pwc-archive/papers-with-abstracts huggingface dataset:
Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model
Authors: Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz, Trey Grainger, John A. Miller, Khaled Rasheed, Krys J. Kochut, William S. York, Rene Ranzinger, Melody Porterfield
Date: 2015-12-28 00:00:00
Tasks: BIG-bench Machine Learning

Probabilistic Graphical Models (PGM) are very useful in the fields of machine
learning and data mining. The crucial limitation of those models,however, is
the scalability. The Bayesian Network, which is one of the most common PGMs
used in machine learning and data mining, demonstrates this limitation when the
training data consists of random variables, each of them has a large set of
possible values. In the big data era, one would expect new extensions to the
existing PGMs to handle the massive amount of data produced these days by
computers, sensors and other electronic devices. With hierarchical data - data
that is arranged in a treelike structure with several levels - one would expect
to see hundreds of thousands or millions of values distributed over even just a
small number of levels. When modeling this kind of hierarchical data across
large data sets, Bayesian Networks become infeasible for representing the
probability distributions. In this paper we introduce an extension to Bayesian
Networks to handle massive sets of hierarchical data in a reasonable amount of
time and space. The proposed model achieves perfect precision of 1.0 and high
recall of 0.93 when it is used as multi-label classifier for the annotation of
mass spectrometry data. On another data set of 1.5 billion search logs provided
by CareerBuilder.com the model was able to predict latent semantic
relationships between search keywords with accuracy up to 0.80.
A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data
Authors: Hongjian Wang, Zhenhui Li, Yu-Hsuan Kuo, Dan Kifer
Date: 2015-12-28 00:00:00
Tasks: Travel Time Estimation

The increased availability of large-scale trajectory data around the world
provides rich information for the study of urban dynamics. For example, New
York City Taxi Limousine Commission regularly releases source-destination
information about trips in the taxis they regulate. Taxi data provide
information about traffic patterns, and thus enable the study of urban flow --
what will traffic between two locations look like at a certain date and time in
the future? Existing big data methods try to outdo each other in terms of
complexity and algorithmic sophistication. In the spirit of "big data beats
algorithms", we present a very simple baseline which outperforms
state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs
permit large scale experimentation). Such a travel time estimation baseline has
several important uses, such as navigation (fast travel time estimates can
serve as approximate heuristics for A search variants for path finding) and
trip planning (which uses operating hours for popular destinations along with
travel time estimates to create an itinerary).
Approximate Hubel-Wiesel Modules and the Data Structures of Neural Computation
Authors: Joel Z. Leibo, Julien Cornebise, Sergio Gómez, Demis Hassabis
Date: 2015-12-28 00:00:00
Tasks: Hippocampus

This paper describes a framework for modeling the interface between
perception and memory on the algorithmic level of analysis. It is consistent
with phenomena associated with many different brain regions. These include
view-dependence (and invariance) effects in visual psychophysics and
inferotemporal cortex physiology, as well as episodic memory recall
interference effects associated with the medial temporal lobe. The perspective
developed here relies on a novel interpretation of Hubel and Wiesel's
conjecture for how receptive fields tuned to complex objects, and invariant to
details, could be achieved. It complements existing accounts of two-speed
learning systems in neocortex and hippocampus (e.g., McClelland et al. 1995)
while significantly expanding their scope to encompass a unified view of the
entire pathway from V1 to hippocampus.
Graph entropies in texture segmentation of images
Authors: Martin Welk
Date: 2015-12-28 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Texture Classification

We study the applicability of a set of texture descriptors introduced in
recent work by the author to texture-based segmentation of images. The texture
descriptors under investigation result from applying graph indices from
quantitative graph theory to graphs encoding the local structure of images. The
underlying graphs arise from the computation of morphological amoebas as
structuring elements for adaptive morphology, either as weighted or unweighted
Dijkstra search trees or as edge-weighted pixel graphs within structuring
elements. In the present paper we focus on texture descriptors in which the
graph indices are entropy-based, and use them in a geodesic active contour
framework for image segmentation. Experiments on several synthetic and one
real-world image are shown to demonstrate texture segmentation by this
approach. Forthermore, we undertake an attempt to analyse selected
entropy-based texture descriptors with regard to what information about texture
they actually encode. Whereas this analysis uses some heuristic assumptions, it
indicates that the graph-based texture descriptors are related to fractal
dimension measures that have been proven useful in texture analysis.
Ordered Decompositional DAG Kernels Enhancements
Authors: Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
Date: 2015-07-13 00:00:00
Tasks: General Classification

