pwc-archive/papers-with-abstracts huggingface dataset:
Groupwise registration of aerial images
Authors: Ognjen Arandjelovic, Duc-Son Pham, Svetha Venkatesh
Date: 2015-04-21 00:00:00
Tasks: Image Registration

This paper addresses the task of time separated aerial image registration.
The ability to solve this problem accurately and reliably is important for a
variety of subsequent image understanding applications. The principal challenge
lies in the extent and nature of transient appearance variation that a land
area can undergo, such as that caused by the change in illumination conditions,
seasonal variations, or the occlusion by non-persistent objects (people, cars).
Our work introduces several novelties: (i) unlike all previous work on aerial
image registration, we approach the problem using a set-based paradigm; (ii) we
show how local, pair-wise constraints can be used to enforce a globally good
registration using a constraints graph structure; (iii) we show how a simple
holistic representation derived from raw aerial images can be used as a basic
building block of the constraints graph in a manner which achieves both high
registration accuracy and speed. We demonstrate: (i) that the proposed method
outperforms the state-of-the-art for pair-wise registration already, achieving
greater accuracy and reliability, while at the same time reducing the
computational cost of the task; and (ii) that the increase in the number of
available images in a set consistently reduces the average registration error.
Viewpoint distortion compensation in practical surveillance systems
Authors: Ognjen Arandjelovic, Duc-Son Pham, Svetha Venkatesh
Date: 2015-04-21 00:00:00
Tasks: global-optimization

Our aim is to estimate the perspective-effected geometric distortion of a
scene from a video feed. In contrast to all previous work we wish to achieve
this using from low-level, spatio-temporally local motion features used in
commercial semi-automatic surveillance systems. We: (i) describe a dense
algorithm which uses motion features to estimate the perspective distortion at
each image locus and then polls all such local estimates to arrive at the
globally best estimate, (ii) present an alternative coarse algorithm which
subdivides the image frame into blocks, and uses motion features to derive
block-specific motion characteristics and constrain the relationships between
these characteristics, with the perspective estimate emerging as a result of a
global optimization scheme, and (iii) report the results of an evaluation using
nine large sets acquired using existing close-circuit television (CCTV)
cameras. Our findings demonstrate that both of the proposed methods are
successful, their accuracy matching that of human labelling using complete
visual data.
Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms
Authors: Rong Ge, Tengyu Ma
Date: 2015-04-21 00:00:00
Tasks: Tensor Decomposition

Tensor rank and low-rank tensor decompositions have many applications in
learning and complexity theory. Most known algorithms use unfoldings of tensors
and can only handle rank up to $n^{\lfloor p/2 \rfloor}$ for a $p$-th order
tensor in $\mathbb{R}^{n^p}$. Previously no efficient algorithm can decompose
3rd order tensors when the rank is super-linear in the dimension. Using ideas
from sum-of-squares hierarchy, we give the first quasi-polynomial time
algorithm that can decompose a random 3rd order tensor decomposition when the
rank is as large as $n^{3/2}/\textrm{polylog} n$.
  We also give a polynomial time algorithm for certifying the injective norm of
random low rank tensors. Our tensor decomposition algorithm exploits the
relationship between injective norm and the tensor components. The proof relies
on interesting tools for decoupling random variables to prove better matrix
concentration bounds, which can be useful in other settings.
An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning
Authors: Richard S. Sutton, A. Rupam Mahmood, Martha White
Date: 2015-03-14 00:00:00

