pwc-archive/papers-with-abstracts huggingface dataset:
Reasoning in Infinitely Valued G-IALCQ
Authors: Stefan Borgwardt, Rafael Peñaloza
Date: 2015-09-29 00:00:00
Tasks: Negation

Fuzzy Description Logics (FDLs) are logic-based formalisms used to represent
and reason with vague or imprecise knowledge. It has been recently shown that
reasoning in most FDLs using truth values from the interval [0,1] becomes
undecidable in the presence of a negation constructor and general concept
inclusion axioms. One exception to this negative result are FDLs whose
semantics is based on the infinitely valued G\"odel t-norm (G). In this paper,
we extend previous decidability results for G-IALC to deal also with qualified
number restrictions. Our novel approach is based on a combination of the known
crispification technique for finitely valued FDLs and the automata-based
procedure originally developed for reasoning in G-IALC. The proposed approach
combines the advantages of these two methods, while removing their respective
drawbacks.
Representation Benefits of Deep Feedforward Networks
Authors: Matus Telgarsky
Date: 2015-09-27 00:00:00
Tasks: 2k, General Classification

This note provides a family of classification problems, indexed by a positive
integer $k$, where all shallow networks with fewer than exponentially (in $k$)
many nodes exhibit error at least $1/6$, whereas a deep network with 2 nodes in
each of $2k$ layers achieves zero error, as does a recurrent network with 3
distinct nodes iterated $k$ times. The proof is elementary, and the networks
are standard feedforward networks with ReLU (Rectified Linear Unit)
nonlinearities.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
Authors: Shakir Mohamed, Danilo Jimenez Rezende
Date: 2015-09-29 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: BIG-bench Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Variational Inference

The mutual information is a core statistical quantity that has applications
in all areas of machine learning, whether this is in training of density models
over multiple data modalities, in maximising the efficiency of noisy
transmission channels, or when learning behaviour policies for exploration by
artificial agents. Most learning algorithms that involve optimisation of the
mutual information rely on the Blahut-Arimoto algorithm --- an enumerative
algorithm with exponential complexity that is not suitable for modern machine
learning applications. This paper provides a new approach for scalable
optimisation of the mutual information by merging techniques from variational
inference and deep learning. We develop our approach by focusing on the problem
of intrinsically-motivated learning, where the mutual information forms the
definition of a well-known internal drive known as empowerment. Using a
variational lower bound on the mutual information, combined with convolutional
networks for handling visual input streams, we develop a stochastic
optimisation algorithm that allows for scalable information maximisation and
empowerment-based reasoning directly from pixels to actions.
Towards Unveiling the Ontology Key Features Altering Reasoner Performances
Authors: Nourhène Alaya, Sadok Ben Yahia, Myriam Lamolle
Date: 2015-09-29 00:00:00

Reasoning with ontologies is one of the core fields of research in
Description Logics. A variety of efficient reasoner with highly optimized
algorithms have been developed to allow inference tasks on expressive ontology
languages such as OWL(DL). However, reasoner reported computing times have
exceeded and sometimes fall behind the expected theoretical values. From an
empirical perspective, it is not yet well understood, which particular aspects
in the ontology are reasoner performance degrading factors. In this paper, we
conducted an investigation about state of art works that attempted to portray
potential correlation between reasoner empirical behaviour and particular
ontological features. These works were analysed and then broken down into
categories. Further, we proposed a set of ontology features covering a broad
range of structural and syntactic ontology characteristics. We claim that these
features are good indicators of the ontology hardness level against reasoning
tasks.
Retinex filtering of foggy images: generation of a bulk set with selection and ranking
Authors: Roberto Marazzato, Amelia Carolina Sparavigna
Date: 2015-09-29 00:00:00
Tasks: Image Manipulation

