pwc-archive/papers-with-abstracts huggingface dataset:
Consistently Estimating Markov Chains with Noisy Aggregate Data
Authors: Garrett Bernstein, Daniel Sheldon
Date: 2016-04-14 00:00:00

We address the problem of estimating the parameters of a time-homogeneous
Markov chain given only noisy, aggregate data. This arises when a population of
individuals behave independently according to a Markov chain, but individual
sample paths cannot be observed due to limitations of the observation process
or the need to protect privacy. Instead, only population-level counts of the
number of individuals in each state at each time step are available. When these
counts are exact, a conditional least squares (CLS) estimator is known to be
consistent and asymptotically normal. We initiate the study of method of
moments estimators for this problem to handle the more realistic case when
observations are additionally corrupted by noise. We show that CLS can be
interpreted as a simple "plug-in" method of moments estimator. However, when
observations are noisy, it is not consistent because it fails to account for
additional variance introduced by the noise. We develop a new, simpler method
of moments estimator that bypasses this problem and is consistent under noisy
observations.
Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images
Authors: Aravindh Mahendran, Andrea Vedaldi
Date: 2015-12-07 00:00:00

Image representations, from SIFT and bag of visual words to Convolutional
Neural Networks (CNNs) are a crucial component of almost all computer vision
systems. However, our understanding of them remains limited. In this paper we
study several landmark representations, both shallow and deep, by a number of
complementary visualization techniques. These visualizations are based on the
concept of "natural pre-image", namely a natural-looking image whose
representation has some notable property. We study in particular three such
visualizations: inversion, in which the aim is to reconstruct an image from its
representation, activation maximization, in which we search for patterns that
maximally stimulate a representation component, and caricaturization, in which
the visual patterns that a representation detects in an image are exaggerated.
We pose these as a regularized energy-minimization framework and demonstrate
its generality and effectiveness. In particular, we show that this method can
invert representations such as HOG more accurately than recent alternatives
while being applicable to CNNs too. Among our findings, we show that several
layers in CNNs retain photographically accurate information about the image,
with different degrees of geometric and photometric invariance.
A Deterministic Annealing Approach to the Multiple Traveling Salesmen and Related Problems
Authors: Mayank Baranwal, Brian Roehl, Srinivasa M. Salapaka
Date: 2016-04-14 00:00:00
Tasks: Traveling Salesman Problem

This paper presents a novel and efficient heuristic framework for
approximating the solutions to the multiple traveling salesmen problem (m-TSP)
and other variants on the TSP. The approach adopted in this paper is an
extension of the Maximum-Entropy-Principle (MEP) and the Deterministic
Annealing (DA) algorithm. The framework is presented as a general tool that can
be suitably adapted to a number of variants on the basic TSP. Additionally,
unlike most other heuristics for the TSP, the framework presented in this paper
is independent of the edges defined between any two pairs of nodes. This makes
the algorithm particularly suited for variants such as the close-enough
traveling salesman problem (CETSP) which are challenging due to added
computational complexity. The examples presented in this paper illustrate the
effectiveness of this new framework for use in TSP and many variants thereof.
Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval
Authors: Olivier Morère, Jie Lin, Antoine Veillard, Vijay Chandrasekhar, Tomaso Poggio
Date: 2016-03-15 00:00:00
Tasks: Image Instance Retrieval, Retrieval, Translation

The goal of this work is the computation of very compact binary hashes for
image instance retrieval. Our approach has two novel contributions. The first
one is Nested Invariance Pooling (NIP), a method inspired from i-theory, a
mathematical theory for computing group invariant transformations with
feed-forward neural networks. NIP is able to produce compact and
well-performing descriptors with visual representations extracted from
convolutional neural networks. We specifically incorporate scale, translation
and rotation invariances but the scheme can be extended to any arbitrary sets
of transformations. We also show that using moments of increasing order
throughout nesting is important. The NIP descriptors are then hashed to the
target code size (32-256 bits) with a Restricted Boltzmann Machine with a novel
batch-level regularization scheme specifically designed for the purpose of
hashing (RBMH). A thorough empirical evaluation with state-of-the-art shows
that the results obtained both with the NIP descriptors and the NIP+RBMH hashes
are consistently outstanding across a wide range of datasets.
Learning to Generate Genotypes with Neural Networks
Authors: Alexander W. Churchill, Siddharth Sigtia, Chrisantha Fernando
Date: 2016-04-14 00:00:00
Tasks: Denoising, Reinforcement Learning

