pwc-archive/papers-with-abstracts huggingface dataset:
Comparison of feature extraction and dimensionality reduction methods for single channel extracellular spike sorting
Authors: Anupam Mitra, Anagh Pathak, Kaushik Majumdar
Date: 2016-02-10 00:00:00
Tasks: Benchmarking, Clustering, Dimensionality Reduction, Spike Sorting

Spikes in the membrane electrical potentials of neurons play a major role in
the functioning of nervous systems of animals. Obtaining the spikes from
different neurons has been a challenging problem for decades. Several schemes
have been proposed for spike sorting to isolate the spikes of individual
neurons from electrical recordings in extracellular media. However, there is
much scope for improvement in the accuracies obtained using the prevailing
methods of spike sorting. To determine more effective spike sorting strategies
using well known methods, we compared different types of signal features and
techniques for dimensionality reduction in feature space. We tried to determine
an optimum or near optimum feature extraction and dimensionality reduction
methods and an optimum or near optimum number of features for spike sorting. We
assessed relative performance of well known methods on simulated recordings
specially designed for development and benchmarking of spike sorting schemes,
with varying number of spike classes and the well established method of
$k$-means clustering of selected features. We found that almost all well known
methods performed quite well. Nevertheless, from spike waveforms of 64 samples,
sampled at 24 kHz, using principal component analysis (PCA) to select around 46
to 55 features led to the better spike sorting performance than most other
methods (Wilcoxon signed rank sum test, $p < 0.001$).
Fast model selection by limiting SVM training times
Authors: Aydin Demircioglu, Daniel Horn, Tobias Glasmachers, Bernd Bischl, Claus Weihs
Date: 2016-02-10 00:00:00
Tasks: model, Model Selection

Kernelized Support Vector Machines (SVMs) are among the best performing
supervised learning methods. But for optimal predictive performance,
time-consuming parameter tuning is crucial, which impedes application. To
tackle this problem, the classic model selection procedure based on grid-search
and cross-validation was refined, e.g. by data subsampling and direct search
heuristics. Here we focus on a different aspect, the stopping criterion for SVM
training. We show that by limiting the training time given to the SVM solver
during parameter tuning we can reduce model selection times by an order of
magnitude.
DAP3D-Net: Where, What and How Actions Occur in Videos?
Authors: Li Liu, Yi Zhou, Ling Shao
Date: 2016-02-10 00:00:00
Tasks: Action Localization, Action Parsing, Action Understanding, Multi-Task Learning

Action parsing in videos with complex scenes is an interesting but
challenging task in computer vision. In this paper, we propose a generic 3D
convolutional neural network in a multi-task learning manner for effective Deep
Action Parsing (DAP3D-Net) in videos. Particularly, in the training phase,
action localization, classification and attributes learning can be jointly
optimized on our appearancemotion data via DAP3D-Net. For an upcoming test
video, we can describe each individual action in the video simultaneously as:
Where the action occurs, What the action is and How the action is performed. To
well demonstrate the effectiveness of the proposed DAP3D-Net, we also
contribute a new Numerous-category Aligned Synthetic Action dataset, i.e.,
NASA, which consists of 200; 000 action clips of more than 300 categories and
with 33 pre-defined action attributes in two hierarchical levels (i.e.,
low-level attributes of basic body part movements and high-level attributes
related to action motion). We learn DAP3D-Net using the NASA dataset and then
evaluate it on our collected Human Action Understanding (HAU) dataset.
Experimental results show that our approach can accurately localize, categorize
and describe multiple actions in realistic videos.
Patterns for Learning with Side Information
Authors: Rico Jonschkowski, Sebastian Höfer, Oliver Brock
Date: 2015-11-19 00:00:00
Tasks: Multi-Task Learning, MULTI-VIEW LEARNING

