pwc-archive/papers-with-abstracts huggingface dataset:
The Secrets of Salient Object Segmentation
Authors: Yin Li, Xiaodi Hou, Christof Koch, James M. Rehg, Alan L. Yuille
Date: 2014-06-11 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object, Segmentation, Semantic Segmentation

In this paper we provide an extensive evaluation of fixation prediction and
salient object segmentation algorithms as well as statistics of major datasets.
Our analysis identifies serious design flaws of existing salient object
benchmarks, called the dataset design bias, by over emphasizing the
stereotypical concepts of saliency. The dataset design bias does not only
create the discomforting disconnection between fixations and salient object
segmentation, but also misleads the algorithm designing. Based on our analysis,
we propose a new high quality dataset that offers both fixation and salient
object segmentation ground-truth. With fixations and salient object being
presented simultaneously, we are able to bridge the gap between fixations and
salient objects, and propose a novel method for salient object segmentation.
Finally, we report significant benchmark progress on three existing datasets of
segmenting salient objects
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
Authors: Charles F. Cadieu, Ha Hong, Daniel L. K. Yamins, Nicolas Pinto, Diego Ardila, Ethan A. Solomon, Najib J. Majaj, James J. DiCarlo
Date: 2014-06-12 00:00:00
Tasks: Object, Object Recognition

The primate visual system achieves remarkable visual object recognition
performance even in brief presentations and under changes to object exemplar,
geometric transformations, and background variation (a.k.a. core visual object
recognition). This remarkable performance is mediated by the representation
formed in inferior temporal (IT) cortex. In parallel, recent advances in
machine learning have led to ever higher performing models of object
recognition using artificial deep neural networks (DNNs). It remains unclear,
however, whether the representational performance of DNNs rivals that of the
brain. To accurately produce such a comparison, a major difficulty has been a
unifying metric that accounts for experimental limitations such as the amount
of noise, the number of neural recording sites, and the number trials, and
computational limitations such as the complexity of the decoding classifier and
the number of classifier training examples. In this work we perform a direct
comparison that corrects for these experimental limitations and computational
considerations. As part of our methodology, we propose an extension of "kernel
analysis" that measures the generalization accuracy as a function of
representational complexity. Our evaluations show that, unlike previous
bio-inspired models, the latest DNNs rival the representational performance of
IT cortex on this visual object recognition task. Furthermore, we show that
models that perform well on measures of representational performance also
perform well on measures of representational similarity to IT and on measures
of predicting individual IT multi-unit responses. Whether these DNNs rely on
computational mechanisms similar to the primate visual system is yet to be
determined, but, unlike all previous bio-inspired models, that possibility
cannot be ruled out merely on representational performance grounds.
A swarm optimization algorithm inspired in the behavior of the social-spider
Authors: Erik Cuevas, Miguel Cienfuegos, Daniel Zaldivar, Marco Perez
Date: 2014-06-12 00:00:00
Tasks: Evolutionary Algorithms

Swarm intelligence is a research field that models the collective behavior in
swarms of insects or animals. Several algorithms arising from such models have
been proposed to solve a wide range of complex optimization problems. In this
paper, a novel swarm algorithm called the Social Spider Optimization (SSO) is
proposed for solving optimization tasks. The SSO algorithm is based on the
simulation of cooperative behavior of social-spiders. In the proposed
algorithm, individuals emulate a group of spiders which interact to each other
based on the biological laws of the cooperative colony. The algorithm considers
two different search agents (spiders): males and females. Depending on gender,
each individual is conducted by a set of different evolutionary operators which
mimic different cooperative behaviors that are typically found in the colony.
In order to illustrate the proficiency and robustness of the proposed approach,
it is compared to other well-known evolutionary methods. The comparison
examines several standard benchmark functions that are commonly considered
within the literature of evolutionary algorithms. The outcome shows a high
performance of the proposed method for searching a global optimum with several
benchmark functions.
Event and Anomaly Detection Using Tucker3 Decomposition
Authors: Hadi Fanaee-T, Márcia D. B. Oliveira, João Gama, Simon Malinowski, Ricardo Morla
Date: 2014-06-12 00:00:00
Tasks: Anomaly Detection