Neural networks and evolutionary computation have a rich intertwined history.
They most commonly appear together when an evolutionary algorithm optimises the
parameters and topology of a neural network for reinforcement learning
problems, or when a neural network is applied as a surrogate fitness function
to aid the evolutionary optimisation of expensive fitness functions. In this
paper we take a different approach, asking the question of whether a neural
network can be used to provide a mutation distribution for an evolutionary
algorithm, and what advantages this approach may offer? Two modern neural
network models are investigated, a Denoising Autoencoder modified to produce
stochastic outputs and the Neural Autoregressive Distribution Estimator.
Results show that the neural network approach to learning genotypes is able to
solve many difficult discrete problems, such as MaxSat and HIFF, and regularly
outperforms other evolutionary techniques.
A Discrete Firefly Algorithm to Solve a Rich Vehicle Routing Problem Modelling a Newspaper Distribution System with Recycling Policy
Authors: E. Osaba, Xin-She Yang, F. Diaz, E. Onieva, A. D. Masegosa, A. Perallos
Date: 2016-04-14 00:00:00

A real-world newspaper distribution problem with recycling policy is tackled
in this work. In order to meet all the complex restrictions contained in such a
problem, it has been modeled as a rich vehicle routing problem, which can be
more specifically considered as an asymmetric and clustered vehicle routing
problem with simultaneous pickup and deliveries, variable costs and forbidden
paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the
literature. For this reason, a benchmark composed by 15 instances has been also
proposed. In the design of this benchmark, real geographical positions have
been used, located in the province of Bizkaia, Spain. For the proper treatment
of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed.
This application is the first application of the firefly algorithm to any rich
vehicle routing problem. To prove that the proposed DFA is a promising
technique, its performance has been compared with two other well-known
techniques: an evolutionary algorithm and an evolutionary simulated annealing.
Our results have shown that the DFA has outperformed these two classic
meta-heuristics.
Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle
Authors: Jason Kuen, Kian Ming Lim, Chin Poo Lee
Date: 2016-04-14 00:00:00
Tasks: Visual Tracking

Visual representation is crucial for a visual tracking method's performances.
Conventionally, visual representations adopted in visual tracking rely on
hand-crafted computer vision descriptors. These descriptors were developed
generically without considering tracking-specific information. In this paper,
we propose to learn complex-valued invariant representations from tracked
sequential image patches, via strong temporal slowness constraint and stacked
convolutional autoencoders. The deep slow local representations are learned
offline on unlabeled data and transferred to the observational model of our
proposed tracker. The proposed observational model retains old training samples
to alleviate drift, and collect negative samples which are coherent with
target's motion pattern for better discriminative tracking. With the learned
representation and online training samples, a logistic regression classifier is
adopted to distinguish target from background, and retrained online to adapt to
appearance changes. Subsequently, the observational model is integrated into a
particle filter framework to peform visual tracking. Experimental results on
various challenging benchmark sequences demonstrate that the proposed tracker
performs favourably against several state-of-the-art trackers.
On Reducing the Number of Visual Words in the Bag-of-Features Representation
Authors: Giuseppe Amato, Fabrizio Falchi, Claudio Gennaro
Date: 2016-04-14 00:00:00
Tasks: Landmark Recognition, Retrieval, Text Retrieval