Supervised, semi-supervised, and unsupervised learning estimate a function
given input/output samples. Generalization of the learned function to unseen
data can be improved by incorporating side information into learning. Side
information are data that are neither from the input space nor from the output
space of the function, but include useful information for learning it. In this
paper we show that learning with side information subsumes a variety of related
approaches, e.g. multi-task learning, multi-view learning and learning using
privileged information. Our main contributions are (i) a new perspective that
connects these previously isolated approaches, (ii) insights about how these
methods incorporate different types of prior knowledge, and hence implement
different patterns, (iii) facilitating the application of these methods in
novel tasks, as well as (iv) a systematic experimental evaluation of these
patterns in two supervised learning tasks.
Word learning under infinite uncertainty
Authors: Richard A. Blythe, Andrew D. M. Smith, Kenny Smith
Date: 2014-12-08 00:00:00

Language learners must learn the meanings of many thousands of words, despite
those words occurring in complex environments in which infinitely many meanings
might be inferred by the learner as a word's true meaning. This problem of
infinite referential uncertainty is often attributed to Willard Van Orman
Quine. We provide a mathematical formalisation of an ideal cross-situational
learner attempting to learn under infinite referential uncertainty, and
identify conditions under which word learning is possible. As Quine's
intuitions suggest, learning under infinite uncertainty is in fact possible,
provided that learners have some means of ranking candidate word meanings in
terms of their plausibility; furthermore, our analysis shows that this ranking
could in fact be exceedingly weak, implying that constraints which allow
learners to infer the plausibility of candidate word meanings could themselves
be weak. This approach lifts the burden of explanation from `smart' word
learning constraints in learners, and suggests a programme of research into
weak, unreliable, probabilistic constraints on the inference of word meaning in
real word learners.
Gabor Wavelets in Image Processing
Authors: David Barina
Date: 2016-02-10 00:00:00

This work shows the use of a two-dimensional Gabor wavelets in image
processing. Convolution with such a two-dimensional wavelet can be separated
into two series of one-dimensional ones. The key idea of this work is to
utilize a Gabor wavelet as a multiscale partial differential operator of a
given order. Gabor wavelets are used here to detect edges, corners and blobs. A
performance of such an interest point detector is compared to detectors
utilizing a Haar wavelet and a derivative of a Gaussian function. The proposed
approach may be useful when a fast implementation of the Gabor transform is
available or when the transform is already precomputed.
Improved and Generalized Upper Bounds on the Complexity of Policy Iteration
Authors: Bruno Scherrer
Date: 2013-06-03 00:00:00
Proceeding: NeurIPS 2013 12

Given a Markov Decision Process (MDP) with $n$ states and a totalnumber $m$
of actions, we study the number of iterations needed byPolicy Iteration (PI)
algorithms to converge to the optimal$\gamma$-discounted policy. We consider
two variations of PI: Howard'sPI that changes the actions in all states with a
positive advantage,and Simplex-PI that only changes the action in the state
with maximaladvantage. We show that Howard's PI terminates after at most
$O\left(\frac{m}{1-\gamma}\log\left(\frac{1}{1-\gamma}\right)\right)$iterations,
improving by a factor $O(\log n)$ a result by Hansen etal., while Simplex-PI
terminates after at most
$O\left(\frac{nm}{1-\gamma}\log\left(\frac{1}{1-\gamma}\right)\right)$iterations,
improving by a factor $O(\log n)$ a result by Ye. Undersome structural
properties of the MDP, we then consider bounds thatare independent of the
discount factor~$\gamma$: quantities ofinterest are bounds $\tau\_t$ and
$\tau\_r$---uniform on all states andpolicies---respectively on the
\emph{expected time spent in transientstates} and \emph{the inverse of the
frequency of visits in recurrentstates} given that the process starts from the
uniform distribution.Indeed, we show that Simplex-PI terminates after at most
$\tilde O\left(n^3 m^2 \tau\_t \tau\_r \right)$ iterations. This extends
arecent result for deterministic MDPs by Post & Ye, in which $\tau\_t\le 1$ and
$\tau\_r \le n$, in particular it shows that Simplex-PI isstrongly polynomial
for a much larger class of MDPs. We explain whysimilar results seem hard to
derive for Howard's PI. Finally, underthe additional (restrictive) assumption
that the state space ispartitioned in two sets, respectively states that are
transient andrecurrent for all policies, we show that both Howard's PI
andSimplex-PI terminate after at most $\tilde
O(m(n^2\tau\_t+n\tau\_r))$iterations.
A statistical perspective of sampling scores for linear regression
Authors: Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovačević
Date: 2015-07-21 00:00:00
Tasks: regression