Failure detection in telecommunication networks is a vital task. So far,
several supervised and unsupervised solutions have been provided for
discovering failures in such networks. Among them unsupervised approaches has
attracted more attention since no label data is required. Often, network
devices are not able to provide information about the type of failure. In such
cases the type of failure is not known in advance and the unsupervised setting
is more appropriate for diagnosis. Among unsupervised approaches, Principal
Component Analysis (PCA) is a well-known solution which has been widely used in
the anomaly detection literature and can be applied to matrix data (e.g.
Users-Features). However, one of the important properties of network data is
their temporal sequential nature. So considering the interaction of dimensions
over a third dimension, such as time, may provide us better insights into the
nature of network failures. In this paper we demonstrate the power of three-way
analysis to detect events and anomalies in time-evolving network data.
HATP: An HTN Planner for Robotics
Authors: Raphaël Lallement, Lavindra de Silva, Rachid Alami
Date: 2014-05-21 00:00:00

Hierarchical Task Network (HTN) planning is a popular approach that cuts down
on the classical planning search space by relying on a given hierarchical
library of domain control knowledge. This provides an intuitive methodology for
specifying high-level instructions on how robots and agents should perform
tasks, while also giving the planner enough flexibility to choose the
lower-level steps and their ordering. In this paper we present the HATP
(Hierarchical Agent-based Task Planner) planning framework which extends the
traditional HTN planning domain representation and semantics by making them
more suitable for roboticists, and treating agents as "first class" entities in
the language. The former is achieved by allowing "social rules" to be defined
which specify what behaviour is acceptable/unacceptable by the agents/robots in
the domain, and interleaving planning with geometric reasoning in order to
validate online -with respect to a detailed geometric 3D world- the human/robot
actions currently being pursued by HATP.
Approximate Decentralized Bayesian Inference
Authors: Trevor Campbell, Jonathan P. How
Date: 2014-03-28 00:00:00
Tasks: Bayesian Inference, Variational Inference

This paper presents an approximate method for performing Bayesian inference
in models with conditional independence over a decentralized network of
learning agents. The method first employs variational inference on each
individual learning agent to generate a local approximate posterior, the agents
transmit their local posteriors to other agents in the network, and finally
each agent combines its set of received local posteriors. The key insight in
this work is that, for many Bayesian models, approximate inference schemes
destroy symmetry and dependencies in the model that are crucial to the correct
application of Bayes' rule when combining the local posteriors. The proposed
method addresses this issue by including an additional optimization step in the
combination procedure that accounts for these broken dependencies. Experiments
on synthetic and real data demonstrate that the decentralized method provides
advantages in computational performance and predictive test likelihood over
previous batch and distributed methods.
A hybrid neuro--wavelet predictor for QoS control and stability
Authors: Christian Napoli, Giuseppe Pappalardo, Emiliano Tramontana
Date: 2014-06-12 00:00:00
Tasks: Denoising, Time Series, Time Series Analysis

For distributed systems to properly react to peaks of requests, their
adaptation activities would benefit from the estimation of the amount of
requests. This paper proposes a solution to produce a short-term forecast based
on data characterising user behaviour of online services. We use \emph{wavelet
analysis}, providing compression and denoising on the observed time series of
the amount of past user requests; and a \emph{recurrent neural network} trained
with observed data and designed so as to provide well-timed estimations of
future requests. The said ensemble has the ability to predict the amount of
future user requests with a root mean squared error below 0.06\%. Thanks to
prediction, advance resource provision can be performed for the duration of a
request peak and for just the right amount of resources, hence avoiding
over-provisioning and associated costs. Moreover, reliable provision lets users
enjoy a level of availability of services unaffected by load variations.
A Cascade Neural Network Architecture investigating Surface Plasmon Polaritons propagation for thin metals in OpenMP
Authors: Francesco Bonanno, Giacomo Capizzi, Grazia Lo Sciuto, Christian Napoli, Giuseppe Pappalardo, Emiliano Tramontana
Date: 2014-06-12 00:00:00