In this paper we introduce the idea of improving the performance of
parametric temporal-difference (TD) learning algorithms by selectively
emphasizing or de-emphasizing their updates on different time steps. In
particular, we show that varying the emphasis of linear TD($\lambda$)'s updates
in a particular way causes its expected update to become stable under
off-policy training. The only prior model-free TD methods to achieve this with
per-step computation linear in the number of function approximation parameters
are the gradient-TD family of methods including TDC, GTD($\lambda$), and
GQ($\lambda$). Compared to these methods, our _emphatic TD($\lambda$)_ is
simpler and easier to use; it has only one learned parameter vector and one
step-size parameter. Our treatment includes general state-dependent discounting
and bootstrapping functions, and a way of specifying varying degrees of
interest in accurately valuing different states.
Show and Tell: A Neural Image Caption Generator
Authors: Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan
Date: 2014-11-17 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Captioning, Image Retrieval with Multi-Modal Query, Sentence, Text Generation, Text-to-Image Generation, Translation

Automatically describing the content of an image is a fundamental problem in
artificial intelligence that connects computer vision and natural language
processing. In this paper, we present a generative model based on a deep
recurrent architecture that combines recent advances in computer vision and
machine translation and that can be used to generate natural sentences
describing an image. The model is trained to maximize the likelihood of the
target description sentence given the training image. Experiments on several
datasets show the accuracy of the model and the fluency of the language it
learns solely from image descriptions. Our model is often quite accurate, which
we verify both qualitatively and quantitatively. For instance, while the
current state-of-the-art BLEU-1 score (the higher the better) on the Pascal
dataset is 25, our approach yields 59, to be compared to human performance
around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66,
and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we
achieve a BLEU-4 of 27.7, which is the current state-of-the-art.
Convolutional Neural Network-Based Image Representation for Visual Loop Closure Detection
Authors: Yi Hou, Hong Zhang, Shilin Zhou
Date: 2015-04-20 00:00:00
Tasks: CPU, GPU, Loop Closure Detection, Simultaneous Localization and Mapping

Deep convolutional neural networks (CNN) have recently been shown in many
computer vision and pattern recog- nition applications to outperform by a
significant margin state- of-the-art solutions that use traditional
hand-crafted features. However, this impressive performance is yet to be fully
exploited in robotics. In this paper, we focus one specific problem that can
benefit from the recent development of the CNN technology, i.e., we focus on
using a pre-trained CNN model as a method of generating an image representation
appropriate for visual loop closure detection in SLAM (simultaneous
localization and mapping). We perform a comprehensive evaluation of the outputs
at the intermediate layers of a CNN as image descriptors, in comparison with
state-of-the-art image descriptors, in terms of their ability to match images
for detecting loop closures. The main conclusions of our study include: (a)
CNN-based image representations perform comparably to state-of-the-art hand-
crafted competitors in environments without significant lighting change, (b)
they outperform state-of-the-art competitors when lighting changes
significantly, and (c) they are also significantly faster to extract than the
state-of-the-art hand-crafted features even on a conventional CPU and are two
orders of magnitude faster on an entry-level GPU.
Scalable Multilabel Prediction via Randomized Methods
Authors: Nikos Karampatziakis, Paul Mineiro
Date: 2015-02-09 00:00:00
Tasks: General Classification, Prediction

Modeling the dependence between outputs is a fundamental challenge in
multilabel classification. In this work we show that a generic regularized
nonlinearity mapping independent predictions to joint predictions is sufficient
to achieve state-of-the-art performance on a variety of benchmark problems.
Crucially, we compute the joint predictions without ever obtaining any
independent predictions, while incorporating low-rank and smoothness
regularization. We achieve this by leveraging randomized algorithms for matrix
decomposition and kernel approximation. Furthermore, our techniques are
applicable to the multiclass setting. We apply our method to a variety of
multiclass and multilabel data sets, obtaining state-of-the-art results.
Multi-view Face Detection Using Deep Convolutional Neural Networks
Authors: Sachin Sudhakar Farfade, Mohammad Saberian, Li-Jia Li
Date: 2015-02-10 00:00:00
Tasks: Data Augmentation, Face Detection, object-detection, Object Detection