In this paper we are proposing the use of GIMP Retinex, a filter of the GNU
Image Manipulation Program, for enhancing foggy images. This filter involves
adjusting four different parameters to find the output image which has to be
preferred according to some specific purposes. Aiming to obtain a processing,
which is able of choosing automatically the best image from a given set, we are
proposing a method for the generation a bulk set of GIMP Retinex filtered
images and a preliminary approach for selecting and ranking them.
DCTNet : A Simple Learning-free Approach for Face Recognition
Authors: Cong Jie Ng, Andrew Beng Jin Teoh
Date: 2015-07-08 00:00:00
Tasks: Binarization, Face Recognition, image-classification, Image Classification

PCANet was proposed as a lightweight deep learning network that mainly
leverages Principal Component Analysis (PCA) to learn multistage filter banks
followed by binarization and block-wise histograming. PCANet was shown worked
surprisingly well in various image classification tasks. However, PCANet is
data-dependence hence inflexible. In this paper, we proposed a
data-independence network, dubbed DCTNet for face recognition in which we adopt
Discrete Cosine Transform (DCT) as filter banks in place of PCA. This is
motivated by the fact that 2D DCT basis is indeed a good approximation for high
ranked eigenvectors of PCA. Both 2D DCT and PCA resemble a kind of modulated
sine-wave patterns, which can be perceived as a bandpass filter bank. DCTNet is
free from learning as 2D DCT bases can be computed in advance. Besides that, we
also proposed an effective method to regulate the block-wise histogram feature
vector of DCTNet for robustness. It is shown to provide surprising performance
boost when the probe image is considerably different in appearance from the
gallery image. We evaluate the performance of DCTNet extensively on a number of
benchmark face databases and being able to achieve on par with or often better
accuracy performance than PCANet.
Adaptive-treed bandits
Authors: Adam D. Bull
Date: 2013-02-11 00:00:00

We describe a novel algorithm for noisy global optimisation and
continuum-armed bandits, with good convergence properties over any continuous
reward function having finitely many polynomial maxima. Over such functions,
our algorithm achieves square-root regret in bandits, and inverse-square-root
error in optimisation, without prior information. Our algorithm works by
reducing these problems to tree-armed bandits, and we also provide new results
in this setting. We show it is possible to adaptively combine multiple trees so
as to minimise the regret, and also give near-matching lower bounds on the
regret in terms of the zooming dimension.
Censoring Diffusion for Harvesting WSNs
Authors: Jesus Fernandez-Bes, Rocío Arroyo-Valles, Jerónimo Arenas-García, Jesús Cid-Sueiro
Date: 2015-09-29 00:00:00

In this paper, we analyze energy-harvesting adaptive diffusion networks for a
distributed estimation problem. In order to wisely manage the available energy
resources, we propose a scheme where a censoring algorithm is jointly applied
over the diffusion strategy. An energy-aware variation of a diffusion algorithm
is used, and a new way of measuring the relevance of the estimates in diffusion
networks is proposed in order to apply a subsequent censoring mechanism.
Simulation results show the potential benefit of integrating censoring schemes
in energy-constrained diffusion networks.
Long-Range Trajectories from Global and Local Motion Representations
Authors: Eduardo M. Pereira, Jaime S. Cardoso, Ricardo Morla
Date: 2015-09-29 00:00:00
Tasks: Action Recognition, Motion Segmentation, Temporal Action Localization

Motion is a fundamental cue for scene analysis and human activity understan-
ding in videos. It can be encoded in trajectories for tracking objects and for
action recognition, or in form of flow to address behaviour analysis in crowded
scenes. Each approach can only be applied on limited scenarios. We propose a
motion-based system that represents the spatial and temporal features of the
flow in terms of long-range trajectories. The novelty resides on the system
formulation, its generic approach to handle scene variability and motion
variations, motion integration from local and global representations, and the
resulting long-range trajectories that overcome trajectory-based approach
problems. We report the results and conclusions that state its pertinence on
different scenarios, comparing and correlating the extracted trajectories of
individual pedestrians, manually annotated. We also propose an evaluation
framework and stress the diverse system characteristics that can be used for
human activity tasks, namely on motion segmentation.
Neural-based machine translation for medical text domain. Based on European Medicines Agency leaflet texts
Authors: Krzysztof Wołk, Krzysztof Marasek
Date: 2015-09-29 00:00:00
Tasks: Decoder, Machine Translation, Sentence, Translation