Surface plasmon polaritons (SPPs) confined along metal-dielectric interface
have attracted a relevant interest in the area of ultracompact photonic
circuits, photovoltaic devices and other applications due to their strong field
confinement and enhancement. This paper investigates a novel cascade neural
network (NN) architecture to find the dependance of metal thickness on the SPP
propagation. Additionally, a novel training procedure for the proposed cascade
NN has been developed using an OpenMP-based framework, thus greatly reducing
training time. The performed experiments confirm the effectiveness of the
proposed NN architecture for the problem at hand.
Expressive Power and Approximation Errors of Restricted Boltzmann Machines
Authors: Guido Montufar, Johannes Rauh, Nihat Ay
Date: 2014-06-12 00:00:00
Proceeding: NeurIPS 2011 12

We present explicit classes of probability distributions that can be learned
by Restricted Boltzmann Machines (RBMs) depending on the number of units that
they contain, and which are representative for the expressive power of the
model. We use this to show that the maximal Kullback-Leibler divergence to the
RBM model with $n$ visible and $m$ hidden units is bounded from above by $n -
\left\lfloor \log(m+1) \right\rfloor -
\frac{m+1}{2^{\left\lfloor\log(m+1)\right\rfloor}} \approx (n -1) - \log(m+1)$.
In this way we can specify the number of hidden units that guarantees a
sufficiently rich model containing different classes of distributions and
respecting a given error tolerance.
Guarantees and Limits of Preprocessing in Constraint Satisfaction and Reasoning
Authors: Serge Gaspers, Stefan Szeider
Date: 2014-06-12 00:00:00

We present a first theoretical analysis of the power of polynomial-time
preprocessing for important combinatorial problems from various areas in AI. We
consider problems from Constraint Satisfaction, Global Constraints,
Satisfiability, Nonmonotonic and Bayesian Reasoning under structural
restrictions. All these problems involve two tasks: (i) identifying the
structure in the input as required by the restriction, and (ii) using the
identified structure to solve the reasoning task efficiently. We show that for
most of the considered problems, task (i) admits a polynomial-time
preprocessing to a problem kernel whose size is polynomial in a structural
problem parameter of the input, in contrast to task (ii) which does not admit
such a reduction to a problem kernel of polynomial size, subject to a
complexity theoretic assumption. As a notable exception we show that the
consistency problem for the AtMost-NValue constraint admits a polynomial kernel
consisting of a quadratic number of variables and domain values. Our results
provide a firm worst-case guarantees and theoretical boundaries for the
performance of polynomial-time preprocessing algorithms for the considered
problems.
Learning ELM network weights using linear discriminant analysis
Authors: Philip de Chazal, Jonathan Tapson, André van Schaik
Date: 2014-06-12 00:00:00
Tasks: General Classification

We present an alternative to the pseudo-inverse method for determining the
hidden to output weight values for Extreme Learning Machines performing
classification tasks. The method is based on linear discriminant analysis and
provides Bayes optimal single point estimates for the weight values.
A Brief State of the Art for Ontology Authoring
Authors: Hazem Safwat, Brian Davis
Date: 2014-06-11 00:00:00

One of the main challenges for building the Semantic web is Ontology
Authoring. Controlled Natural Languages CNLs offer a user friendly means for
non-experts to author ontologies. This paper provides a snapshot of the
state-of-the-art for the core CNLs for ontology authoring and reviews their
respective evaluations.
Gradient Sliding for Composite Optimization
Authors: Guanghui Lan
Date: 2014-06-04 00:00:00