In this paper we consider the problem of multi-view face detection. While
there has been significant research on this problem, current state-of-the-art
approaches for this task require annotation of facial landmarks, e.g. TSM [25],
or annotation of face poses [28, 22]. They also require training dozens of
models to fully capture faces in all orientations, e.g. 22 models in HeadHunter
method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method
that does not require pose/landmark annotation and is able to detect faces in a
wide range of orientations using a single model based on deep convolutional
neural networks. The proposed method has minimal complexity; unlike other
recent deep learning object detection methods [9], it does not require
additional components such as segmentation, bounding-box regression, or SVM
classifiers. Furthermore, we analyzed scores of the proposed face detector for
faces in different orientations and found that 1) the proposed method is able
to detect faces from different angles and can handle occlusion to some extent,
2) there seems to be a correlation between dis- tribution of positive examples
in the training set and scores of the proposed face detector. The latter
suggests that the proposed methods performance can be further improved by using
better sampling strategies and more sophisticated data augmentation techniques.
Evaluations on popular face detection benchmark datasets show that our
single-model face detector algorithm has similar or better performance compared
to the previous methods, which are more complex and require annotations of
either different poses or facial landmarks.
Multi-swarm PSO algorithm for the Quadratic Assignment Problem: a massive parallel implementation on the OpenCL platform
Authors: Piotr Szwed, Wojciech Chmiel
Date: 2015-04-20 00:00:00

This paper presents a multi-swarm PSO algorithm for the Quadratic Assignment
Problem (QAP) implemented on OpenCL platform. Our work was motivated by results
of time efficiency tests performed for single-swarm algorithm implementation
that showed clearly that the benefits of a parallel execution platform can be
fully exploited, if the processed population is large. The described algorithm
can be executed in two modes: with independent swarms or with migration. We
discuss the algorithm construction, as well as we report results of tests
performed on several problem instances from the QAPLIB library. During the
experiments the algorithm was configured to process large populations. This
allowed us to collect statistical data related to values of goal function
reached by individual particles. We use them to demonstrate on two test cases
that although single particles seem to behave chaotically during the
optimization process, when the whole population is analyzed, the probability
that a particle will select a near-optimal solution grows.
Restricted Boltzmann Machine for Classification with Hierarchical Correlated Prior
Authors: Gang Chen, Sargur H. Srihari
Date: 2014-06-13 00:00:00
Tasks: Classification, General Classification

Restricted Boltzmann machines (RBM) and its variants have become hot research
topics recently, and widely applied to many classification problems, such as
character recognition and document categorization. Often, classification RBM
ignores the interclass relationship or prior knowledge of sharing information
among classes. In this paper, we are interested in RBM with the hierarchical
prior over classes. We assume parameters for nearby nodes are correlated in the
hierarchical tree, and further the parameters at each node of the tree be
orthogonal to those at its ancestors. We propose a hierarchical correlated RBM
for classification problem, which generalizes the classification RBM with
sharing information among different classes. In order to reduce the redundancy
between node parameters in the hierarchy, we also introduce orthogonal
restrictions to our objective function. We test our method on challenge
datasets, and show promising results compared to competitive baselines.
Network Plasticity as Bayesian Inference
Authors: David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass
Date: 2015-04-20 00:00:00
Tasks: Bayesian Inference, Learning Theory

General results from statistical learning theory suggest to understand not
only brain computations, but also brain plasticity as probabilistic inference.
But a model for that has been missing. We propose that inherently stochastic
features of synaptic plasticity and spine motility enable cortical networks of
neurons to carry out probabilistic inference by sampling from a posterior
distribution of network configurations. This model provides a viable
alternative to existing models that propose convergence of parameters to
maximum likelihood values. It explains how priors on weight distributions and
connection probabilities can be merged optimally with learned experience, how
cortical networks can generalize learned information so well to novel
experiences, and how they can compensate continuously for unforeseen
disturbances of the network. The resulting new theory of network plasticity
explains from a functional perspective a number of experimental data on
stochastic aspects of synaptic plasticity that previously appeared to be quite
puzzling.
Optimal Nudging: Solving Average-Reward Semi-Markov Decision Processes as a Minimal Sequence of Cumulative Tasks
Authors: Reinaldo Uribe Muriel, Fernando Lozando, Charles Anderson
Date: 2015-04-20 00:00:00
Tasks: Reinforcement Learning