A new class of applications based on visual search engines are emerging,
especially on smart-phones that have evolved into powerful tools for processing
images and videos. The state-of-the-art algorithms for large visual content
recognition and content based similarity search today use the "Bag of Features"
(BoF) or "Bag of Words" (BoW) approach. The idea, borrowed from text retrieval,
enables the use of inverted files. A very well known issue with this approach
is that the query images, as well as the stored data, are described with
thousands of words. This poses obvious efficiency problems when using inverted
files to perform efficient image matching. In this paper, we propose and
compare various techniques to reduce the number of words describing an image to
improve efficiency and we study the effects of this reduction on effectiveness
in landmark recognition and retrieval scenarios. We show that very relevant
improvement in performance are achievable still preserving the advantages of
the BoF base approach.
An Improved Discrete Bat Algorithm for Symmetric and Asymmetric Traveling Salesman Problems
Authors: Eneko Osaba, Xin-She Yang, Fernando Diaz, Pedro Lopez-Garcia, Roberto Carballedo
Date: 2016-04-14 00:00:00

Bat algorithm is a population metaheuristic proposed in 2010 which is based
on the echolocation or bio-sonar characteristics of microbats. Since its first
implementation, the bat algorithm has been used in a wide range of fields. In
this paper, we present a discrete version of the bat algorithm to solve the
well-known symmetric and asymmetric traveling salesman problems. In addition,
we propose an improvement in the basic structure of the classic bat algorithm.
To prove that our proposal is a promising approximation method, we have
compared its performance in 37 instances with the results obtained by five
different techniques: evolutionary simulated annealing, genetic algorithm, an
island based distributed genetic algorithm, a discrete firefly algorithm and an
imperialist competitive algorithm. In order to obtain fair and rigorous
comparisons, we have conducted three different statistical tests along the
paper: the Student's $t$-test, the Holm's test, and the Friedman test. We have
also compared the convergence behaviour shown by our proposal with the ones
shown by the evolutionary simulated annealing, and the discrete firefly
algorithm. The experimentation carried out in this study has shown that the
presented improved bat algorithm outperforms significantly all the other
alternatives in most of the cases.
Random-Key Cuckoo Search for the Travelling Salesman Problem
Authors: Aziz Ouaarab, B. Ahiod, Xin-She Yang
Date: 2016-04-14 00:00:00
Tasks: Combinatorial Optimization

Combinatorial optimization problems are typically NP-hard, and thus very
challenging to solve. In this paper, we present the random key cuckoo search
(RKCS) algorithm for solving the famous Travelling Salesman Problem (TSP). We
used a simplified random-key encoding scheme to pass from a continuous space
(real numbers) to a combinatorial space. We also consider the displacement of a
solution in both spaces using Levy flights. The performance of the proposed
RKCS is tested against a set of benchmarks of symmetric TSP from the well-known
TSPLIB library. The results of the tests show that RKCS is superior to some
other metaheuristic algorithms.
Filling in the details: Perceiving from low fidelity images
Authors: Farahnaz Ahmed Wick, Michael L. Wick, Marc Pomplun
Date: 2016-04-14 00:00:00
Tasks: Foveation, Visual Reasoning

Humans perceive their surroundings in great detail even though most of our
visual field is reduced to low-fidelity color-deprived (e.g. dichromatic) input
by the retina. In contrast, most deep learning architectures are
computationally wasteful in that they consider every part of the input when
performing an image processing task. Yet, the human visual system is able to
perform visual reasoning despite having only a small fovea of high visual
acuity. With this in mind, we wish to understand the extent to which
connectionist architectures are able to learn from and reason with low acuity,
distorted inputs. Specifically, we train autoencoders to generate full-detail
images from low-detail "foveations" of those images and then measure their
ability to reconstruct the full-detail images from the foveated versions. By
varying the type of foveation, we can study how well the architectures can cope
with various types of distortion. We find that the autoencoder compensates for
lower detail by learning increasingly global feature functions. In many cases,
the learnt features are suitable for reconstructing the original full-detail
image. For example, we find that the networks accurately perceive color in the
periphery, even when 75\% of the input is achromatic.
Training Deep Networks with Structured Layers by Matrix Backpropagation
Authors: Catalin Ionescu, Orestis Vantzos, Cristian Sminchisescu
Date: 2015-09-25 00:00:00