We consider in this paper a class of composite optimization problems whose
objective function is given by the summation of a general smooth and nonsmooth
component, together with a relatively simple nonsmooth term. We present a new
class of first-order methods, namely the gradient sliding algorithms, which can
skip the computation of the gradient for the smooth component from time to
time. As a consequence, these algorithms require only ${\cal
O}(1/\sqrt{\epsilon})$ gradient evaluations for the smooth component in order
to find an $\epsilon$-solution for the composite problem, while still
maintaining the optimal ${\cal O}(1/\epsilon^2)$ bound on the total number of
subgradient evaluations for the nonsmooth component. We then present a
stochastic counterpart for these algorithms and establish similar complexity
bounds for solving an important class of stochastic composite optimization
problems. Moreover, if the smooth component in the composite function is
strongly convex, the developed gradient sliding algorithms can significantly
reduce the number of graduate and subgradient evaluations for the smooth and
nonsmooth component to ${\cal O} (\log (1/\epsilon))$ and ${\cal
O}(1/\epsilon)$, respectively. Finally, we generalize these algorithms to the
case when the smooth component is replaced by a nonsmooth one possessing a
certain bi-linear saddle point structure.
Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency
Authors: Wenhao Lu, Xiaochen Lian, Alan Yuille
Date: 2014-06-09 00:00:00

This paper addresses the problem of semantic part parsing (segmentation) of
cars, i.e.assigning every pixel within the car to one of the parts (e.g.body,
window, lights, license plates and wheels). We formulate this as a landmark
identification problem, where a set of landmarks specifies the boundaries of
the parts. A novel mixture of graphical models is proposed, which dynamically
couples the landmarks to a hierarchy of segments. When modeling pairwise
relation between landmarks, this coupling enables our model to exploit the
local image contents in addition to spatial deformation, an aspect that most
existing graphical models ignore. In particular, our model enforces appearance
consistency between segments within the same part. Parsing the car, including
finding the optimal coupling between landmarks and segments in the hierarchy,
is performed by dynamic programming. We evaluate our method on a subset of
PASCAL VOC 2010 car images and on the car subset of 3D Object Category dataset
(CAR3D). We show good results and, in particular, quantify the effectiveness of
using the segment appearance consistency in terms of accuracy of part
localization and segmentation.
Distributed Parameter Estimation in Probabilistic Graphical Models
Authors: Yariv Dror Mizrahi, Misha Denil, Nando de Freitas
Date: 2014-06-11 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: parameter estimation

This paper presents foundational theoretical results on distributed parameter
estimation for undirected probabilistic graphical models. It introduces a
general condition on composite likelihood decompositions of these models which
guarantees the global consistency of distributed estimators, provided the local
estimators are consistent.
Input Warping for Bayesian Optimization of Non-stationary Functions
Authors: Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams
Date: 2014-02-05 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes, global-optimization

Bayesian optimization has proven to be a highly effective methodology for the
global optimization of unknown, expensive and multimodal functions. The ability
to accurately model distributions over functions is critical to the
effectiveness of Bayesian optimization. Although Gaussian processes provide a
flexible prior over functions which can be queried efficiently, there are
various classes of functions that remain difficult to model. One of the most
frequently occurring of these is the class of non-stationary functions. The
optimization of the hyperparameters of machine learning algorithms is a problem
domain in which parameters are often manually transformed a priori, for example
by optimizing in "log-space," to mitigate the effects of spatially-varying
length scale. We develop a methodology for automatically learning a wide family
of bijective transformations or warpings of the input space using the Beta
cumulative distribution function. We further extend the warping framework to
multi-task Bayesian optimization so that multiple tasks can be warped into a
jointly stationary space. On a set of challenging benchmark optimization tasks,
we observe that the inclusion of warping greatly improves on the
state-of-the-art, producing better results faster and more reliably.
Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection
Authors: Yi-Lun Wang, Xinhua Su
Date: 2014-06-11 00:00:00
Tasks: Compressive Sensing, Image Inpainting, Image Restoration, Matrix Completion