This paper describes a novel method to solve average-reward semi-Markov
decision processes, by reducing them to a minimal sequence of cumulative reward
problems. The usual solution methods for this type of problems update the gain
(optimal average reward) immediately after observing the result of taking an
action. The alternative introduced, optimal nudging, relies instead on setting
the gain to some fixed value, which transitorily makes the problem a
cumulative-reward task, solving it by any standard reinforcement learning
method, and only then updating the gain in a way that minimizes uncertainty in
a minmax sense. The rule for optimal gain update is derived by exploiting the
geometric features of the w-l space, a simple mapping of the space of policies.
The total number of cumulative reward tasks that need to be solved is shown to
be small. Some experiments are presented to explore the features of the
algorithm and to compare its performance with other approaches.
Hybrid Genetic Algorithm and Lasso Test Approach for Inferring Well Supported Phylogenetic Trees based on Subsets of Chloroplastic Core Genes
Authors: Bassam AlKindy, Christophe Guyeux, Jean-François Couchot, Michel Salomon, Christian Parisod, Jacques M. Bahi
Date: 2015-04-20 00:00:00

The amount of completely sequenced chloroplast genomes increases rapidly
every day, leading to the possibility to build large scale phylogenetic trees
of plant species. Considering a subset of close plant species defined according
to their chloroplasts, the phylogenetic tree that can be inferred by their core
genes is not necessarily well supported, due to the possible occurrence of
"problematic" genes (i.e., homoplasy, incomplete lineage sorting, horizontal
gene transfers, etc.) which may blur phylogenetic signal. However, a
trustworthy phylogenetic tree can still be obtained if the number of
problematic genes is low, the problem being to determine the largest subset of
core genes that produces the best supported tree. To discard problematic genes
and due to the overwhelming number of possible combinations, we propose an
hybrid approach that embeds both genetic algorithms and statistical tests.
Given a set of organisms, the result is a pipeline of many stages for the
production of well supported phylogenetic trees. The proposal has been applied
to different cases of plant families, leading to encouraging results for these
families.
Nonparametric Nearest Neighbor Random Process Clustering
Authors: Michael Tschannen, Helmut Bölcskei
Date: 2015-04-20 00:00:00
Tasks: Clustering

We consider the problem of clustering noisy finite-length observations of
stationary ergodic random processes according to their nonparametric generative
models without prior knowledge of the model statistics and the number of
generative models. Two algorithms, both using the L1-distance between estimated
power spectral densities (PSDs) as a measure of dissimilarity, are analyzed.
The first algorithm, termed nearest neighbor process clustering (NNPC), to the
best of our knowledge, is new and relies on partitioning the nearest neighbor
graph of the observations via spectral clustering. The second algorithm, simply
referred to as k-means (KM), consists of a single k-means iteration with
farthest point initialization and was considered before in the literature,
albeit with a different measure of dissimilarity and with asymptotic
performance results only. We show that both NNPC and KM succeed with high
probability under noise and even when the generative process PSDs overlap
significantly, all provided that the observation length is sufficiently large.
Our results quantify the tradeoff between the overlap of the generative process
PSDs, the noise variance, and the observation length. Finally, we present
numerical performance results for synthetic and real data.
F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation
Authors: Xiaohe Wu, WangMeng Zuo, Yuanyuan Zhu, Liang Lin
Date: 2015-04-20 00:00:00

