pwc-archive/papers-with-abstracts huggingface dataset:
Active Learning for Accurate Estimation of Linear Models
Authors: Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric
Date: 2017-03-02 00:00:00
Proceeding: ICML 2017 8
Tasks: Active Learning, Decision Making, Sequential Decision Making

We explore the sequential decision making problem where the goal is to
estimate uniformly well a number of linear models, given a shared budget of
random contexts independently sampled from a known distribution. The decision
maker must query one of the linear models for each incoming context, and
receives an observation corrupted by noise levels that are unknown, and depend
on the model instance. We present Trace-UCB, an adaptive allocation algorithm
that learns the noise levels while balancing contexts accordingly across the
different linear functions, and derive guarantees for simple regret in both
expectation and high-probability. Finally, we extend the algorithm and its
guarantees to high dimensional settings, where the number of linear models
times the dimension of the contextual space is higher than the total budget of
samples. Simulations with real data suggest that Trace-UCB is remarkably
robust, outperforming a number of baselines even when its assumptions are
violated.
A generalized multivariate Student-t mixture model for Bayesian classification and clustering of radar waveforms
Authors: Guillaume Revillon, Ali Mohammad-Djafari, Cyrille Enderli
Date: 2017-07-29 00:00:00
Tasks: Classification, Clustering, General Classification

In this paper, a generalized multivariate Student-t mixture model is
developed for classification and clustering of Low Probability of Intercept
radar waveforms. A Low Probability of Intercept radar signal is characterized
by a pulse compression waveform which is either frequency-modulated or
phase-modulated. The proposed model can classify and cluster different
modulation types such as linear frequency modulation, non linear frequency
modulation, polyphase Barker, polyphase P1, P2, P3, P4, Frank and Zadoff codes.
The classification method focuses on the introduction of a new prior
distribution for the model hyper-parameters that gives us the possibility to
handle sensitivity of mixture models to initialization and to allow a less
restrictive modeling of data. Inference is processed through a Variational
Bayes method and a Bayesian treatment is adopted for model learning, supervised
classification and clustering. Moreover, the novel prior distribution is not a
well-known probability distribution and both deterministic and stochastic
methods are employed to estimate its expectations. Some numerical experiments
show that the proposed method is less sensitive to initialization and provides
more accurate results than the previous state of the art mixture models.
On the Min-cost Traveling Salesman Problem with Drone
Authors: Quang Minh Ha, Yves Deville, Quang Dung Pham, Minh Hoàng Hà
Date: 2015-09-29 00:00:00
Tasks: Traveling Salesman Problem

Over the past few years, unmanned aerial vehicles (UAV), also known as
drones, have been adopted as part of a new logistic method in the commercial
sector called "last-mile delivery". In this novel approach, they are deployed
alongside trucks to deliver goods to customers to improve the quality of
service and reduce the transportation cost. This approach gives rise to a new
variant of the traveling salesman problem (TSP), called TSP with drone (TSP-D).
A variant of this problem that aims to minimize the time at which truck and
drone finish the service (or, in other words, to maximize the quality of
service) was studied in the work of Murray and Chu (2015). In contrast, this
paper considers a new variant of TSP-D in which the objective is to minimize
operational costs including total transportation cost and one created by waste
time a vehicle has to wait for the other. The problem is first formulated
mathematically. Then, two algorithms are proposed for the solution. The first
algorithm (TSP-LS) was adapted from the approach proposed by Murray and Chu
(2015), in which an optimal TSP solution is converted to a feasible TSP-D
solution by local searches. The second algorithm, a Greedy Randomized Adaptive
Search Procedure (GRASP), is based on a new split procedure that optimally
splits any TSP tour into a TSP-D solution. After a TSP-D solution has been
generated, it is then improved through local search operators. Numerical
results obtained on various instances of both objective functions with
different sizes and characteristics are presented. The results show that GRASP
outperforms TSP-LS in terms of solution quality under an acceptable running
time.
HashNet: Deep Learning to Hash by Continuation
Authors: Zhangjie Cao, Mingsheng Long, Jian-Min Wang, Philip S. Yu
Date: 2017-02-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: Binarization, Deep Learning, Representation Learning, Retrieval

