pwc-archive/papers-with-abstracts huggingface dataset:
Co-Clustering Network-Constrained Trajectory Data
Authors: Mohamed Khalil El Mahrsi, Romain Guigourès, Fabrice Rossi, Marc Boullé
Date: 2015-11-04 00:00:00
Tasks: Clustering

Recently, clustering moving object trajectories kept gaining interest from
both the data mining and machine learning communities. This problem, however,
was studied mainly and extensively in the setting where moving objects can move
freely on the euclidean space. In this paper, we study the problem of
clustering trajectories of vehicles whose movement is restricted by the
underlying road network. We model relations between these trajectories and road
segments as a bipartite graph and we try to cluster its vertices. We
demonstrate our approaches on synthetic data and show how it could be useful in
inferring knowledge about the flow dynamics and the behavior of the drivers
using the road network.
Study of a bias in the offline evaluation of a recommendation algorithm
Authors: Arnaud De Myttenaere, Boris Golden, Bénédicte Le Grand, Fabrice Rossi
Date: 2015-11-04 00:00:00
Tasks: Recommendation Systems

Recommendation systems have been integrated into the majority of large online
systems to filter and rank information according to user profiles. It thus
influences the way users interact with the system and, as a consequence, bias
the evaluation of the performance of a recommendation algorithm computed using
historical data (via offline evaluation). This paper describes this bias and
discuss the relevance of a weighted offline evaluation to reduce this bias for
different classes of recommendation algorithms.
Face Aging Effect Simulation using Hidden Factor Analysis Joint Sparse Representation
Authors: Hongyu Yang, Di Huang, Yunhong Wang, Heng Wang, Yuanyan Tang
Date: 2015-11-04 00:00:00

Face aging simulation has received rising investigations nowadays, whereas it
still remains a challenge to generate convincing and natural age-progressed
face images. In this paper, we present a novel approach to such an issue by
using hidden factor analysis joint sparse representation. In contrast to the
majority of tasks in the literature that handle the facial texture integrally,
the proposed aging approach separately models the person-specific facial
properties that tend to be stable in a relatively long period and the
age-specific clues that change gradually over time. It then merely transforms
the age component to a target age group via sparse reconstruction, yielding
aging effects, which is finally combined with the identity component to achieve
the aged face. Experiments are carried out on three aging databases, and the
results achieved clearly demonstrate the effectiveness and robustness of the
proposed method in rendering a face with aging effects. Additionally, a series
of evaluations prove its validity with respect to identity preservation and
aging effect generation.
Global convergence of splitting methods for nonconvex composite optimization
Authors: Guoyin Li, Ting Kei Pong
Date: 2014-07-03 00:00:00

We consider the problem of minimizing the sum of a smooth function $h$ with a
bounded Hessian, and a nonsmooth function. We assume that the latter function
is a composition of a proper closed function $P$ and a surjective linear map
$\cal M$, with the proximal mappings of $\tau P$, $\tau > 0$, simple to
compute. This problem is nonconvex in general and encompasses many important
applications in engineering and machine learning. In this paper, we examined
two types of splitting methods for solving this nonconvex optimization problem:
alternating direction method of multipliers and proximal gradient algorithm.
For the direct adaptation of the alternating direction method of multipliers,
we show that, if the penalty parameter is chosen sufficiently large and the
sequence generated has a cluster point, then it gives a stationary point of the
nonconvex problem. We also establish convergence of the whole sequence under an
additional assumption that the functions $h$ and $P$ are semi-algebraic.
Furthermore, we give simple sufficient conditions to guarantee boundedness of
the sequence generated. These conditions can be satisfied for a wide range of
applications including the least squares problem with the $\ell_{1/2}$
regularization. Finally, when $\cal M$ is the identity so that the proximal
gradient algorithm can be efficiently applied, we show that any cluster point
is stationary under a slightly more flexible constant step-size rule than what
is known in the literature for a nonconvex $h$.
Cell identification in whole-brain multiview images of neural activation
Authors: Marco Paciscopi, Ludovico Silvestri, Francesco Saverio Pavone, Paolo Frasconi
Date: 2015-11-04 00:00:00
Tasks: ARC

