pwc-archive/papers-with-abstracts huggingface dataset:
Learning Bounded Treewidth Bayesian Networks with Thousands of Variables
Authors: Mauro Scanagatta, Giorgio Corani, Cassio P. de Campos, Marco Zaffalon
Date: 2016-05-11 00:00:00

We present a method for learning treewidth-bounded Bayesian networks from
data sets containing thousands of variables. Bounding the treewidth of a
Bayesian greatly reduces the complexity of inferences. Yet, being a global
property of the graph, it considerably increases the difficulty of the learning
process. We propose a novel algorithm for this task, able to scale to large
domains and large treewidths. Our novel approach consistently outperforms the
state of the art on data sets with up to ten thousand variables.
Ultimate Intelligence Part II: Physical Measure and Complexity of Intelligence
Authors: Eray Özkural
Date: 2015-04-09 00:00:00

We continue our analysis of volume and energy measures that are appropriate
for quantifying inductive inference systems. We extend logical depth and
conceptual jump size measures in AIT to stochastic problems, and physical
measures that involve volume and energy. We introduce a graphical model of
computational complexity that we believe to be appropriate for intelligent
machines. We show several asymptotic relations between energy, logical depth
and volume of computation for inductive inference. In particular, we arrive at
a "black-hole equation" of inductive inference, which relates energy, volume,
space, and algorithmic information for an optimal inductive inference solution.
We introduce energy-bounded algorithmic entropy. We briefly apply our ideas to
the physical limits of intelligent computation in our universe.
Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration
Authors: Jiaolong Yang, Hongdong Li, Dylan Campbell, Yunde Jia
Date: 2016-05-11 00:00:00
Tasks: Image to Point Cloud Registration, Point Cloud Registration

The Iterative Closest Point (ICP) algorithm is one of the most widely used
methods for point-set registration. However, being based on local iterative
optimization, ICP is known to be susceptible to local minima. Its performance
critically relies on the quality of the initialization and only local
optimality is guaranteed. This paper presents the first globally optimal
algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D
point-sets under the L2 error metric defined in ICP. The Go-ICP method is based
on a branch-and-bound (BnB) scheme that searches the entire 3D motion space
SE(3). By exploiting the special structure of SE(3) geometry, we derive novel
upper and lower bounds for the registration error function. Local ICP is
integrated into the BnB scheme, which speeds up the new method while
guaranteeing global optimality. We also discuss extensions, addressing the
issue of outlier robustness. The evaluation demonstrates that the proposed
method is able to produce reliable registration results regardless of the
initialization. Go-ICP can be applied in scenarios where an optimal solution is
desirable or where a good initialization is not always available.
Asymptotic properties for combined $L_1$ and concave regularization
Authors: Yingying Fan, Jinchi Lv
Date: 2016-05-11 00:00:00
Tasks: Variable Selection

Two important goals of high-dimensional modeling are prediction and variable
selection. In this article, we consider regularization with combined $L_1$ and
concave penalties, and study the sampling properties of the global optimum of
the suggested method in ultra-high dimensional settings. The $L_1$-penalty
provides the minimum regularization needed for removing noise variables in
order to achieve oracle prediction risk, while concave penalty imposes
additional regularization to control model sparsity. In the linear model
setting, we prove that the global optimum of our method enjoys the same oracle
inequalities as the lasso estimator and admits an explicit bound on the false
sign rate, which can be asymptotically vanishing. Moreover, we establish oracle
risk inequalities for the method and the sampling properties of computable
solutions. Numerical studies suggest that our method yields more stable
estimates than using a concave penalty alone.
A robust particle detection algorithm based on symmetry
Authors: Alvaro Rodriguez, Hanqing Zhang, Krister Wiklund, Tomas Brodin, Jonatan Klaminder, Patrik Andersson, Magnus Andersson
Date: 2016-05-11 00:00:00
Tasks: Position