Learning to hash has been widely applied to approximate nearest neighbor
search for large-scale multimedia retrieval, due to its computation efficiency
and retrieval quality. Deep learning to hash, which improves retrieval quality
by end-to-end representation learning and hash encoding, has received
increasing attention recently. Subject to the ill-posed gradient difficulty in
the optimization with sign activations, existing deep learning to hash methods
need to first learn continuous representations and then generate binary hash
codes in a separated binarization step, which suffer from substantial loss of
retrieval quality. This work presents HashNet, a novel deep architecture for
deep learning to hash by continuation method with convergence guarantees, which
learns exactly binary hash codes from imbalanced similarity data. The key idea
is to attack the ill-posed gradient problem in optimizing deep networks with
non-smooth binary activations by continuation method, in which we begin from
learning an easier network with smoothed activation function and let it evolve
during the training, until it eventually goes back to being the original,
difficult to optimize, deep network with the sign activation function.
Comprehensive empirical evidence shows that HashNet can generate exactly binary
hash codes and yield state-of-the-art multimedia retrieval performance on
standard benchmarks.
Synthetic Database for Evaluation of General, Fundamental Biometric Principles
Authors: Lee Friedman, Oleg Komogortsev
Date: 2017-07-29 00:00:00

We create synthetic biometric databases to study general, fundamental,
biometric principles. First, we check the validity of the synthetic database
design by comparing it to real data in terms of biometric performance. The real
data used for this validity check was from an eye-movement related biometric
database. Next, we employ our database to evaluate the impact of variations of
temporal persistence of features on biometric performance. We index temporal
persistence with the intraclass correlation coefficient (ICC). We find that
variations in temporal persistence are extremely highly correlated with
variations in biometric performance. Finally, we use our synthetic database
strategy to determine how many features are required to achieve particular
levels of performance as the number of subjects in the database increases from
100 to 10,000. An important finding is that the number of features required to
achieve various EER values (2%, 0.3%, 0.15%) is essentially constant in the
database sizes that we studied. We hypothesize that the insights obtained from
our study would be applicable to many biometric modalities where extracted
feature properties resemble the properties of the synthetic features we discuss
in this work.
Benchmarking Multimodal Sentiment Analysis
Authors: Erik Cambria, Devamanyu Hazarika, Soujanya Poria, Amir Hussain, R. B. V. Subramaanyam
Date: 2017-07-29 00:00:00
Tasks: Benchmarking, Emotion Recognition, Multimodal Sentiment Analysis, Sentiment Analysis

We propose a framework for multimodal sentiment analysis and emotion
recognition using convolutional neural network-based feature extraction from
text and visual modalities. We obtain a performance improvement of 10% over the
state of the art by combining visual, text and audio features. We also discuss
some major issues frequently ignored in multimodal sentiment analysis research:
the role of speaker-independent models, importance of the modalities and
generalizability. The paper thus serve as a new benchmark for further research
in multimodal sentiment analysis and also demonstrates the different facets of
analysis to be considered while performing such tasks.
Curriculum Learning and Minibatch Bucketing in Neural Machine Translation
Authors: Tom Kocmi, Ondrej Bojar
Date: 2017-07-29 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, NMT, Sentence, Translation

We examine the effects of particular orderings of sentence pairs on the
on-line training of neural machine translation (NMT). We focus on two types of
such orderings: (1) ensuring that each minibatch contains sentences similar in
some aspect and (2) gradual inclusion of some sentence types as the training
progresses (so called "curriculum learning"). In our English-to-Czech
experiments, the internal homogeneity of minibatches has no effect on the
training but some of our "curricula" achieve a small improvement over the
baseline.
LanideNN: Multilingual Language Identification on Character Window
Authors: Tom Kocmi, Ondřej Bojar
Date: 2017-01-12 00:00:00
Proceeding: EACL 2017 4
Tasks: Language Identification