We present a scalable method for brain cell identification in multiview
confocal light sheet microscopy images. Our algorithmic pipeline includes a
hierarchical registration approach and a novel multiview version of semantic
deconvolution that simultaneously enhance visibility of fluorescent cell
bodies, equalize their contrast, and fuses adjacent views into a single 3D
images on which cell identification is performed with mean shift.
  We present empirical results on a whole-brain image of an adult Arc-dVenus
mouse acquired at 4micron resolution. Based on an annotated test volume
containing 3278 cells, our algorithm achieves an $F_1$ measure of 0.89.
Robust Large-Scale Localization in 3D Point Clouds Revisited
Authors: Fabian Tschopp, Marco Zorzi
Date: 2015-11-03 00:00:00
Tasks: Pose Estimation

We tackle the problem of getting a full 6-DOF pose estimation of a query
image inside a given point cloud. This technical report re-evaluates the
algorithms proposed by Y. Li et al. "Worldwide Pose Estimation using 3D Point
Cloud". Our code computes poses from 3 or 4 points, with both known and unknown
focal length. The results can easily be displayed and analyzed with Meshlab. We
found both advantages and shortcomings of the methods proposed. Furthermore,
additional priors and parameters for point selection, RANSAC and pose quality
estimate (inlier test) are proposed and applied.
Robust Registration of Calcium Images by Learned Contrast Synthesis
Authors: John A. Bogovic, Philipp Hanslovsky, Allan Wong, Stephan Saalfeld
Date: 2015-11-03 00:00:00
Tasks: BIG-bench Machine Learning, Image Registration

Multi-modal image registration is a challenging task that is vital to fuse
complementary signals for subsequent analyses. Despite much research into cost
functions addressing this challenge, there exist cases in which these are
ineffective. In this work, we show that (1) this is true for the registration
of in-vivo Drosophila brain volumes visualizing genetically encoded calcium
indicators to an nc82 atlas and (2) that machine learning based contrast
synthesis can yield improvements. More specifically, the number of subjects for
which the registration outright failed was greatly reduced (from 40% to 15%) by
using a synthesized image.
Neuroevolution in Games: State of the Art and Open Challenges
Authors: Sebastian Risi, Julian Togelius
Date: 2014-10-27 00:00:00
Tasks: Evolutionary Algorithms

This paper surveys research on applying neuroevolution (NE) to games. In
neuroevolution, artificial neural networks are trained through evolutionary
algorithms, taking inspiration from the way biological brains evolved. We
analyse the application of NE in games along five different axes, which are the
role NE is chosen to play in a game, the different types of neural networks
used, the way these networks are evolved, how the fitness is determined and
what type of input the network receives. The article also highlights important
open research challenges in the field.
There is no fast lunch: an examination of the running speed of evolutionary algorithms in several languages
Authors: Juan-J. Merelo, Pablo García-Sánchez, Mario García-Valdez, Israel Blancas
Date: 2015-11-03 00:00:00
Tasks: Evolutionary Algorithms

It is quite usual when an evolutionary algorithm tool or library uses a
language other than C, C++, Java or Matlab that a reviewer or the audience
questions its usefulness based on the speed of those other languages,
purportedly slower than the aforementioned ones. Despite speed being not
everything needed to design a useful evolutionary algorithm application, in
this paper we will measure the speed for several very basic evolutionary
algorithm operations in several languages which use different virtual machines
and approaches, and prove that, in fact, there is no big difference in speed
between interpreted and compiled languages, and that in some cases, interpreted
languages such as JavaScript or Python can be faster than compiled languages
such as Scala, making them worthy of use for evolutionary algorithm
experimentation.
Understanding symmetries in deep networks
Authors: Vijay Badrinarayanan, Bamdev Mishra, Roberto Cipolla
Date: 2015-11-03 00:00:00
Tasks: Computational Efficiency