The generalization error bound of support vector machine (SVM) depends on the
ratio of radius and margin, while standard SVM only considers the maximization
of the margin but ignores the minimization of the radius. Several approaches
have been proposed to integrate radius and margin for joint learning of feature
transformation and SVM classifier. However, most of them either require the
form of the transformation matrix to be diagonal, or are non-convex and
computationally expensive. In this paper, we suggest a novel approximation for
the radius of minimum enclosing ball (MEB) in feature space, and then propose a
convex radius-margin based SVM model for joint learning of feature
transformation and SVM classifier, i.e., F-SVM. An alternating minimization
method is adopted to solve the F-SVM model, where the feature transformation is
updatedvia gradient descent and the classifier is updated by employing the
existing SVM solver. By incorporating with kernel principal component analysis,
F-SVM is further extended for joint learning of nonlinear transformation and
classifier. Experimental results on the UCI machine learning datasets and the
LFW face datasets show that F-SVM outperforms the standard SVM and the existing
radius-margin based SVMs, e.g., RMM, R-SVM+ and R-SVM+{\mu}.
Weakly Supervised Multi-Embeddings Learning of Acoustic Models
Authors: Gabriel Synnaeve, Emmanuel Dupoux
Date: 2014-12-20 00:00:00

We trained a Siamese network with multi-task same/different information on a
speech dataset, and found that it was possible to share a network for both
tasks without a loss in performance. The first task was to discriminate between
two same or different words, and the second was to discriminate between two
same or different talkers.
Multi-Level Anomaly Detection on Time-Varying Graph Data
Authors: Robert A. Bridges, John Collins, Erik M. Ferragut, Jason Laska, Blair D. Sullivan
Date: 2014-10-16 00:00:00
Tasks: Anomaly Detection, Graph Anomaly Detection

This work presents a novel modeling and analysis framework for graph
sequences which addresses the challenge of detecting and contextualizing
anomalies in labelled, streaming graph data. We introduce a generalization of
the BTER model of Seshadhri et al. by adding flexibility to community
structure, and use this model to perform multi-scale graph anomaly detection.
Specifically, probability models describing coarse subgraphs are built by
aggregating probabilities at finer levels, and these closely related
hierarchical models simultaneously detect deviations from expectation. This
technique provides insight into a graph's structure and internal context that
may shed light on a detected event. Additionally, this multi-scale analysis
facilitates intuitive visualizations by allowing users to narrow focus from an
anomalous graph to particular subgraphs or nodes causing the anomaly.
  For evaluation, two hierarchical anomaly detectors are tested against a
baseline Gaussian method on a series of sampled graphs. We demonstrate that our
graph statistics-based approach outperforms both a distribution-based detector
and the baseline in a labeled setting with community structure, and it
accurately detects anomalies in synthetic and real-world datasets at the node,
subgraph, and graph levels. To illustrate the accessibility of information made
possible via this technique, the anomaly detector and an associated interactive
visualization tool are tested on NCAA football data, where teams and
conferences that moved within the league are identified with perfect recall,
and precision greater than 0.786.
Low Rank Matrix Completion with Exponential Family Noise
Authors: Jean Lafond
Date: 2015-02-24 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, Prediction

The matrix completion problem consists in reconstructing a matrix from a
sample of entries, possibly observed with noise. A popular class of estimator,
known as nuclear norm penalized estimators, are based on minimizing the sum of
a data fitting term and a nuclear norm penalization. Here, we investigate the
case where the noise distribution belongs to the exponential family and is
sub-exponential. Our framework alllows for a general sampling scheme. We first
consider an estimator defined as the minimizer of the sum of a log-likelihood
term and a nuclear norm penalization and prove an upper bound on the Frobenius
prediction risk. The rate obtained improves on previous works on matrix
completion for exponential family. When the sampling distribution is known, we
propose another estimator and prove an oracle inequality w.r.t. the
Kullback-Leibler prediction risk, which translates immediatly into an upper
bound on the Frobenius prediction risk. Finally, we show that all the rates
obtained are minimax optimal up to a logarithmic factor.