In language identification, a common first step in natural language
processing, we want to automatically determine the language of some input text.
Monolingual language identification assumes that the given document is written
in one language. In multilingual language identification, the document is
usually in two or three languages and we just want their names. We aim one step
further and propose a method for textual language identification where
languages can change arbitrarily and the goal is to identify the spans of each
of the languages. Our method is based on Bidirectional Recurrent Neural
Networks and it performs well in monolingual and multilingual language
identification tasks on six datasets covering 131 languages. The method keeps
the accuracy also for short documents and across domains, so it is ideal for
off-the-shelf use without preparation of training data.
Generalizing the Role of Determinization in Probabilistic Planning
Authors: Luis Pineda, Shlomo Zilberstein
Date: 2017-05-21 00:00:00

The stochastic shortest path problem (SSP) is a highly expressive model for
probabilistic planning. The computational hardness of SSPs has sparked interest
in determinization-based planners that can quickly solve large problems.
However, existing methods employ a simplistic approach to determinization. In
particular, they ignore the possibility of tailoring the determinization to the
specific characteristics of the target domain. In this work we examine this
question, by showing that learning a good determinization for a planning domain
can be done efficiently and can improve performance. Moreover, we show how to
directly incorporate probabilistic reasoning into the planning problem when a
good determinization is not sufficient by itself. Based on these insights, we
introduce a planner, FF-LAO*, that outperforms state-of-the-art probabilistic
planners on several well-known competition benchmarks.
Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm
Authors: Vassilis Vassiliades, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret
Date: 2016-10-18 00:00:00

The recently introduced Multi-dimensional Archive of Phenotypic Elites
(MAP-Elites) is an evolutionary algorithm capable of producing a large archive
of diverse, high-performing solutions in a single run. It works by discretizing
a continuous feature space into unique regions according to the desired
discretization per dimension. While simple, this algorithm has a main drawback:
it cannot scale to high-dimensional feature spaces since the number of regions
increase exponentially with the number of dimensions. In this paper, we address
this limitation by introducing a simple extension of MAP-Elites that has a
constant, pre-defined number of regions irrespective of the dimensionality of
the feature space. Our main insight is that methods from computational geometry
could partition a high-dimensional space into well-spread geometric regions. In
particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to
divide the feature space into a desired number of regions; it then places every
generated individual in its closest region, replacing a less fit one if the
region is already occupied. We demonstrate the effectiveness of the new
"CVT-MAP-Elites" algorithm in high-dimensional feature spaces through
comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks.
Topology Analysis of International Networks Based on Debates in the United Nations
Authors: Stefano Gurciullo, Slava Mikhaylov
Date: 2017-07-29 00:00:00
Tasks: Clustering, Dimensionality Reduction

In complex, high dimensional and unstructured data it is often difficult to
extract meaningful patterns. This is especially the case when dealing with
textual data. Recent studies in machine learning, information theory and
network science have developed several novel instruments to extract the
semantics of unstructured data, and harness it to build a network of relations.
Such approaches serve as an efficient tool for dimensionality reduction and
pattern detection. This paper applies semantic network science to extract
ideological proximity in the international arena, by focusing on the data from
General Debates in the UN General Assembly on the topics of high salience to
international community. UN General Debate corpus (UNGDC) covers all high-level
debates in the UN General Assembly from 1970 to 2014, covering all UN member
states. The research proceeds in three main steps. First, Latent Dirichlet
Allocation (LDA) is used to extract the topics of the UN speeches, and
therefore semantic information. Each country is then assigned a vector
specifying the exposure to each of the topics identified. This intermediate
output is then used in to construct a network of countries based on information
theoretical metrics where the links capture similar vectorial patterns in the
topic distributions. Topology of the networks is then analyzed through network
properties like density, path length and clustering. Finally, we identify
specific topological features of our networks using the map equation framework
to detect communities in our networks of countries.
Method and apparatus for automatic text input insertion in digital devices with a restricted number of keys
Authors: Nikolaos Tselios, Manolis Maragoudakis
Date: 2017-07-29 00:00:00