In this paper, we show how the Ordered Decomposition DAGs (ODD) kernel
framework, a framework that allows the definition of graph kernels from tree
kernels, allows to easily define new state-of-the-art graph kernels. Here we
consider a fast graph kernel based on the Subtree kernel (ST), and we propose
various enhancements to increase its expressiveness. The proposed DAG kernel
has the same worst-case complexity as the one based on ST, but an improved
expressivity due to an augmented set of features. Moreover, we propose a novel
weighting scheme for the features, which can be applied to other kernels of the
ODD framework. These improvements allow the proposed kernels to improve on the
classification performances of the ST-based kernel for several real-world
datasets, reaching state-of-the-art performances.
Outlier Detection In Large-scale Traffic Data By Naïve Bayes Method and Gaussian Mixture Model Method
Authors: Philip Lam, Li-Li Wang, Henry Y. T. Ngan, Nelson H. C. Yung, Anthony G. O. Yeh
Date: 2015-12-28 00:00:00
Tasks: Dimensionality Reduction, Management, Outlier Detection

It is meaningful to detect outliers in traffic data for traffic management.
However, this is a massive task for people from large-scale database to
distinguish outliers. In this paper, we present two methods: Kernel Smoothing
Na\"ive Bayes (NB) method and Gaussian Mixture Model (GMM) method to
automatically detect any hardware errors as well as abnormal traffic events in
traffic data collected at a four-arm junction in Hong Kong. Traffic data was
recorded in a video format, and converted to spatial-temporal (ST) traffic
signals by statistics. The ST signals are then projected to a two-dimensional
(2D) (x,y)-coordinate plane by Principal Component Analysis (PCA) for dimension
reduction. We assume that inlier data are normal distributed. As such, the NB
and GMM methods are successfully applied in outlier detection (OD) for traffic
data. The kernel smooth NB method assumes the existence of kernel distributions
in traffic data and uses Bayes' Theorem to perform OD. In contrast, the GMM
method believes the traffic data is formed by the mixture of Gaussian
distributions and exploits confidence region for OD. This paper would address
the modeling of each method and evaluate their respective performances.
Experimental results show that the NB algorithm with Triangle kernel and GMM
method achieve up to 93.78% and 94.50% accuracies, respectively.
Automatic Incident Classification for Big Traffic Data by Adaptive Boosting SVM
Authors: Li-Li Wang, Henry Y. T. Ngan, Nelson H. C. Yung
Date: 2015-12-14 00:00:00
Tasks: Classification, General Classification

Modern cities experience heavy traffic flows and congestions regularly across
space and time. Monitoring traffic situations becomes an important challenge
for the Traffic Control and Surveillance Systems (TCSS). In advanced TCSS, it
is helpful to automatically detect and classify different traffic incidents
such as severity of congestion, abnormal driving pattern, abrupt or illegal
stop on road, etc. Although most TCSS are equipped with basic incident
detection algorithms, they are however crude to be really useful as an
automated tool for further classification. In literature, there is a lack of
research for Automated Incident Classification (AIC). Therefore, a novel AIC
method is proposed in this paper to tackle such challenges. In the proposed
method, traffic signals are firstly extracted from captured videos and
converted as spatial-temporal (ST) signals. Based on the characteristics of the
ST signals, a set of realistic simulation data are generated to construct an
extended big traffic database to cover a variety of traffic situations. Next, a
Mean-Shift filter is introduced to suppress the effect of noise and extract
significant features from the ST signals. The extracted features are then
associated with various types of traffic data: one normal type (inliers) and
multiple abnormal types (outliers). For the classification, an adaptive
boosting classifier is trained to detect outliers in traffic data
automatically. Further, a Support Vector Machine (SVM) based method is adopted
to train the model for identifying the categories of outliers. In short, this
hybrid approach is called an Adaptive Boosting Support Vector Machines (AB-SVM)
method. Experimental results show that the proposed AB-SVM method achieves a
satisfied result with more than 92% classification accuracy on average.
Linear-time Online Action Detection From 3D Skeletal Data Using Bags of Gesturelets
Authors: Moustafa Meshry, Mohamed E. Hussein, Marwan Torki
Date: 2015-02-04 00:00:00
Tasks: Action Detection, Action Recognition, Online Action Detection, Temporal Action Localization