In this paper, we consider a statistical problem of learning a linear model
from noisy samples. Existing work has focused on approximating the least
squares solution by using leverage-based scores as an importance sampling
distribution. However, no finite sample statistical guarantees and no
computationally efficient optimal sampling strategies have been proposed. To
evaluate the statistical properties of different sampling strategies, we
propose a simple yet effective estimator, which is easy for theoretical
analysis and is useful in multitask linear regression. We derive the exact mean
square error of the proposed estimator for any given sampling scores. Based on
minimizing the mean square error, we propose the optimal sampling scores for
both estimator and predictor, and show that they are influenced by the
noise-to-signal ratio. Numerical simulations match the theoretical analysis
well.
Improved Eigenfeature Regularization for Face Identification
Authors: Bappaditya Mandal
Date: 2016-02-10 00:00:00
Tasks: Face Identification, Face Recognition

In this work, we propose to divide each class (a person) into subclasses
using spatial partition trees which helps in better capturing the
intra-personal variances arising from the appearances of the same individual.
We perform a comprehensive analysis on within-class and within-subclass
eigenspectrums of face images and propose a novel method of eigenspectrum
modeling which extracts discriminative features of faces from both
within-subclass and total or between-subclass scatter matrices. Effective
low-dimensional face discriminative features are extracted for face recognition
(FR) after performing discriminant evaluation in the entire eigenspace.
Experimental results on popular face databases (AR, FERET) and the challenging
unconstrained YouTube Face database show the superiority of our proposed
approach on all three databases.
Appearance Based Robot and Human Activity Recognition System
Authors: Bappaditya Mandal
Date: 2016-02-04 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

In this work, we present an appearance based human activity recognition
system. It uses background modeling to segment the foreground object and
extracts useful discriminative features for representing activities performed
by humans and robots. Subspace based method like principal component analysis
is used to extract low dimensional features from large voluminous activity
images. These low dimensional features are then used to classify an activity.
An apparatus is designed using a webcam, which watches a robot replicating a
human fall under indoor environment. In this apparatus, a robot performs
various activities (like walking, bending, moving arms) replicating humans,
which also includes a sudden fall. Experimental results on robot performing
various activities and standard human activity recognition databases show the
efficacy of our proposed method.
Loss factorization, weakly supervised learning and label noise robustness
Authors: Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni
Date: 2016-02-08 00:00:00
Tasks: Generalization Bounds, Weakly-supervised Learning

We prove that the empirical risk of most well-known loss functions factors
into a linear term aggregating all labels with a term that is label free, and
can further be expressed by sums of the loss. This holds true even for
non-smooth, non-convex losses and in any RKHS. The first term is a (kernel)
mean operator --the focal quantity of this work-- which we characterize as the
sufficient statistic for the labels. The result tightens known generalization
bounds and sheds new light on their interpretation.
  Factorization has a direct application on weakly supervised learning. In
particular, we demonstrate that algorithms like SGD and proximal methods can be
adapted with minimal effort to handle weak supervision, once the mean operator
has been estimated. We apply this idea to learning with asymmetric noisy
labels, connecting and extending prior work. Furthermore, we show that most
losses enjoy a data-dependent (by the mean operator) form of noise robustness,
in contrast with known negative results.
Image encryption with dynamic chaotic Look-Up Table
Authors: Med Karim Abdmouleh, Ali Khalfallah, Med Salim Bouhlel
Date: 2016-02-09 00:00:00