A device which contains number of symbol input keys, where the number of
available keys is less than the number of symbols of an alphabet of any given
language, screen, and dynamic reordering table of the symbols which are mapped
onto those keys, according to a disambiguation method based on the previously
entered symbols. The device incorporates a previously entered keystrokes
tracking mechanism, and the key selected by the user detector, as well as a
mechanism to select the dynamic symbol reordering mapped onto this key
according to the information contained to the reordering table. The reordering
table occurs from a disambiguation method which reorders the symbol appearance.
The reordering information occurs from Bayesian Belief network construction and
training from text corpora of the specific language.
Phase Diagram of Restricted Boltzmann Machines and Generalised Hopfield Networks with Arbitrary Priors
Authors: Adriano Barra, Giuseppe Genovese, Peter Sollich, Daniele Tantari
Date: 2017-02-20 00:00:00
Tasks: Retrieval

Restricted Boltzmann Machines are described by the Gibbs measure of a
bipartite spin glass, which in turn corresponds to the one of a generalised
Hopfield network. This equivalence allows us to characterise the state of these
systems in terms of retrieval capabilities, both at low and high load. We study
the paramagnetic-spin glass and the spin glass-retrieval phase transitions, as
the pattern (i.e. weight) distribution and spin (i.e. unit) priors vary
smoothly from Gaussian real variables to Boolean discrete variables. Our
analysis shows that the presence of a retrieval phase is robust and not
peculiar to the standard Hopfield model with Boolean patterns. The retrieval
region is larger when the pattern entries and retrieval units get more peaked
and, conversely, when the hidden units acquire a broader prior and therefore
have a stronger response to high fields. Moreover, at low load retrieval always
exists below some critical temperature, for every pattern distribution ranging
from the Boolean to the Gaussian case.
Weakly-supervised learning of visual relations
Authors: Julia Peyre, Ivan Laptev, Cordelia Schmid, Josef Sivic
Date: 2017-07-29 00:00:00
Proceeding: ICCV 2017 10
Tasks: Clustering, Relation, Retrieval, Triplet, Weakly-supervised Learning, Zero-Shot Learning

This paper introduces a novel approach for modeling visual relations between
pairs of objects. We call relation a triplet of the form (subject, predicate,
object) where the predicate is typically a preposition (eg. 'under', 'in front
of') or a verb ('hold', 'ride') that links a pair of objects (subject, object).
Learning such relations is challenging as the objects have different spatial
configurations and appearances depending on the relation in which they occur.
Another major challenge comes from the difficulty to get annotations,
especially at box-level, for all possible triplets, which makes both learning
and evaluation difficult. The contributions of this paper are threefold. First,
we design strong yet flexible visual features that encode the appearance and
spatial configuration for pairs of objects. Second, we propose a
weakly-supervised discriminative clustering model to learn relations from
image-level labels only. Third we introduce a new challenging dataset of
unusual relations (UnRel) together with an exhaustive annotation, that enables
accurate evaluation of visual relation retrieval. We show experimentally that
our model results in state-of-the-art results on the visual relationship
dataset significantly improving performance on previously unseen relations
(zero-shot learning), and confirm this observation on our newly introduced
UnRel dataset.
All that is English may be Hindi: Enhancing language identification through automatic ranking of likeliness of word borrowing in social media
Authors: Jasabanta Patro, Bidisha Samanta, Saurabh Singh, Abhipsa Basu, Prithwish Mukherjee, Monojit Choudhury, Animesh Mukherjee
Date: 2017-07-25 00:00:00
Tasks: All, Language Identification, TAG