Sliding window is one direct way to extend a successful recognition system to
handle the more challenging detection problem. While action recognition decides
only whether or not an action is present in a pre-segmented video sequence,
action detection identifies the time interval where the action occurred in an
unsegmented video stream. Sliding window approaches for action detection can
however be slow as they maximize a classifier score over all possible
sub-intervals. Even though new schemes utilize dynamic programming to speed up
the search for the optimal sub-interval, they require offline processing on the
whole video sequence. In this paper, we propose a novel approach for online
action detection based on 3D skeleton sequences extracted from depth data. It
identifies the sub-interval with the maximum classifier score in linear time.
Furthermore, it is invariant to temporal scale variations and is suitable for
real-time applications with low latency.
Using Causal Discovery to Track Information Flow in Spatio-Temporal Data - A Testbed and Experimental Results Using Advection-Diffusion Simulations
Authors: Imme Ebert-Uphoff, Yi Deng
Date: 2015-12-27 00:00:00
Tasks: Causal Discovery, scientific discovery

Causal discovery algorithms based on probabilistic graphical models have
emerged in geoscience applications for the identification and visualization of
dynamical processes. The key idea is to learn the structure of a graphical
model from observed spatio-temporal data, which indicates information flow,
thus pathways of interactions, in the observed physical system. Studying those
pathways allows geoscientists to learn subtle details about the underlying
dynamical mechanisms governing our planet. Initial studies using this approach
on real-world atmospheric data have shown great potential for scientific
discovery. However, in these initial studies no ground truth was available, so
that the resulting graphs have been evaluated only by whether a domain expert
thinks they seemed physically plausible. This paper seeks to fill this gap. We
develop a testbed that emulates two dynamical processes dominant in many
geoscience applications, namely advection and diffusion, in a 2D grid. Then we
apply the causal discovery based information tracking algorithms to the
simulation data to study how well the algorithms work for different scenarios
and to gain a better understanding of the physical meaning of the graph
results, in particular of instantaneous connections. We make all data sets used
in this study available to the community as a benchmark.
  Keywords: Information flow, graphical model, structure learning, causal
discovery, geoscience.
Statistical and Computational Guarantees for the Baum-Welch Algorithm
Authors: Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright
Date: 2015-12-27 00:00:00
Tasks: Econometrics, speech-recognition, Speech Recognition, Time Series, Time Series Analysis

The Hidden Markov Model (HMM) is one of the mainstays of statistical modeling
of discrete time series, with applications including speech recognition,
computational biology, computer vision and econometrics. Estimating an HMM from
its observation process is often addressed via the Baum-Welch algorithm, which
is known to be susceptible to local optima. In this paper, we first give a
general characterization of the basin of attraction associated with any global
optimum of the population likelihood. By exploiting this characterization, we
provide non-asymptotic finite sample guarantees on the Baum-Welch updates,
guaranteeing geometric convergence to a small ball of radius on the order of
the minimax rate around a global optimum. As a concrete example, we prove a
linear rate of convergence for a hidden Markov mixture of two isotropic
Gaussians given a suitable mean separation and an initialization within a ball
of large radius around (one of) the true parameters. To our knowledge, these
are the first rigorous local convergence guarantees to global optima for the
Baum-Welch algorithm in a setting where the likelihood function is nonconvex.
We complement our theoretical results with thorough numerical simulations
studying the convergence of the Baum-Welch algorithm and illustrating the
accuracy of our predictions.
Sequence to Sequence Learning for Optical Character Recognition
Authors: Devendra Kumar Sahu, Mohak Sukhwani
Date: 2015-11-13 00:00:00
Tasks: Decoder, General Classification, Optical Character Recognition, Optical Character Recognition (OCR), Retrieval