Deep neural network architectures have recently produced excellent results in
a variety of areas in artificial intelligence and visual recognition, well
surpassing traditional shallow architectures trained using hand-designed
features. The power of deep networks stems both from their ability to perform
local computations followed by pointwise non-linearities over increasingly
larger receptive fields, and from the simplicity and scalability of the
gradient-descent training procedure based on backpropagation. An open problem
is the inclusion of layers that perform global, structured matrix computations
like segmentation (e.g. normalized cuts) or higher-order pooling (e.g.
log-tangent space metrics defined over the manifold of symmetric positive
definite matrices) while preserving the validity and efficiency of an
end-to-end deep training framework. In this paper we propose a sound
mathematical apparatus to formally integrate global structured computation into
deep computation architectures. At the heart of our methodology is the
development of the theory and practice of backpropagation that generalizes to
the calculus of adjoint matrix variations. The proposed matrix backpropagation
methodology applies broadly to a variety of problems in machine learning or
computational perception. Here we illustrate it by performing visual
segmentation experiments using the BSDS and MSCOCO benchmarks, where we show
that deep networks relying on second-order pooling and normalized cuts layers,
trained end-to-end using matrix backpropagation, outperform counterparts that
do not take advantage of such global layers.
A General Framework for Describing Creative Agents
Authors: Valerio Velardo, Mauro Vallati
Date: 2016-04-14 00:00:00

Computational creativity is a subfield of AI focused on developing and
studying creative systems. Few academic studies analysing the behaviour of
creative agents from a theoretical viewpoint have been proposed. The proposed
frameworks are vague and hard to exploit; moreover, such works are focused on a
notion of creativity tailored for humans.
  In this paper we introduce General Creativity, which extends that traditional
notion. General Creativity provides the basis for a formalised theoretical
framework, that allows one to univocally describe any creative agent, and their
behaviour within societies of creative systems. Given the growing number of AI
creative systems developed over recent years, it is of fundamental importance
to understand how they could influence each other as well as how to gauge their
impact on human society. In particular, in this paper we exploit the proposed
framework for (i) identifying different forms of creativity; (ii) describing
some typical creative agents behaviour, and (iii) analysing the dynamics of
societies in which both human and non-human creative systems coexist.
Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources
Authors: Qi Wu, Peng Wang, Chunhua Shen, Anthony Dick, Anton Van Den Hengel
Date: 2015-11-22 00:00:00
Proceeding: CVPR 2016 6
Tasks: Form, General Knowledge, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We propose a method for visual question answering which combines an internal
representation of the content of an image with information extracted from a
general knowledge base to answer a broad range of image-based questions. This
allows more complex questions to be answered using the predominant neural
network-based approach than has previously been possible. It particularly
allows questions to be asked about the contents of an image, even when the
image itself does not contain the whole answer. The method constructs a textual
representation of the semantic content of an image, and merges it with textual
information sourced from a knowledge base, to develop a deeper understanding of
the scene viewed. Priming a recurrent neural network with this combined
information, and the submitted question, leads to a very flexible visual
question answering approach. We are specifically able to answer questions posed
in natural language, that refer to information not contained in the image. We
demonstrate the effectiveness of our model on two publicly available datasets,
Toronto COCO-QA and MS COCO-VQA and show that it produces the best reported
results in both cases.
Unconstrained Facial Landmark Localization with Backbone-Branches Fully-Convolutional Networks
Authors: Zhujin Liang, Shengyong Ding, Liang Lin
Date: 2015-07-13 00:00:00
Tasks: Face Alignment