Recovering a large matrix from limited measurements is a challenging task
arising in many real applications, such as image inpainting, compressive
sensing and medical imaging, and this kind of problems are mostly formulated as
low-rank matrix approximation problems. Due to the rank operator being
non-convex and discontinuous, most of the recent theoretical studies use the
nuclear norm as a convex relaxation and the low-rank matrix recovery problem is
solved through minimization of the nuclear norm regularized problem. However, a
major limitation of nuclear norm minimization is that all the singular values
are simultaneously minimized and the rank may not be well approximated
\cite{hu2012fast}. Correspondingly, in this paper, we propose a new multi-stage
algorithm, which makes use of the concept of Truncated Nuclear Norm
Regularization (TNNR) proposed in \citep{hu2012fast} and Iterative Support
Detection (ISD) proposed in \citep{wang2010sparse} to overcome the above
limitation. Besides matrix completion problems considered in
\citep{hu2012fast}, the proposed method can be also extended to the general
low-rank matrix recovery problems. Extensive experiments well validate the
superiority of our new algorithms over other state-of-the-art methods.
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets
Authors: Steve Branson, Grant van Horn, Serge Belongie, Pietro Perona
Date: 2014-06-11 00:00:00
Tasks: Classification, Clustering, Fine-Grained Visual Categorization, General Classification

We propose an architecture for fine-grained visual categorization that
approaches expert human performance in the classification of bird species. Our
architecture first computes an estimate of the object's pose; this is used to
compute local image features which are, in turn, used for classification. The
features are computed by applying deep convolutional nets to image patches that
are located and normalized by the pose. We perform an empirical study of a
number of pose normalization schemes, including an investigation of higher
order geometric warping functions. We propose a novel graph-based clustering
algorithm for learning a compact pose normalization space. We perform a
detailed investigation of state-of-the-art deep convolutional feature
implementations and fine-tuning feature learning for fine-grained
classification. We observe that a model that integrates lower-level feature
layers with pose-normalized extraction routines and higher-level feature layers
with unaligned image features works best. Our experiments advance
state-of-the-art performance on bird species recognition, with a large
improvement of correct classification rates over previous methods (75% vs.
55-65%).
Acoustic Gait-based Person Identification using Hidden Markov Models
Authors: Jürgen T. Geiger, Maximilian Kneißl, Björn Schuller, Gerhard Rigoll
Date: 2014-06-11 00:00:00
Tasks: Gait Recognition, Person Identification

We present a system for identifying humans by their walking sounds. This
problem is also known as acoustic gait recognition. The goal of the system is
to analyse sounds emitted by walking persons (mostly the step sounds) and
identify those persons. These sounds are characterised by the gait pattern and
are influenced by the movements of the arms and legs, but also depend on the
type of shoe. We extract cepstral features from the recorded audio signals and
use hidden Markov models for dynamic classification. A cyclic model topology is
employed to represent individual gait cycles. This topology allows to model and
detect individual steps, leading to very promising identification rates. For
experimental validation, we use the publicly available TUM GAID database, which
is a large gait recognition database containing 3050 recordings of 305 subjects
in three variations. In the best setup, an identification rate of 65.5 % is
achieved out of 155 subjects. This is a relative improvement of almost 30 %
compared to our previous work, which used various audio features and support
vector machines.
Explicit Computation of Input Weights in Extreme Learning Machines
Authors: Jonathan Tapson, Philip de Chazal, André van Schaik
Date: 2014-06-11 00:00:00

We present a closed form expression for initializing the input weights in a
multi-layer perceptron, which can be used as the first step in synthesis of an
Extreme Learning Ma-chine. The expression is based on the standard function for
a separating hyperplane as computed in multilayer perceptrons and linear
Support Vector Machines; that is, as a linear combination of input data
samples. In the absence of supervised training for the input weights, random
linear combinations of training data samples are used to project the input data
to a higher dimensional hidden layer. The hidden layer weights are solved in
the standard ELM fashion by computing the pseudoinverse of the hidden layer
outputs and multiplying by the desired output values. All weights for this
method can be computed in a single pass, and the resulting networks are more
accurate and more consistent on some standard problems than regular ELM
networks of the same size.