In this paper, we present a set of computational methods to identify the
likeliness of a word being borrowed, based on the signals from social media. In
terms of Spearman correlation coefficient values, our methods perform more than
two times better (nearly 0.62) in predicting the borrowing likeliness compared
to the best performing baseline (nearly 0.26) reported in literature. Based on
this likeliness estimate we asked annotators to re-annotate the language tags
of foreign words in predominantly native contexts. In 88 percent of cases the
annotators felt that the foreign language tag should be replaced by native
language tag, thus indicating a huge scope for improvement of automatic
language identification systems.
Recursive Diffeomorphism-Based Regression for Shape Functions
Authors: Jieren Xu, Haizhao Yang, Ingrid Daubechies
Date: 2016-10-12 00:00:00
Tasks: regression

This paper proposes a recursive diffeomorphism based regression method for
one-dimensional generalized mode decomposition problem that aims at extracting
generalized modes $\alpha_k(t)s_k(2\pi N_k\phi_k(t))$ from their superposition
$\sum_{k=1}^K \alpha_k(t)s_k(2\pi N_k\phi_k(t))$. First, a one-dimensional
synchrosqueezed transform is applied to estimate instantaneous information,
e.g., $\alpha_k(t)$ and $N_k\phi_k(t)$. Second, a novel approach based on
diffeomorphisms and nonparametric regression is proposed to estimate wave shape
functions $s_k(t)$. These two methods lead to a framework for the generalized
mode decomposition problem under a weak well-separation condition. Numerical
examples of synthetic and real data are provided to demonstrate the fruitful
applications of these methods.
SSGAN: Secure Steganography Based on Generative Adversarial Networks
Authors: Haichao Shi, Jing Dong, Wei Wang, Yinlong Qian, Xiao-Yu Zhang
Date: 2017-07-06 00:00:00
Tasks: Steganalysis

In this paper, a novel strategy of Secure Steganograpy based on Generative
Adversarial Networks is proposed to generate suitable and secure covers for
steganography. The proposed architecture has one generative network, and two
discriminative networks. The generative network mainly evaluates the visual
quality of the generated images for steganography, and the discriminative
networks are utilized to assess their suitableness for information hiding.
Different from the existing work which adopts Deep Convolutional Generative
Adversarial Networks, we utilize another form of generative adversarial
networks. By using this new form of generative adversarial networks,
significant improvements are made on the convergence speed, the training
stability and the image quality. Furthermore, a sophisticated steganalysis
network is reconstructed for the discriminative network, and the network can
better evaluate the performance of the generated images. Numerous experiments
are conducted on the publicly available datasets to demonstrate the
effectiveness and robustness of the proposed method.
Preference Reasoning in Matching Procedures: Application to the Admission Post-Baccalaureat Platform
Authors: Youssef Hamadi, Souhila Kaci
Date: 2017-07-23 00:00:00

Because preferences naturally arise and play an important role in many
real-life decisions, they are at the backbone of various fields. In particular
preferences are increasingly used in almost all matching procedures-based
applications. In this work we highlight the benefit of using AI insights on
preferences in a large scale application, namely the French Admission
Post-Baccalaureat Platform (APB). Each year APB allocates hundreds of thousands
first year applicants to universities. This is done automatically by matching
applicants preferences to university seats. In practice, APB can be unable to
distinguish between applicants which leads to the introduction of random
selection. This has created frustration in the French public since randomness,
even used as a last mean does not fare well with the republican egalitarian
principle. In this work, we provide a solution to this problem. We take
advantage of recent AI Preferences Theory results to show how to enhance APB in
order to improve expressiveness of applicants preferences and reduce their
exposure to random decisions.