Particle tracking is common in many biophysical, ecological, and
micro-fluidic applications. Reliable tracking information is heavily dependent
on of the system under study and algorithms that correctly determines particle
position between images. However, in a real environmental context with the
presence of noise including particular or dissolved matter in water, and low
and fluctuating light conditions, many algorithms fail to obtain reliable
information. We propose a new algorithm, the Circular Symmetry algorithm
(C-Sym), for detecting the position of a circular particle with high accuracy
and precision in noisy conditions. The algorithm takes advantage of the spatial
symmetry of the particle allowing for subpixel accuracy. We compare the
proposed algorithm with four different methods using both synthetic and
experimental datasets. The results show that C-Sym is the most accurate and
precise algorithm when tracking micro-particles in all tested conditions and it
has the potential for use in applications including tracking biota in their
environment.
Unsupervised Semantic Action Discovery from Video Collections
Authors: Ozan Sener, Amir Roshan Zamir, Chenxia Wu, Silvio Savarese, Ashutosh Saxena
Date: 2016-05-11 00:00:00

Human communication takes many forms, including speech, text and
instructional videos. It typically has an underlying structure, with a starting
point, ending, and certain objective steps between them. In this paper, we
consider instructional videos where there are tens of millions of them on the
Internet.
  We propose a method for parsing a video into such semantic steps in an
unsupervised way. Our method is capable of providing a semantic "storyline" of
the video composed of its objective steps. We accomplish this using both visual
and language cues in a joint generative model. Our method can also provide a
textual description for each of the identified semantic steps and video
segments. We evaluate our method on a large number of complex YouTube videos
and show that our method discovers semantically correct instructions for a
variety of tasks.
Tuning parameter selection in high dimensional penalized likelihood
Authors: Yingying Fan, Cheng Yong Tang
Date: 2016-05-11 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Determining how to appropriately select the tuning parameter is essential in
penalized likelihood methods for high-dimensional data analysis. We examine
this problem in the setting of penalized likelihood methods for generalized
linear models, where the dimensionality of covariates p is allowed to increase
exponentially with the sample size n. We propose to select the tuning parameter
by optimizing the generalized information criterion (GIC) with an appropriate
model complexity penalty. To ensure that we consistently identify the true
model, a range for the model complexity penalty is identified in GIC. We find
that this model complexity penalty should diverge at the rate of some power of
$\log p$ depending on the tail probability behavior of the response variables.
This reveals that using the AIC or BIC to select the tuning parameter may not
be adequate for consistently identifying the true model. Based on our
theoretical study, we propose a uniform choice of the model complexity penalty
and show that the proposed approach consistently identifies the true model
among candidate models with asymptotic probability one. We justify the
performance of the proposed procedure by numerical simulations and a gene
expression data analysis.
Interaction pursuit in high-dimensional multi-response regression via distance correlation
Authors: Yinfei Kong, Daoji Li, Yingying Fan, Jinchi Lv
Date: 2016-05-11 00:00:00
Tasks: feature selection, regression, Vocal Bursts Intensity Prediction

Feature interactions can contribute to a large proportion of variation in
many prediction models. In the era of big data, the coexistence of high
dimensionality in both responses and covariates poses unprecedented challenges
in identifying important interactions. In this paper, we suggest a two-stage
interaction identification method, called the interaction pursuit via distance
correlation (IPDC), in the setting of high-dimensional multi-response
interaction models that exploits feature screening applied to transformed
variables with distance correlation followed by feature selection. Such a
procedure is computationally efficient, generally applicable beyond the
heredity assumption, and effective even when the number of responses diverges
with the sample size. Under mild regularity conditions, we show that this
method enjoys nice theoretical properties including the sure screening
property, support union recovery, and oracle inequalities in prediction and
estimation for both interactions and main effects. The advantages of our method
are supported by several simulation studies and real data analysis.
Innovated scalable efficient estimation in ultra-large Gaussian graphical models
Authors: Yingying Fan, Jinchi Lv
Date: 2016-05-11 00:00:00

Large-scale precision matrix estimation is of fundamental importance yet
challenging in many contemporary applications for recovering Gaussian graphical
models. In this paper, we suggest a new approach of innovated scalable
efficient estimation (ISEE) for estimating large precision matrix. Motivated by
the innovated transformation, we convert the original problem into that of
large covariance matrix estimation. The suggested method combines the strengths
of recent advances in high-dimensional sparse modeling and large covariance
matrix estimation. Compared to existing approaches, our method is scalable and
can deal with much larger precision matrices with simple tuning. Under mild
regularity conditions, we establish that this procedure can recover the
underlying graphical structure with significant probability and provide
efficient estimation of link strengths. Both computational and theoretical
advantages of the procedure are evidenced through simulation and real data
examples.
The constrained Dantzig selector with enhanced consistency
Authors: Yinfei Kong, Zemin Zheng, Jinchi Lv
Date: 2016-05-11 00:00:00
Tasks: compressed sensing, Computational Efficiency