In this paper we propose a novel image encryption scheme. The proposed method
is based on the chaos theory. Our cryptosystem uses the chaos theory to define
a dynamic chaotic Look-Up Table (LUT) to compute the new value of the current
pixel to cipher. Applying this process on each pixel of the plain image, we
generate the encrypted image. The results of different experimental tests, such
as Key space analysis, Information Entropy and Histogram analysis, show that
the proposed encryption image scheme seems to be protected against various
attacks. A comparison between the plain and encrypted image, in terms of
correlation coefficient, proves that the plain image is very different from the
encrypted one.
Time Resource Networks
Authors: Szymon Sidor, Peng Yu, Cheng Fang, Brian Williams
Date: 2016-02-09 00:00:00
Tasks: Management, Scheduling

The problem of scheduling under resource constraints is widely applicable.
One prominent example is power management, in which we have a limited
continuous supply of power but must schedule a number of power-consuming tasks.
Such problems feature tightly coupled continuous resource constraints and
continuous temporal constraints.
  We address such problems by introducing the Time Resource Network (TRN), an
encoding for resource-constrained scheduling problems. The definition allows
temporal specifications using a general family of representations derived from
the Simple Temporal network, including the Simple Temporal Network with
Uncertainty, and the probabilistic Simple Temporal Network (Fang et al.
(2014)).
  We propose two algorithms for determining the consistency of a TRN: one based
on Mixed Integer Programing and the other one based on Constraint Programming,
which we evaluate on scheduling problems with Simple Temporal Constraints and
Probabilistic Temporal Constraints.
Neural Random-Access Machines
Authors: Karol Kurach, Marcin Andrychowicz, Ilya Sutskever
Date: 2015-11-19 00:00:00

In this paper, we propose and investigate a new neural network architecture
called Neural Random Access Machine. It can manipulate and dereference pointers
to an external variable-size random-access memory. The model is trained from
pure input-output examples using backpropagation.
  We evaluate the new model on a number of simple algorithmic tasks whose
solutions require pointer manipulation and dereferencing. Our results show that
the proposed model can learn to solve algorithmic tasks of such type and is
capable of operating on simple data structures like linked-lists and binary
trees. For easier tasks, the learned solutions generalize to sequences of
arbitrary length. Moreover, memory access during inference can be done in a
constant time under some assumptions.
A Controller-Recognizer Framework: How necessary is recognition for control?
Authors: Marcin Moczulski, Kelvin Xu, Aaron Courville, Kyunghyun Cho
Date: 2015-11-19 00:00:00

Recently there has been growing interest in building active visual object
recognizers, as opposed to the usual passive recognizers which classifies a
given static image into a predefined set of object categories. In this paper we
propose to generalize these recently proposed end-to-end active visual
recognizers into a controller-recognizer framework. A model in the
controller-recognizer framework consists of a controller, which interfaces with
an external manipulator, and a recognizer which classifies the visual input
adjusted by the manipulator. We describe two most recently proposed
controller-recognizer models: recurrent attention model and spatial transformer
network as representative examples of controller-recognizer models. Based on
this description we observe that most existing end-to-end
controller-recognizers tightly, or completely, couple a controller and
recognizer. We ask a question whether this tight coupling is necessary, and try
to answer this empirically by building a controller-recognizer model with a
decoupled controller and recognizer. Our experiments revealed that it is not
always necessary to tightly couple them and that by decoupling a controller and
recognizer, there is a possibility of building a generic controller that is
pretrained and works together with any subsequent recognizer.
A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images
Authors: Guillaume Noyel, Jesus Angulo, Dominique Jeulin
Date: 2016-02-09 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, Segmentation

A general framework of spatio-spectral segmentation for multi-spectral images
is introduced in this paper. The method is based on classification-driven
stochastic watershed (WS) by Monte Carlo simulations, and it gives more regular
and reliable contours than standard WS. The present approach is decomposed into
several sequential steps. First, a dimensionality-reduction stage is performed
using the factor-correspondence analysis method. In this context, a new way to
select the factor axes (eigenvectors) according to their spatial information is
introduced. Then, a spectral classification produces a spectral
pre-segmentation of the image. Subsequently, a probability density function
(pdf) of contours containing spatial and spectral information is estimated by
simulation using a stochastic WS approach driven by the spectral
classification. The pdf of the contours is finally segmented by a WS controlled
by markers from a regularization of the initial classification.
Coupled Depth Learning
Authors: Mohammad Haris Baig, Lorenzo Torresani
Date: 2015-01-19 00:00:00
Tasks: Depth Estimation, regression

In this paper we propose a method for estimating depth from a single image
using a coarse to fine approach. We argue that modeling the fine depth details
is easier after a coarse depth map has been computed. We express a global
(coarse) depth map of an image as a linear combination of a depth basis learned
from training examples. The depth basis captures spatial and statistical
regularities and reduces the problem of global depth estimation to the task of
predicting the input-specific coefficients in the linear combination. This is
formulated as a regression problem from a holistic representation of the image.
Crucially, the depth basis and the regression function are {\bf coupled} and
jointly optimized by our learning scheme. We demonstrate that this results in a
significant improvement in accuracy compared to direct regression of depth
pixel values or approaches learning the depth basis disjointly from the
regression function. The global depth estimate is then used as a guidance by a
local refinement method that introduces depth details that were not captured at
the global level. Experiments on the NYUv2 and KITTI datasets show that our
method outperforms the existing state-of-the-art at a considerably lower
computational cost for both training and testing.
Bayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm
Authors: Richard Yi Da Xu, Francois Caron, Arnaud Doucet
Date: 2016-02-09 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation, Unsupervised Image Segmentation

Unsupervised image segmentation aims at clustering the set of pixels of an
image into spatially homogeneous regions. We introduce here a class of Bayesian
nonparametric models to address this problem. These models are based on a
combination of a Potts-like spatial smoothness component and a prior on
partitions which is used to control both the number and size of clusters. This
class of models is flexible enough to include the standard Potts model and the
more recent Potts-Dirichlet Process model \cite{Orbanz2008}. More importantly,
any prior on partitions can be introduced to control the global clustering
structure so that it is possible to penalize small or large clusters if
necessary. Bayesian computation is carried out using an original generalized
Swendsen-Wang algorithm. Experiments demonstrate that our method is competitive
in terms of RAND\ index compared to popular image segmentation methods, such as
mean-shift, and recent alternative Bayesian nonparametric models.
Minimax Lower Bounds for Realizable Transductive Classification
Authors: Ilya Tolstikhin, David Lopez-Paz
Date: 2016-02-09 00:00:00
Tasks: Binary Classification, Classification, General Classification, Inductive Learning, Transductive Learning

Transductive learning considers a training set of $m$ labeled samples and a
test set of $u$ unlabeled samples, with the goal of best labeling that
particular test set. Conversely, inductive learning considers a training set of
$m$ labeled samples drawn iid from $P(X,Y)$, with the goal of best labeling any
future samples drawn iid from $P(X)$. This comparison suggests that
transduction is a much easier type of inference than induction, but is this
really the case? This paper provides a negative answer to this question, by
proving the first known minimax lower bounds for transductive, realizable,
binary classification. Our lower bounds show that $m$ should be at least
$\Omega(d/\epsilon + \log(1/\delta)/\epsilon)$ when $\epsilon$-learning a
concept class $\mathcal{H}$ of finite VC-dimension $d<\infty$ with confidence
$1-\delta$, for all $m \leq u$. This result draws three important conclusions.
First, general transduction is as hard as general induction, since both
problems have $\Omega(d/m)$ minimax values. Second, the use of unlabeled data
does not help general transduction, since supervised learning algorithms such
as ERM and (Hanneke, 2015) match our transductive lower bounds while ignoring
the unlabeled test set. Third, our transductive lower bounds imply lower bounds
for semi-supervised learning, which add to the important discussion about the
role of unlabeled data in machine learning.