This paper investigates how to rapidly and accurately localize facial
landmarks in unconstrained, cluttered environments rather than in the well
segmented face images. We present a novel Backbone-Branches Fully-Convolutional
Neural Network (BB-FCN), which produces facial landmark response maps directly
from raw images without relying on pre-process or sliding window approaches.
BB-FCN contains one backbone and a number of network branches with each
corresponding to one landmark type, and it operates in a progressive manner.
Specifically, the backbone roughly detects the locations of facial landmarks by
taking the whole image as input, and the branches further refine the
localizations based on a local observation from the backbone's intermediate
feature map. Moreover, our backbone-branches architecture does not contain
full-connection layers for location regression, leading to efficient learning
and inference. Our extensive experiments show that our model achieves superior
performances over other state-of-the-arts under both the constrained (i.e. with
face regions) and the "in the wild" scenarios.
Optimal Rates For Regularization Of Statistical Inverse Learning Problems
Authors: Gilles Blanchard, Nicole Mücke
Date: 2016-04-14 00:00:00

We consider a statistical inverse learning problem, where we observe the
image of a function $f$ through a linear operator $A$ at i.i.d. random design
points $X_i$, superposed with an additive noise. The distribution of the design
points is unknown and can be very general. We analyze simultaneously the direct
(estimation of $Af$) and the inverse (estimation of $f$) learning problems. In
this general framework, we obtain strong and weak minimax optimal rates of
convergence (as the number of observations $n$ grows large) for a large class
of spectral regularization methods over regularity classes defined through
appropriate source conditions. This improves on or completes previous results
obtained in related settings. The optimality of the obtained rates is shown not
only in the exponent in $n$ but also in the explicit dependency of the constant
factor in the variance of the noise and the radius of the source condition set.
Object Detection from Video Tubelets with Convolutional Neural Networks
Authors: Kai Kang, Wanli Ouyang, Hongsheng Li, Xiaogang Wang
Date: 2016-04-14 00:00:00
Proceeding: CVPR 2016 6
Tasks: image-classification, Image Classification, Object, object-detection, Object Detection, Object Tracking, Semantic Segmentation

Deep Convolution Neural Networks (CNNs) have shown impressive performance in
various vision tasks such as image classification, object detection and
semantic segmentation. For object detection, particularly in still images, the
performance has been significantly increased last year thanks to powerful deep
networks (e.g. GoogleNet) and detection frameworks (e.g. Regions with CNN
features (R-CNN)). The lately introduced ImageNet task on object detection from
video (VID) brings the object detection task into the video domain, in which
objects' locations at each frame are required to be annotated with bounding
boxes. In this work, we introduce a complete framework for the VID task based
on still-image object detection and general object tracking. Their relations
and contributions in the VID task are thoroughly studied and evaluated. In
addition, a temporal convolution network is proposed to incorporate temporal
information to regularize the detection results and shows its effectiveness for
the task.
Deep Feature Based Contextual Model for Object Detection
Authors: Wenqing Chu, Deng Cai
Date: 2016-04-14 00:00:00
Tasks: model, Object, object-detection, Object Detection

Object detection is one of the most active areas in computer vision, which
has made significant improvement in recent years. Current state-of-the-art
object detection methods mostly adhere to the framework of regions with
convolutional neural network (R-CNN) and only use local appearance features
inside object bounding boxes. Since these approaches ignore the contextual
information around the object proposals, the outcome of these detectors may
generate a semantically incoherent interpretation of the input image. In this
paper, we propose an ensemble object detection system which incorporates the
local appearance, the contextual information in term of relationships among
objects and the global scene based contextual feature generated by a
convolutional neural network. The system is formulated as a fully connected
conditional random field (CRF) defined on object proposals and the contextual
constraints among object proposals are modeled as edges naturally. Furthermore,
a fast mean field approximation method is utilized to inference in this CRF
model efficiently. The experimental results demonstrate that our approach
achieves a higher mean average precision (mAP) on PASCAL VOC 2007 datasets
compared to the baseline algorithm Faster R-CNN.