We propose an end-to-end recurrent encoder-decoder based sequence learning
approach for printed text Optical Character Recognition (OCR). In contrast to
present day existing state-of-art OCR solution which uses connectionist
temporal classification (CTC) output layer, our approach makes minimalistic
assumptions on the structure and length of the sequence. We use a two step
encoder-decoder approach -- (a) A recurrent encoder reads a variable length
printed text word image and encodes it to a fixed dimensional embedding. (b)
This fixed dimensional embedding is subsequently comprehended by decoder
structure which converts it into a variable length text output. Our
architecture gives competitive performance relative to connectionist temporal
classification (CTC) output layer while being executed in more natural
settings. The learnt deep word image embedding from encoder can be used for
printed text based retrieval systems. The expressive fixed dimensional
embedding for any variable length input expedites the task of retrieval and
makes it more efficient which is not possible with other recurrent neural
network architectures. We empirically investigate the expressiveness and the
learnability of long short term memory (LSTMs) in the sequence to sequence
learning regime by training our network for prediction tasks in segmentation
free printed text OCR. The utility of the proposed architecture for printed
text is demonstrated by quantitative and qualitative evaluation of two tasks --
word prediction and retrieval.
New Perspectives on $k$-Support and Cluster Norms
Authors: Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
Date: 2015-12-27 00:00:00
Tasks: Matrix Completion

We study a regularizer which is defined as a parameterized infimum of
quadratics, and which we call the box-norm. We show that the k-support norm, a
regularizer proposed by [Argyriou et al, 2012] for sparse vector prediction
problems, belongs to this family, and the box-norm can be generated as a
perturbation of the former. We derive an improved algorithm to compute the
proximity operator of the squared box-norm, and we provide a method to compute
the norm. We extend the norms to matrices, introducing the spectral k-support
norm and spectral box-norm. We note that the spectral box-norm is essentially
equivalent to the cluster norm, a multitask learning regularizer introduced by
[Jacob et al. 2009a], and which in turn can be interpreted as a perturbation of
the spectral k-support norm. Centering the norm is important for multitask
learning and we also provide a method to use centered versions of the norms as
regularizers. Numerical experiments indicate that the spectral k-support and
box-norms and their centered variants provide state of the art performance in
matrix completion and multitask learning problems respectively.
Mixed Gaussian-Impulse Noise Removal from Highly Corrupted Images via Adaptive Local and Nonlocal Statistical Priors
Authors: Nasser Eslahi, Hami Mahdavinataj, Ali Aghagolzadeh
Date: 2015-08-29 00:00:00
Tasks: Denoising

The motivation of this paper is to introduce a novel framework for the
restoration of images corrupted by mixed Gaussian-impulse noise. To this aim,
first, an adaptive curvelet thresholding criterion is proposed which tries to
adaptively remove the perturbations appeared during denoising process. Then, a
new statistical regularization term, called joint adaptive statistical prior
(JASP), is established which enforces both the local and nonlocal statistical
consistencies, simultaneously, in a unified manner. Furthermore, a novel
technique for mixed Gaussian plus impulse noise removal using JASP in a
variational scheme is developed--we refer to it as De-JASP. To efficiently
solve the above variational scheme, an efficient alternating minimization
algorithm based on split Bregman iterative framework is developed. Extensive
experimental results manifest the effectiveness of the proposed method
comparing with the current state-of-the-art methods in mixed Gaussian-impulse
noise removal.
Electricity Demand Forecasting by Multi-Task Learning
Authors: Jean-Baptiste Fiot, Francesco Dinuzzo
Date: 2015-12-27 00:00:00
Tasks: Additive models, Demand Forecasting, Multi-Task Learning

