities between the problems of customer
churn and employee turnover. An example of employee turnover prediction model
leveraging classical machine learning techniques is developed. Model outputs
are then discussed to design \& test employee retention policies. This type of
retention discussion is, to our knowledge, innovative and constitutes the main
value of this paper.
Deep Learning Based Large-Scale Automatic Satellite Crosswalk Classification
Authors: Rodrigo F. Berriel, Andre Teixeira Lopes, Alberto F. de Souza, Thiago Oliveira-Santos
Date: 2017-06-28 00:00:00
Tasks: Classification, Deep Learning, General Classification

High-resolution satellite imagery have been increasingly used on remote
sensing classification problems. One of the main factors is the availability of
this kind of data. Even though, very little effort has been placed on the zebra
crossing classification problem. In this letter, crowdsourcing systems are
exploited in order to enable the automatic acquisition and annotation of a
large-scale satellite imagery database for crosswalks related tasks. Then, this
dataset is used to train deep-learning-based models in order to accurately
classify satellite images that contains or not zebra crossings. A novel dataset
with more than 240,000 images from 3 continents, 9 countries and more than 20
cities was used in the experiments. Experimental results showed that freely
available crowdsourcing data can be used to accurately (97.11%) train robust
models to perform crosswalk classification on a global scale.