The quality of machine translation is rapidly evolving. Today one can find
several machine translation systems on the web that provide reasonable
translations, although the systems are not perfect. In some specific domains,
the quality may decrease. A recently proposed approach to this domain is neural
machine translation. It aims at building a jointly-tuned single neural network
that maximizes translation performance, a very different approach from
traditional statistical machine translation. Recently proposed neural machine
translation models often belong to the encoder-decoder family in which a source
sentence is encoded into a fixed length vector that is, in turn, decoded to
generate a translation. The present research examines the effects of different
training methods on a Polish-English Machine Translation system used for
medical data. The European Medicines Agency parallel text corpus was used as
the basis for training of neural and statistical network-based translation
systems. The main machine translation evaluation metrics have also been used in
analysis of the systems. A comparison and implementation of a real-time medical
translator is the main focus of our experiments.
Tuned and GPU-accelerated parallel data mining from comparable corpora
Authors: Krzysztof Wołk, Krzysztof Marasek
Date: 2015-09-29 00:00:00
Tasks: GPU, Translation

The multilingual nature of the world makes translation a crucial requirement
today. Parallel dictionaries constructed by humans are a widely-available
resource, but they are limited and do not provide enough coverage for good
quality translation purposes, due to out-of-vocabulary words and neologisms.
This motivates the use of statistical translation systems, which are
unfortunately dependent on the quantity and quality of training data. Such has
a very limited availability especially for some languages and very narrow text
domains. Is this research we present our improvements to Yalign mining
methodology by reimplementing the comparison algorithm, introducing a tuning
scripts and by improving performance using GPU computing acceleration. The
experiments are conducted on various text domains and bi-data is extracted from
the Wikipedia dumps.
Learning dynamic Boltzmann machines with spike-timing dependent plasticity
Authors: Takayuki Osogami, Makoto Otsuka
Date: 2015-09-29 00:00:00
Tasks: Time Series, Time Series Analysis

We propose a particularly structured Boltzmann machine, which we refer to as
a dynamic Boltzmann machine (DyBM), as a stochastic model of a
multi-dimensional time-series. The DyBM can have infinitely many layers of
units but allows exact and efficient inference and learning when its parameters
have a proposed structure. This proposed structure is motivated by postulates
and observations, from biological neural networks, that the synaptic weight is
strengthened or weakened, depending on the timing of spikes (i.e., spike-timing
dependent plasticity or STDP). We show that the learning rule of updating the
parameters of the DyBM in the direction of maximizing the likelihood of given
time-series can be interpreted as STDP with long term potentiation and long
term depression. The learning rule has a guarantee of convergence and can be
performed in a distributed matter (i.e., local in space) with limited memory
(i.e., local in time).
Semantics, Representations and Grammars for Deep Learning
Authors: David Balduzzi
Date: 2015-09-29 00:00:00
Tasks: Deep Learning, Distributed Optimization

Deep learning is currently the subject of intensive study. However,
fundamental concepts such as representations are not formally defined --
researchers "know them when they see them" -- and there is no common language
for describing and analyzing algorithms. This essay proposes an abstract
framework that identifies the essential features of current practice and may
provide a foundation for future developments.
  The backbone of almost all deep learning algorithms is backpropagation, which
is simply a gradient computation distributed over a neural network. The main
ingredients of the framework are thus, unsurprisingly: (i) game theory, to
formalize distributed optimization; and (ii) communication protocols, to track
the flow of zeroth and first-order information. The framework allows natural
definitions of semantics (as the meaning encoded in functions), representations
(as functions whose semantics is chosen to optimized a criterion) and grammars
(as communication protocols equipped with first-order convergence guarantees).
  Much of the essay is spent discussing examples taken from the literature. The
ultimate aim is to develop a graphical language for describing the structure of
deep learning algorithms that backgrounds the details of the optimization
procedure and foregrounds how the components interact. Inspiration is taken
from probabilistic graphical models and factor graphs, which capture the
essential structural features of multivariate distributions.
Tractable Fully Bayesian Inference via Convex Optimization and Optimal Transport Theory
Authors: Sanggyun Kim, Diego Mesa, Rui Ma, Todd P. Coleman
Date: 2015-09-29 00:00:00
Tasks: Bayesian Inference, Sleep Staging