Recent works have highlighted scale invariance or symmetry present in the
weight space of a typical deep network and the adverse effect it has on the
Euclidean gradient based stochastic gradient descent optimization. In this
work, we show that a commonly used deep network, which uses convolution, batch
normalization, reLU, max-pooling, and sub-sampling pipeline, possess more
complex forms of symmetry arising from scaling-based reparameterization of the
network weights. We propose to tackle the issue of the weight space symmetry by
constraining the filters to lie on the unit-norm manifold. Consequently,
training the network boils down to using stochastic gradient descent updates on
the unit-norm manifold. Our empirical evidence based on the MNIST dataset shows
that the proposed updates improve the test performance beyond what is achieved
with batch normalization and without sacrificing the computational efficiency
of the weight updates.
A Survey of Online Experiment Design with the Stochastic Multi-Armed Bandit
Authors: Giuseppe Burtini, Jason Loeppky, Ramon Lawrence
Date: 2015-10-02 00:00:00
Tasks: Decision Making, Multi-Armed Bandits

Adaptive and sequential experiment design is a well-studied area in numerous
domains. We survey and synthesize the work of the online statistical learning
paradigm referred to as multi-armed bandits integrating the existing research
as a resource for a certain class of online experiments. We first explore the
traditional stochastic model of a multi-armed bandit, then explore a taxonomic
scheme of complications to that model, for each complication relating it to a
specific requirement or consideration of the experiment design context.
Finally, at the end of the paper, we present a table of known upper-bounds of
regret for all studied algorithms providing both perspectives for future
theoretical work and a decision-making tool for practitioners looking for
theoretical guarantees.
Highway Networks
Authors: Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber
Date: 2015-05-03 00:00:00
Tasks: Language Modelling

There is plenty of theoretical and empirical evidence that depth of neural
networks is a crucial ingredient for their success. However, network training
becomes more difficult with increasing depth and training of very deep networks
remains an open problem. In this extended abstract, we introduce a new
architecture designed to ease gradient-based training of very deep networks. We
refer to networks with this architecture as highway networks, since they allow
unimpeded information flow across several layers on "information highways". The
architecture is characterized by the use of gating units which learn to
regulate the flow of information through a network. Highway networks with
hundreds of layers can be trained directly using stochastic gradient descent
and with a variety of activation functions, opening up the possibility of
studying extremely deep and efficient architectures.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Authors: David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams
Date: 2015-09-30 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Drug Discovery, Graph Regression, Node Classification

We introduce a convolutional neural network that operates directly on graphs.
These networks allow end-to-end learning of prediction pipelines whose inputs
are graphs of arbitrary size and shape. The architecture we present generalizes
standard molecular feature extraction methods based on circular fingerprints.
We show that these data-driven features are more interpretable, and have better
predictive performance on a variety of tasks.
Data Stream Classification using Random Feature Functions and Novel Method Combinations
Authors: Diego Marrón, Jesse Read, Albert Bifet, Nacho Navarro
Date: 2015-11-03 00:00:00
Tasks: General Classification

Big Data streams are being generated in a faster, bigger, and more
commonplace. In this scenario, Hoeffding Trees are an established method for
classification. Several extensions exist, including high-performing ensemble
setups such as online and leveraging bagging. Also, $k$-nearest neighbors is a
popular choice, with most extensions dealing with the inherent performance
limitations over a potentially-infinite stream.
  At the same time, gradient descent methods are becoming increasingly popular,
owing in part to the successes of deep learning. Although deep neural networks
can learn incrementally, they have so far proved too sensitive to
hyper-parameter options and initial conditions to be considered an effective
`off-the-shelf' data-streams solution.
  In this work, we look at combinations of Hoeffding-trees, nearest neighbour,
and gradient descent methods with a streaming preprocessing approach in the
form of a random feature functions filter for additional predictive power.
  We further extend the investigation to implementing methods on GPUs, which we
test on some large real-world datasets, and show the benefits of using GPUs for
data-stream learning due to their high scalability.
  Our empirical evaluation yields positive results for the novel approaches
that we experiment with, highlighting important issues, and shed light on
promising future directions in approaches to data-stream classification.
Face Detection with a 3D Model
Authors: Adrian Barbu, Nathan Lay, Gary Gramajo
Date: 2014-04-14 00:00:00
Tasks: Face Detection, model, regression