The Dantzig selector has received popularity for many applications such as
compressed sensing and sparse modeling, thanks to its computational efficiency
as a linear programming problem and its nice sampling properties. Existing
results show that it can recover sparse signals mimicking the accuracy of the
ideal procedure, up to a logarithmic factor of the dimensionality. Such a
factor has been shown to hold for many regularization methods. An important
question is whether this factor can be reduced to a logarithmic factor of the
sample size in ultra-high dimensions under mild regularity conditions. To
provide an affirmative answer, in this paper we suggest the constrained Dantzig
selector, which has more flexible constraints and parameter space. We prove
that the suggested method can achieve convergence rates within a logarithmic
factor of the sample size of the oracle rates and improved sparsity, under a
fairly weak assumption on the signal strength. Such improvement is significant
in ultra-high dimensions. This method can be implemented efficiently through
sequential linear programming. Numerical studies confirm that the sample size
needed for a certain level of accuracy in these problems can be much reduced.
Asymptotic equivalence of regularization methods in thresholded parameter space
Authors: Yingying Fan, Jinchi Lv
Date: 2016-05-11 00:00:00
Tasks: Variable Selection

High-dimensional data analysis has motivated a spectrum of regularization
methods for variable selection and sparse modeling, with two popular classes of
convex ones and concave ones. A long debate has been on whether one class
dominates the other, an important question both in theory and to practitioners.
In this paper, we characterize the asymptotic equivalence of regularization
methods, with general penalty functions, in a thresholded parameter space under
the generalized linear model setting, where the dimensionality can grow up to
exponentially with the sample size. To assess their performance, we establish
the oracle inequalities, as in Bickel, Ritov and Tsybakov (2009), of the global
minimizer for these methods under various prediction and variable selection
losses. These results reveal an interesting phase transition phenomenon. For
polynomially growing dimensionality, the $L_1$-regularization method of Lasso
and concave methods are asymptotically equivalent, having the same convergence
rates in the oracle inequalities. For exponentially growing dimensionality,
concave methods are asymptotically equivalent but have faster convergence rates
than the Lasso. We also establish a stronger property of the oracle risk
inequalities of the regularization methods, as well as the sampling properties
of computable solutions. Our new theoretical results are illustrated and
justified by simulation and real data examples.
High dimensional thresholded regression and shrinkage effect
Authors: Zemin Zheng, Yingying Fan, Jinchi Lv
Date: 2016-05-11 00:00:00
Tasks: Prediction, regression, Variable Selection, Vocal Bursts Intensity Prediction

High-dimensional sparse modeling via regularization provides a powerful tool
for analyzing large-scale data sets and obtaining meaningful, interpretable
models. The use of nonconvex penalty functions shows advantage in selecting
important features in high dimensions, but the global optimality of such
methods still demands more understanding. In this paper, we consider sparse
regression with hard-thresholding penalty, which we show to give rise to
thresholded regression. This approach is motivated by its close connection with
the $L_0$-regularization, which can be unrealistic to implement in practice but
of appealing sampling properties, and its computational advantage. Under some
mild regularity conditions allowing possibly exponentially growing
dimensionality, we establish the oracle inequalities of the resulting
regularized estimator, as the global minimizer, under various prediction and
variable selection losses, as well as the oracle risk inequalities of the
hard-thresholded estimator followed by a further $L_2$-regularization. The risk
properties exhibit interesting shrinkage effects under both estimation and
prediction losses. We identify the optimal choice of the ridge parameter, which
is shown to have simultaneous advantages to both the $L_2$-loss and prediction
loss. These new results and phenomena are evidenced by simulation and real data
examples.
Minimizing Finite Sums with the Stochastic Average Gradient
Authors: Mark Schmidt, Nicolas Le Roux, Francis Bach
Date: 2013-09-10 00:00:00