We consider the problem of transforming samples from one continuous source
distribution into samples from another target distribution. We demonstrate with
optimal transport theory that when the source distribution can be easily
sampled from and the target distribution is log-concave, this can be tractably
solved with convex optimization. We show that a special case of this, when the
source is the prior and the target is the posterior, is Bayesian inference.
Here, we can tractably calculate the normalization constant and draw posterior
i.i.d. samples. Remarkably, our Bayesian tractability criterion is simply log
concavity of the prior and likelihood: the same criterion for tractable
calculation of the maximum a posteriori point estimate. With simulated data, we
demonstrate how we can attain the Bayes risk in simulations. With physiologic
data, we demonstrate improvements over point estimation in intensive care unit
outcome prediction and electroencephalography-based sleep staging.
Recurrent Network Models for Human Dynamics
Authors: Katerina Fragkiadaki, Sergey Levine, Panna Felsen, Jitendra Malik
Date: 2015-08-02 00:00:00
Proceeding: ICCV 2015 12
Tasks: Decoder, Human Dynamics, Human Pose Forecasting, Optical Flow Estimation, Representation Learning

We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and
prediction of human body pose in videos and motion capture. The ERD model is a
recurrent neural network that incorporates nonlinear encoder and decoder
networks before and after recurrent layers. We test instantiations of ERD
architectures in the tasks of motion capture (mocap) generation, body pose
labeling and body pose forecasting in videos. Our model handles mocap training
data across multiple subjects and activity domains, and synthesizes novel
motions while avoid drifting for long periods of time. For human pose labeling,
ERD outperforms a per frame body part detector by resolving left-right body
part confusions. For video pose forecasting, ERD predicts body joint
displacements across a temporal horizon of 400ms and outperforms a first order
motion model based on optical flow. ERDs extend previous Long Short Term Memory
(LSTM) models in the literature to jointly learn representations and their
dynamics. Our experiments show such representation learning is crucial for both
labeling and prediction in space-time. We find this is a distinguishing feature
between the spatio-temporal visual domain in comparison to 1D text, speech or
handwriting, where straightforward hard coded representations have shown
excellent results when directly combined with recurrent units.
Parallel Stochastic Gradient Markov Chain Monte Carlo for Matrix Factorisation Models
Authors: Umut Şimşekli, Hazal Koptagel, Hakan Güldaş, A. Taylan Cemgil, Figen Öztoprak, Ş. İlker Birbil
Date: 2015-06-03 00:00:00

For large matrix factorisation problems, we develop a distributed Markov
Chain Monte Carlo (MCMC) method based on stochastic gradient Langevin dynamics
(SGLD) that we call Parallel SGLD (PSGLD). PSGLD has very favourable scaling
properties with increasing data size and is comparable in terms of
computational requirements to optimisation methods based on stochastic gradient
descent. PSGLD achieves high performance by exploiting the conditional
independence structure of the MF models to sub-sample data in a systematic
manner as to allow parallelisation and distributed computation. We provide a
convergence proof of the algorithm and verify its superior performance on
various architectures such as Graphics Processing Units, shared memory
multi-core systems and multi-computer clusters.
Efficient Empowerment
Authors: Maximilian Karl, Justin Bayer, Patrick van der Smagt
Date: 2015-09-28 00:00:00
Tasks: Reinforcement Learning