We explore the application of kernel-based multi-task learning techniques to
forecast the demand of electricity in multiple nodes of a distribution network.
We show that recently developed output kernel learning techniques are
particularly well suited to solve this problem, as they allow to flexibly model
the complex seasonal effects that characterize electricity demand data, while
learning and exploiting correlations between multiple demand profiles. We also
demonstrate that kernels with a multiplicative structure yield superior
predictive performance with respect to the widely adopted (generalized)
additive models. Our study is based on residential and industrial smart meter
data provided by the Irish Commission for Energy Regulation (CER).
Self-Excitation: An Enabler for Online Thermal Estimation and Model Predictive Control of Buildings
Authors: Peter Radecki, Brandon Hencey
Date: 2015-12-27 00:00:00
Tasks: Active Learning, Model Predictive Control

This paper investigates a method to improve buildings' thermal predictive
control performance via online identification and excitation (active learning
process) that minimally disrupts normal operations. In previous studies we have
demonstrated scalable methods to acquire multi-zone thermal models of passive
buildings using a gray-box approach that leverages building topology and
measurement data. Here we extend the method to multi-zone actively controlled
buildings and examine how to improve the thermal model estimation by using the
controller to excite unknown portions of the building's dynamics. Comparing
against a baseline thermostat controller, we demonstrate the utility of both
the initially acquired and improved thermal models within a Model Predictive
Control (MPC) framework, which anticipates weather uncertainty and time-varying
temperature set-points. A simulation study demonstrates self-excitation
improves model estimation, which corresponds to improved MPC energy savings and
occupant comfort. By coupling building topology, estimation, and control
routines into a single online framework, we have demonstrated the potential for
low-cost scalable methods to actively learn and control buildings to ensure
occupant comfort and minimize energy usage, all while using the existing
building's HVAC sensors and hardware.
Online Model Estimation for Predictive Thermal Control of Buildings
Authors: Peter Radecki, Brandon Hencey
Date: 2015-12-27 00:00:00
Tasks: Model Predictive Control, parameter estimation

This study proposes a general, scalable method to learn control-oriented
thermal models of buildings that could enable wide-scale deployment of
cost-effective predictive controls. An Unscented Kalman Filter augmented for
parameter and disturbance estimation is shown to accurately learn and predict a
building's thermal response. Recent studies of heating, ventilating, and air
conditioning (HVAC) systems have shown significant energy savings with advanced
model predictive control (MPC). A scalable cost-effective method to readily
acquire accurate, robust models of individual buildings' unique thermal
envelopes has historically been elusive and hindered the widespread deployment
of prediction-based control systems. Continuous commissioning and lifetime
performance of these thermal models requires deployment of on-line data-driven
system identification and parameter estimation routines. We propose a novel
gray-box approach using an Unscented Kalman Filter based on a multi-zone
thermal network and validate it with EnergyPlus simulation data. The filter
quickly learns parameters of a thermal network during periods of known or
constrained loads and then characterizes unknown loads in order to provide
accurate 24+ hour energy predictions. This study extends our initial
investigation by formalizing parameter and disturbance estimation routines and
demonstrating results across a year-long study.
Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models
Authors: Calvin Mccarter, Seyoung Kim
Date: 2015-09-15 00:00:00

This paper addresses the problem of scalable optimization for L1-regularized
conditional Gaussian graphical models. Conditional Gaussian graphical models
generalize the well-known Gaussian graphical models to conditional
distributions to model the output network influenced by conditioning input
variables. While highly scalable optimization methods exist for sparse Gaussian
graphical model estimation, state-of-the-art methods for conditional Gaussian
graphical models are not efficient enough and more importantly, fail due to
memory constraints for very large problems. In this paper, we propose a new
optimization procedure based on a Newton method that efficiently iterates over
two sub-problems, leading to drastic improvement in computation time compared
to the previous methods. We then extend our method to scale to large problems
under memory constraints, using block coordinate descent to limit memory usage
while achieving fast convergence. Using synthetic and genomic data, we show
that our methods can solve one million dimensional problems to high accuracy in
a little over a day on a single machine.