This paper presents a part-based face detection approach where the spatial relationship between the face parts is represented by a hidden 3D model with six parameters. The computational complexity of the search in the six dimensional pose space is addressed by proposing meaningful 3D pose candidates by image-based regression from detected face keypoint locations. The 3D pose candidates are evaluated using a parameter sensitive classifier based on difference features relative to the 3D pose. A compatible subset of candidates is then obtained by non-maximal suppression. Experiments on two standard face detection datasets show that the proposed 3D model based approach obtains results comparable to or better than state of the art.
Quantifying the Cognitive Extent of Science
Authors: Staša Milojević
Date: 2015-10-30 00:00:00
Tasks: Articles, Astronomy

While the modern science is characterized by an exponential growth in
scientific literature, the increase in publication volume clearly does not
reflect the expansion of the cognitive boundaries of science. Nevertheless,
most of the metrics for assessing the vitality of science or for making funding
and policy decisions are based on productivity. Similarly, the increasing level
of knowledge production by large science teams, whose results often enjoy
greater visibility, does not necessarily mean that "big science" leads to
cognitive expansion. Here we present a novel, big-data method to quantify the
extents of cognitive domains of different bodies of scientific literature
independently from publication volume, and apply it to 20 million articles
published over 60-130 years in physics, astronomy, and biomedicine. The method
is based on the lexical diversity of titles of fixed quotas of research
articles. Owing to large size of quotas, the method overcomes the inherent
stochasticity of article titles to achieve <1% precision. We show that the
periods of cognitive growth do not necessarily coincide with the trends in
publication volume. Furthermore, we show that the articles produced by larger
teams cover significantly smaller cognitive territory than (the same quota of)
articles from smaller teams. Our findings provide a new perspective on the role
of small teams and individual researchers in expanding the cognitive boundaries
of science. The proposed method of quantifying the extent of the cognitive
territory can also be applied to study many other aspects of "science of
science."
Visualising interactive inferences with IDPD3
Authors: Ruben Lapauw, Ingmar Dasseville, Marc Denecker
Date: 2015-11-03 00:00:00

A large part of the use of knowledge base systems is the interpretation of
the output by the end-users and the interaction with these users. Even during
the development process visualisations can be a great help to the developer. We
created IDPD3 as a library to visualise models of logic theories. IDPD3 is a
new version of $ID^{P}_{Draw}$ and adds support for visualised interactive
simulations.
Bound Your Models! How to Make OWL an ASP Modeling Language
Authors: Sarah Alice Gaggl, Sebastian Rudolph, Lukas Schweizer
Date: 2015-11-03 00:00:00
Tasks: Model extraction, Translation

To exploit the Web Ontology Language OWL as an answer set programming (ASP)
language, we introduce the notion of bounded model semantics, as an intuitive
and computationally advantageous alternative to its classical semantics. We
show that a translation into ASP allows for solving a wide range of
bounded-model reasoning tasks, including satisfiability and axiom entailment
but also novel ones such as model extraction and enumeration. Ultimately, our
work facilitates harnessing advanced semantic web modeling environments for the
logic programming community through an "off-label use" of OWL.
A web-based IDE for IDP
Authors: Ingmar Dasseville, Gerda Janssens
Date: 2015-11-03 00:00:00

IDP is a knowledge base system based on first order logic. It is finding its
way to a larger public but is still facing practical challenges. Adoption of
new languages requires a newcomer-friendly way for users to interact with it.
Both an online presence to try to convince potential users to download the
system and offline availability to develop larger applications are essential.
We developed an IDE which can serve both purposes through the use of web
technology. It enables us to provide the user with a modern IDE with relatively
little effort.
Lowering the learning curve for declarative programming: a Python API for the IDP system
Authors: Joost Vennekens
Date: 2015-11-03 00:00:00