Empowerment quantifies the influence an agent has on its environment. This is
formally achieved by the maximum of the expected KL-divergence between the
distribution of the successor state conditioned on a specific action and a
distribution where the actions are marginalised out. This is a natural
candidate for an intrinsic reward signal in the context of reinforcement
learning: the agent will place itself in a situation where its action have
maximum stability and maximum influence on the future. The limiting factor so
far has been the computational complexity of the method: the only way of
calculation has so far been a brute force algorithm, reducing the applicability
of the method to environments with a small set discrete states. In this work,
we propose to use an efficient approximation for marginalising out the actions
in the case of continuous environments. This allows fast evaluation of
empowerment, paving the way towards challenging environments such as real world
robotics. The method is presented on a pendulum swing up problem.
Hyper-Fisher Vectors for Action Recognition
Authors: Sanath Narayan, Kalpathi R. Ramakrishnan
Date: 2015-09-28 00:00:00
Tasks: Action Recognition, Temporal Action Localization

In this paper, a novel encoding scheme combining Fisher vector and
bag-of-words encodings has been proposed for recognizing action in videos. The
proposed Hyper-Fisher vector encoding is sum of local Fisher vectors which are
computed based on the traditional Bag-of-Words (BoW) encoding. Thus, the
proposed encoding is simple and yet an effective representation over the
traditional Fisher Vector encoding. By extensive evaluation on challenging
action recognition datasets, viz., Youtube, Olympic Sports, UCF50 and HMDB51,
we show that the proposed Hyper-Fisher Vector encoding improves the recognition
performance by around 2-3% compared to the improved Fisher Vector encoding. We
also perform experiments to show that the performance of the Hyper-Fisher
Vector is robust to the dictionary size of the BoW encoding.
Ensemble UCT Needs High Exploitation
Authors: S. Ali Mirsoleimani, Aske Plaat, Jaap van den Herik
Date: 2015-09-28 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Recent results have shown that the MCTS algorithm (a new, adaptive,
randomized optimization algorithm) is effective in a remarkably diverse set of
applications in Artificial Intelligence, Operations Research, and High Energy
Physics. MCTS can find good solutions without domain dependent heuristics,
using the UCT formula to balance exploitation and exploration. It has been
suggested that the optimum in the exploitation- exploration balance differs for
different search tree sizes: small search trees needs more exploitation; large
search trees need more exploration. Small search trees occur in variations of
MCTS, such as parallel and ensemble approaches. This paper investigates the
possibility of improving the performance of Ensemble UCT by increasing the
level of exploitation. As the search trees becomes smaller we achieve an
improved performance. The results are important for improving the performance
of large scale parallelism of MCTS.
Pose Induction for Novel Object Categories
Authors: Shubham Tulsiani, João Carreira, Jitendra Malik
Date: 2015-05-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object

We address the task of predicting pose for objects of unannotated object
categories from a small seed set of annotated object classes. We present a
generalized classifier that can reliably induce pose given a single instance of
a novel category. In case of availability of a large collection of novel
instances, our approach then jointly reasons over all instances to improve the
initial estimates. We empirically validate the various components of our
algorithm and quantitatively show that our method produces reliable pose
estimates. We also show qualitative results on a diverse set of classes and
further demonstrate the applicability of our system for learning shape models
of novel object classes.
A Review of Relational Machine Learning for Knowledge Graphs
Authors: Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich
Date: 2015-03-02 00:00:00
Tasks: BIG-bench Machine Learning, Knowledge Graphs

Relational machine learning studies methods for the statistical analysis of
relational, or graph-structured, data. In this paper, we provide a review of
how such statistical models can be "trained" on large knowledge graphs, and
then used to predict new facts about the world (which is equivalent to
predicting new edges in the graph). In particular, we discuss two fundamentally
different kinds of statistical relational models, both of which can scale to
massive datasets. The first is based on latent feature models such as tensor
factorization and multiway neural networks. The second is based on mining
observable patterns in the graph. We also show how to combine these latent and
observable models to get improved modeling power at decreased computational
cost. Finally, we discuss how such statistical models of graphs can be combined
with text-based information extraction methods for automatically constructing
knowledge graphs from the Web. To this end, we also discuss Google's Knowledge
Vault project as an example of such combination.