We propose the stochastic average gradient (SAG) method for optimizing the
sum of a finite number of smooth convex functions. Like stochastic gradient
(SG) methods, the SAG method's iteration cost is independent of the number of
terms in the sum. However, by incorporating a memory of previous gradient
values the SAG method achieves a faster convergence rate than black-box SG
methods. The convergence rate is improved from O(1/k^{1/2}) to O(1/k) in
general, and when the sum is strongly-convex the convergence rate is improved
from the sub-linear O(1/k) to a linear convergence rate of the form O(p^k) for
p \textless{} 1. Further, in many cases the convergence rate of the new method
is also faster than black-box deterministic gradient methods, in terms of the
number of gradient evaluations. Numerical experiments indicate that the new
algorithm often dramatically outperforms existing SG and deterministic gradient
methods, and that the performance may be further improved through the use of
non-uniform sampling strategies.
Multi-Scale Convolutional Neural Networks for Time Series Classification
Authors: Zhicheng Cui, Wenlin Chen, Yixin Chen
Date: 2016-03-22 00:00:00
Tasks: Classification, Dynamic Time Warping, General Classification, GPU, Time Series, Time Series Analysis, Time Series Classification

Time series classification (TSC), the problem of predicting class labels of
time series, has been around for decades within the community of data mining
and machine learning, and found many important applications such as biomedical
engineering and clinical prediction. However, it still remains challenging and
falls short of classification accuracy and efficiency. Traditional approaches
typically involve extracting discriminative features from the original time
series using dynamic time warping (DTW) or shapelet transformation, based on
which an off-the-shelf classifier can be applied. These methods are ad-hoc and
separate the feature extraction part with the classification part, which limits
their accuracy performance. Plus, most existing methods fail to take into
account the fact that time series often have features at different time scales.
To address these problems, we propose a novel end-to-end neural network model,
Multi-Scale Convolutional Neural Networks (MCNN), which incorporates feature
extraction and classification in a single framework. Leveraging a novel
multi-branch layer and learnable convolutional layers, MCNN automatically
extracts features at different scales and frequencies, leading to superior
feature representation. MCNN is also computationally efficient, as it naturally
leverages GPU computing. We conduct comprehensive empirical evaluation with
various existing methods on a large number of benchmark datasets, and show that
MCNN advances the state-of-the-art by achieving superior accuracy performance
than other leading methods.
A Hierarchical Emotion Regulated Sensorimotor Model: Case Studies
Authors: Junpei Zhong, Rony Novianto, Mingjun Dai, Xinzheng Zhang, Angelo Cangelosi
Date: 2016-05-11 00:00:00
Tasks: model

Inspired by the hierarchical cognitive architecture and the perception-action
model (PAM), we propose that the internal status acts as a kind of
common-coding representation which affects, mediates and even regulates the
sensorimotor behaviours. These regulation can be depicted in the Bayesian
framework, that is why cognitive agents are able to generate behaviours with
subtle differences according to their emotion or recognize the emotion by
perception. A novel recurrent neural network called recurrent neural network
with parametric bias units (RNNPB) runs in three modes, constructing a
two-level emotion regulated learning model, was further applied to testify this
theory in two different cases.
Learning to decompose for object detection and instance segmentation
Authors: Eunbyung Park, Alexander C. Berg
Date: 2015-11-19 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Segmentation, Semantic Segmentation

Although deep convolutional neural networks(CNNs) have achieved remarkable
results on object detection and segmentation, pre- and post-processing steps
such as region proposals and non-maximum suppression(NMS), have been required.
These steps result in high computational complexity and sensitivity to
hyperparameters, e.g. thresholds for NMS. In this work, we propose a novel
end-to-end trainable deep neural network architecture, which consists of
convolutional and recurrent layers, that generates the correct number of object
instances and their bounding boxes (or segmentation masks) given an image,
using only a single network evaluation without any pre- or post-processing
steps. We have tested on detecting digits in multi-digit images synthesized
using MNIST, automatically segmenting digits in these images, and detecting
cars in the KITTI benchmark dataset. The proposed approach outperforms a strong
CNN baseline on the synthesized digits datasets and shows promising results on
KITTI car detection.
Sensorimotor Input as a Language Generalisation Tool: A Neurorobotics Model for Generation and Generalisation of Noun-Verb Combinations with Sensorimotor Inputs
Authors: Junpei Zhong, Martin Peniak, