pwc-archive/papers-with-abstracts huggingface dataset:
The discriminative Kalman filter for nonlinear and non-Gaussian sequential Bayesian filtering
Authors: Michael C. Burkhart, David M. Brandman, Carlos E. Vargas-Irwin, Matthew T. Harrison
Date: 2016-08-23 00:00:00

The Kalman filter (KF) is used in a variety of applications for computing the
posterior distribution of latent states in a state space model. The model
requires a linear relationship between states and observations. Extensions to
the Kalman filter have been proposed that incorporate linear approximations to
nonlinear models, such as the extended Kalman filter (EKF) and the unscented
Kalman filter (UKF). However, we argue that in cases where the dimensionality
of observed variables greatly exceeds the dimensionality of state variables, a
model for $p(\text{state}|\text{observation})$ proves both easier to learn and
more accurate for latent space estimation. We derive and validate what we call
the discriminative Kalman filter (DKF): a closed-form discriminative version of
Bayesian filtering that readily incorporates off-the-shelf discriminative
learning techniques. Further, we demonstrate that given mild assumptions,
highly non-linear models for $p(\text{state}|\text{observation})$ can be
specified. We motivate and validate on synthetic datasets and in neural
decoding from non-human primates, showing substantial increases in decoding
performance versus the standard Kalman filter.
BreakID: Static Symmetry Breaking for ASP (System Description)
Authors: Jo Devriendt, Bart Bogaerts
Date: 2016-08-30 00:00:00

Symmetry breaking has been proven to be an efficient preprocessing technique
for satisfiability solving (SAT). In this paper, we port the state-of-the-art
SAT symmetry breaker BreakID to answer set programming (ASP). The result is a
lightweight tool that can be plugged in between the grounding and the solving
phases that are common when modelling in ASP. We compare our tool with sbass,
the current state-of-the-art symmetry breaker for ASP.
Multiple penalized principal curves: analysis and computation
Authors: Slav Kirov, Dejan Slepčev
Date: 2015-12-15 00:00:00

We study the problem of finding the one-dimensional structure in a given data
set. In other words we consider ways to approximate a given measure (data) by
curves. We consider an objective functional whose minimizers are a
regularization of principal curves and introduce a new functional which allows
for multiple curves. We prove the existence of minimizers and establish their
basic properties. We develop an efficient algorithm for obtaining (near)
minimizers of the functional. While both of the functionals used are nonconvex,
we argue that enlarging the configuration space to allow for multiple curves
leads to a simpler energy landscape with fewer undesirable (high-energy) local
minima. Furthermore we note that the approach proposed is able to find the
one-dimensional structure even for data with considerable amount of noise.
Multi-Label Classification Method Based on Extreme Learning Machines
Authors: Rajasekar Venkatesan, Meng Joo Er
Date: 2016-08-30 00:00:00
Tasks: Classification, General Classification, Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this paper, an Extreme Learning Machine (ELM) based technique for
Multi-label classification problems is proposed and discussed. In multi-label
classification, each of the input data samples belongs to one or more than one
class labels. The traditional binary and multi-class classification problems
are the subset of the multi-label problem with the number of labels
corresponding to each sample limited to one. The proposed ELM based multi-label
classification technique is evaluated with six different benchmark multi-label
datasets from different domains such as multimedia, text and biology. A
detailed comparison of the results is made by comparing the proposed method
with the results from nine state of the arts techniques for five different
evaluation metrics. The nine methods are chosen from different categories of
multi-label methods. The comparative results shows that the proposed Extreme
Learning Machine based multi-label classification technique is a better
alternative than the existing state of the art methods for multi-label
problems.
Multi-Class Multi-Object Tracking using Changing Point Detection
Authors: Byungjae Lee, Enkhbayar Erdenee, Songguo Jin, Phill Kyu Rhee
Date: 2016-08-30 00:00:00
Tasks: Multi-Object Tracking, Multiple Object Tracking, Object, Object Tracking

This paper presents a robust multi-class multi-object tracking (MCMOT)
formulated by a Bayesian filtering framework. Multi-object tracking for
unlimited object classes is conducted by combining detection responses and
changing point detection (CPD) algorithm. The CPD model is used to observe
abrupt or abnormal changes due to a drift and an occlusion based spatiotemporal
characteristics of track states. The ensemble of convolutional neural network
(CNN) based object detector and Lucas-Kanede Tracker (KLT) based motion
detector is employed to compute the likelihoods of foreground regions as the
detection responses of different object classes. Extensive experiments are
performed using lately introduced challenging benchmark videos; ImageNet VID
and MOT benchmark dataset. The comparison to state-of-the-art video tracking
techniques shows very encouraging results.
Using k-nearest neighbors to construct cancelable minutiae templates
Authors: Qinghai Gao
Date: 2016-08-29 00:00:00

Fingerprint is widely used in a variety of applications. Security measures
have to be taken to protect the privacy of fingerprint data. Cancelable
biometrics is proposed as an effective mechanism of using and protecting
biometrics. In this paper we propose a new method of constructing cancelable
fingerprint template by combining real template with synthetic template.
Specifically, each user is given one synthetic minutia template generated with
random number generator. Every minutia point from the real template is
individually thrown into the synthetic template, from which its k-nearest
neighbors are found. The verification template is constructed by combining an
arbitrary set of the k-nearest neighbors. To prove the validity of the scheme,
testing is carried out on three databases. The results show that the
constructed templates satisfy the requirements of cancelable biometrics.
Limits on Support Recovery with Probabilistic Models: An Information-Theoretic Framework
Authors: Jonathan Scarlett, Volkan Cevher
Date: 2015-01-29 00:00:00
Tasks: Compressive Sensing

The support recovery problem consists of determining a sparse subset of a set
of variables that is relevant in generating a set of observations, and arises
in a diverse range of settings such as compressive sensing, and subset
selection in regression, and group testing. In this paper, we take a unified
approach to support recovery problems, considering general probabilistic models
relating a sparse data vector to an observation vector. We study the
information-theoretic limits of both exact and partial support recovery, taking
a novel approach motivated by thresholding techniques in channel coding. We
provide general achievability and converse bounds characterizing the trade-off
between the error probability and number of measurements, and we specialize
these to the linear, 1-bit, and group testing models. In several cases, our
bounds not only provide matching scaling laws in the necessary and sufficient
number of measurements, but also sharp thresholds with matching constant
factors. Our approach has several advantages over previous approaches: For the
achievability part, we obtain sharp thresholds under broader scalings of the
sparsity level and other parameters (e.g., signal-to-noise ratio) compared to
several previous works, and for the converse part, we not only provide
conditions under which the error probability fails to vanish, but also
conditions under which it tends to one.
Motion Representation with Acceleration Images
Authors: Hirokatsu Kataoka, Yun He, Soma Shirakabe, Yutaka Satoh
Date: 2016-08-30 00:00:00
Tasks: Optical Flow Estimation

Information of time differentiation is extremely important cue for a motion
representation. We have applied first-order differential velocity from a
positional information, moreover we believe that second-order differential
acceleration is also a significant feature in a motion representation. However,
an acceleration image based on a typical optical flow includes motion noises.
We have not employed the acceleration image because the noises are too strong
to catch an effective motion feature in an image sequence. On one hand, the
recent convolutional neural networks (CNN) are robust against input noises. In
this paper, we employ acceleration-stream in addition to the spatial- and
temporal-stream based on the two-stream CNN. We clearly show the effectiveness
of adding the acceleration stream to the two-stream CNN.
Online Action Detection
Authors: Roeland De Geest, Efstratios Gavves, Amir Ghodrati, Zhenyang Li, Cees Snoek, Tinne Tuytelaars
Date: 2016-04-21 00:00:00
Tasks: Action Detection, Online Action Detection

In online action detection, the goal is to detect the start of an action in a
video stream as soon as it happens. For instance, if a child is chasing a ball,
an autonomous car should recognize what is going on and respond immediately.
This is a very challenging problem for four reasons. First, only partial
actions are observed. Second, there is a large variability in negative data.
Third, the start of the action is unknown, so it is unclear over what time
window the information should be integrated. Finally, in real world data, large
within-class variability exists. This problem has been addressed before, but
only to some extent. Our contributions to online action detection are
threefold. First, we introduce a realistic dataset composed of 27 episodes from
6 popular TV series. The dataset spans over 16 hours of footage annotated with
30 action classes, totaling 6,231 action instances. Second, we analyze and
compare various baseline methods, showing this is a challenging problem for
which none of the methods provides a good solution. Third, we analyze the
change in performance when there is a variation in viewpoint, occlusion,
truncation, etc. We introduce an evaluation protocol for fair comparison. The
dataset, the baselines and the models will all be made publicly available to
encourage (much needed) further research on online action detection on
realistic data.
On Structured Sparsity of Phonological Posteriors for Linguistic Parsing
Authors: Milos Cernak, Afsaneh Asaei, Hervé Bourlard
Date: 2016-01-21 00:00:00

The speech signal conveys information on different time scales from short
time scale or segmental, associated to phonological and phonetic information to
long time scale or supra segmental, associated to syllabic and prosodic
information. Linguistic and neurocognitive studies recognize the phonological
classes at segmental level as the essential and invariant representations used
in speech temporal organization. In the context of speech processing, a deep
neural network (DNN) is an effective computational method to infer the
probability of individual phonological classes from a short segment of speech
signal. A vector of all phonological class probabilities is referred to as
phonological posterior. There are only very few classes comprising a short term
speech signal; hence, the phonological posterior is a sparse vector. Although
the phonological posteriors are estimated at segmental level, we claim that
they convey supra-segmental information. Specifically, we demonstrate that
phonological posteriors are indicative of syllabic and prosodic events.
Building on findings from converging linguistic evidence on the gestural model
of Articulatory Phonology as well as the neural basis of speech perception, we
hypothesize that phonological posteriors convey properties of linguistic
classes at multiple time scales, and this information is embedded in their
support (index) of active coefficients. To verify this hypothesis, we obtain a
binary representation of phonological posteriors at the segmental level which
is referred to as first-order sparsity structure; the high-order structures are
obtained by the concatenation of first-order binary vectors. It is then
confirmed that the classification of supra-segmental linguistic events, the
problem known as linguistic parsing, can be achieved with high accuracy using
asimple binary pattern matching of first-order or high-order structures.
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
Authors: Xiao-Jiao Mao, Chunhua Shen, Yu-Bin Yang
Date: 2016-06-29 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, JPEG Artifact Correction, Super-Resolution

Image restoration, including image denoising, super resolution, inpainting,
and so on, is a well-studied problem in computer vision and image processing,
as well as a test bed for low-level image modeling algorithms. In this work, we
propose a very deep fully convolutional auto-encoder network for image
restoration, which is a encoding-decoding framework with symmetric
convolutional-deconvolutional layers. In other words, the network is composed
of multiple layers of convolution and de-convolution operators, learning
end-to-end mappings from corrupted images to the original ones. The
convolutional layers capture the abstraction of image contents while
eliminating corruptions. Deconvolutional layers have the capability to upsample
the feature maps and recover the image details. To deal with the problem that
deeper networks tend to be more difficult to train, we propose to symmetrically
link convolutional and deconvolutional layers with skip-layer connections, with
which the training converges much faster and attains better results.
Incremental Nonlinear System Identification and Adaptive Particle Filtering Using Gaussian Process
Authors: Vahid Bastani, Lucio Marcenaro, Carlo Regazzoni
Date: 2016-08-30 00:00:00
Tasks: State Estimation, State Space Models

An incremental/online state dynamic learning method is proposed for
identification of the nonlinear Gaussian state space models. The method embeds
the stochastic variational sparse Gaussian process as the probabilistic state
dynamic model inside a particle filter framework. Model updating is done at
measurement sample rate using stochastic gradient descent based optimization
implemented in the state estimation filtering loop. The performance of the
proposed method is compared with state-of-the-art Gaussian process based batch
learning methods. Finally, it is shown that the state estimation performance
significantly improves due to the online learning of state dynamics.
American Sign Language fingerspelling recognition from video: Methods for unrestricted recognition and signer-independence
Authors: Taehwan Kim
Date: 2016-08-30 00:00:00

In this thesis, we study the problem of recognizing video sequences of
fingerspelled letters in American Sign Language (ASL). Fingerspelling comprises
a significant but relatively understudied part of ASL, and recognizing it is
challenging for a number of reasons: It involves quick, small motions that are
often highly coarticulated; it exhibits significant variation between signers;
and there has been a dearth of continuous fingerspelling data collected. In
this work, we propose several types of recognition approaches, and explore the
signer variation problem. Our best-performing models are segmental
(semi-Markov) conditional random fields using deep neural network-based
features. In the signer-dependent setting, our recognizers achieve up to about
8% letter error rates. The signer-independent setting is much more challenging,
but with neural network adaptation we achieve up to 17% letter error rates.
Data Dependent Convergence for Distributed Stochastic Optimization
Authors: Avleen S. Bijral
Date: 2016-08-30 00:00:00
Tasks: Stochastic Optimization

In this dissertation we propose alternative analysis of distributed
stochastic gradient descent (SGD) algorithms that rely on spectral properties
of the data covariance. As a consequence we can relate questions pertaining to
speedups and convergence rates for distributed SGD to the data distribution
instead of the regularity properties of the objective functions. More precisely
we show that this rate depends on the spectral norm of the sample covariance
matrix. An estimate of this norm can provide practitioners with guidance
towards a potential gain in algorithm performance. For example many sparse
datasets with low spectral norm prove to be amenable to gains in distributed
settings. Towards establishing this data dependence we first study a
distributed consensus-based SGD algorithm and show that the rate of convergence
involves the spectral norm of the sample covariance matrix when the underlying
data is assumed to be independent and identically distributed (homogenous).
This dependence allows us to identify network regimes that prove to be
beneficial for datasets with low sample covariance spectral norm. Existing
consensus based analyses prove to be sub-optimal in the homogenous setting. Our
analysis method also allows us to find data-dependent convergence rates as we
limit the amount of communication. Spreading a fixed amount of data across more
nodes slows convergence; in the asymptotic regime we show that adding more
machines can help when minimizing twice-differentiable losses. Since the
mini-batch results don't follow from the consensus results we propose a
different data dependent analysis thereby providing theoretical validation for
why certain datasets are more amenable to mini-batching. We also provide
empirical evidence for results in this thesis.
Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions
Authors: Ronghang Hu, Marcus Rohrbach, Subhashini Venugopalan, Trevor Darrell
Date: 2016-08-30 00:00:00
Tasks: Image Captioning, Image Segmentation, Language Modeling, Language Modelling, Segmentation, Semantic Segmentation

Image segmentation from referring expressions is a joint vision and language
modeling task, where the input is an image and a textual expression describing
a particular region in the image; and the goal is to localize and segment the
specific image region based on the given expression. One major difficulty to
train such language-based image segmentation systems is the lack of datasets
with joint vision and text annotations. Although existing vision datasets such
as MS COCO provide image captions, there are few datasets with region-level
textual annotations for images, and these are often smaller in scale. In this
paper, we explore how existing large scale vision-only and text-only datasets
can be utilized to train models for image segmentation from referring
expressions. We propose a method to address this problem, and show in
experiments that our method can help this joint vision and language modeling
task with vision-only and text-only data and outperforms previous results.
Robust Energy Storage Scheduling for Imbalance Reduction of Strategically Formed Energy Balancing Groups
Authors: Shantanu Chakraborty, Toshiya Okabe
Date: 2016-08-30 00:00:00
Tasks: Bayesian Inference, Probabilistic Programming, Scheduling

Imbalance (on-line energy gap between contracted supply and actual demand,
and associated cost) reduction is going to be a crucial service for a Power
Producer and Supplier (PPS) in the deregulated energy market. PPS requires
forward market interactions to procure energy as precisely as possible in order
to reduce imbalance energy. This paper presents, 1) (off-line) an effective
demand aggregation based strategy for creating a number of balancing groups
that leads to higher predictability of group-wise aggregated demand, 2)
(on-line) a robust energy storage scheduling that minimizes the imbalance for a
particular balancing group considering the demand prediction uncertainty. The
group formation is performed by a Probabilistic Programming approach using
Bayesian Markov Chain Monte Carlo (MCMC) method after applied on the historical
demand statistics. Apart from the group formation, the aggregation strategy
(with the help of Bayesian Inference) also clears out the upper-limit of the
required storage capacity for a formed group, fraction of which is to be
utilized in on-line operation. For on-line operation, a robust energy storage
scheduling method is proposed that minimizes expected imbalance energy and cost
(a non-linear function of imbalance energy) while incorporating the demand
uncertainty of a particular group. The proposed methods are applied on the real
apartment buildings' demand data in Tokyo, Japan. Simulation results are
presented to verify the effectiveness of the proposed methods.
Learning and Inferring Relations in Cortical Networks
Authors: Peter U. Diehl, Matthew Cook
Date: 2016-08-29 00:00:00
Tasks: Anatomy

A pressing scientific challenge is to understand how brains work. Of
particular interest is the neocortex,the part of the brain that is especially
large in humans, capable of handling a wide variety of tasks including visual,
auditory, language, motor, and abstract processing. These functionalities are
processed in different self-organized regions of the neocortical sheet, and yet
the anatomical structure carrying out the processing is relatively uniform
across the sheet. We are at a loss to explain, simulate, or understand such a
multi-functional homogeneous sheet-like computational structure - we do not
have computational models which work in this way. Here we present an important
step towards developing such models: we show how uniform modules of excitatory
and inhibitory neurons can be connected bidirectionally in a network that, when
exposed to input in the form of population codes, learns the input encodings as
well as the relationships between the inputs. STDP learning rules lead the
modules to self-organize into a relational network, which is able to infer
missing inputs,restore noisy signals, decide between conflicting inputs, and
combine cues to improve estimates. These networks show that it is possible for
a homogeneous network of spiking units to self-organize so as to provide
meaningful processing of its inputs. If such networks can be scaled up, they
could provide an initial computational model relevant to the large scale
anatomy of the neocortex.
Visualizing and Understanding Sum-Product Networks
Authors: Antonio Vergari, Nicola Di Mauro, Floriana Esposito
Date: 2016-08-29 00:00:00
Tasks: Diagnostic, Representation Learning

Sum-Product Networks (SPNs) are recently introduced deep tractable
probabilistic models by which several kinds of inference queries can be
answered exactly and in a tractable time. Up to now, they have been largely
used as black box density estimators, assessed only by comparing their
likelihood scores only. In this paper we explore and exploit the inner
representations learned by SPNs. We do this with a threefold aim: first we want
to get a better understanding of the inner workings of SPNs; secondly, we seek
additional ways to evaluate one SPN model and compare it against other
probabilistic models, providing diagnostic tools to practitioners; lastly, we
want to empirically evaluate how good and meaningful the extracted
representations are, as in a classic Representation Learning framework. In
order to do so we revise their interpretation as deep neural networks and we
propose to exploit several visualization techniques on their node activations
and network outputs under different types of inference queries. To investigate
these models as feature extractors, we plug some SPNs, learned in a greedy
unsupervised fashion on image datasets, in supervised classification learning
tasks. We extract several embedding types from node activations by filtering
nodes by their type, by their associated feature abstraction level and by their
scope. In a thorough empirical comparison we prove them to be competitive
against those generated from popular feature extractors as Restricted Boltzmann
Machines. Finally, we investigate embeddings generated from random
probabilistic marginal queries as means to compare other tractable
probabilistic models on a common ground, extending our experiments to Mixtures
of Trees.