Programmers may be hesitant to use declarative systems, because of the
associated learning curve. In this paper, we present an API that integrates the
IDP Knowledge Base system into the Python programming language. IDP is a
state-of-the-art logical system, which uses SAT, SMT, Logic Programming and
Answer Set Programming technology. Python is currently one of the most widely
used (teaching) languages for programming. The first goal of our API is to
allow a Python programmer to use the declarative power of IDP, without needing
to learn any new syntax or semantics. The second goal is allow IDP to be added
to/removed from an existing code base with minimal changes.
SWISH: SWI-Prolog for Sharing
Authors: Jan Wielemaker, Torbjörn Lager, Fabrizio Riguzzi
Date: 2015-11-03 00:00:00

Recently, we see a new type of interfaces for programmers based on web
technology. For example, JSFiddle, IPython Notebook and R-studio. Web
technology enables cloud-based solutions, embedding in tutorial web pages,
atractive rendering of results, web-scale cooperative development, etc. This
article describes SWISH, a web front-end for Prolog. A public website exposes
SWI-Prolog using SWISH, which is used to run small Prolog programs for
demonstration, experimentation and education. We connected SWISH to the
ClioPatria semantic web toolkit, where it allows for collaborative development
of programs and queries related to a dataset as well as performing maintenance
tasks on the running server and we embedded SWISH in the Learn Prolog Now!
online Prolog book.
Learning to Transduce with Unbounded Memory
Authors: Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom
Date: 2015-06-08 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Natural Language Transduction, Translation

Recently, strong results have been demonstrated by Deep Recurrent Neural
Networks on natural language transduction problems. In this paper we explore
the representational power of these models using synthetic grammars designed to
exhibit phenomena similar to those found in real transduction problems such as
machine translation. These experiments lead us to propose new memory-based
recurrent networks that implement continuously differentiable analogues of
traditional data structures such as Stacks, Queues, and DeQues. We show that
these architectures exhibit superior generalisation performance to Deep RNNs
and are often able to learn the underlying generating algorithms in our
transduction experiments.
Properties of the Sample Mean in Graph Spaces and the Majorize-Minimize-Mean Algorithm
Authors: Brijnesh J. Jain
Date: 2015-11-03 00:00:00

One of the most fundamental concepts in statistics is the concept of sample
mean. Properties of the sample mean that are well-defined in Euclidean spaces
become unwieldy or even unclear in graph spaces. Open problems related to the
sample mean of graphs include: non-existence, non-uniqueness, statistical
inconsistency, lack of convergence results of mean algorithms, non-existence of
midpoints, and disparity to midpoints. We present conditions to resolve all six
problems and propose a Majorize-Minimize-Mean (MMM) Algorithm. Experiments on
graph datasets representing images and molecules show that the MMM-Algorithm
best approximates a sample mean of graphs compared to six other mean
algorithms.
Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The Best Input Parameters For R* Algorithm Through Series Of Experiments
Authors: Konstantin Yakovlev, Egor Baskin, Ivan Hramoin
Date: 2015-11-03 00:00:00
Tasks: Heuristic Search

Path planning is typically considered in Artificial Intelligence as a graph
searching problem and R* is state-of-the-art algorithm tailored to solve it.
The algorithm decomposes given path finding task into the series of subtasks
each of which can be easily (in computational sense) solved by well-known
methods (such as A*). Parameterized random choice is used to perform the
decomposition and as a result R* performance largely depends on the choice of
its input parameters. In our work we formulate a range of assumptions
concerning possible upper and lower bounds of R* parameters, their
interdependency and their influence on R* performance. Then we evaluate these
assumptions by running a large number of experiments. As a result we formulate
a set of heuristic rules which can be used to initialize the values of R*
parameters in a way that leads to algorithm's best performance.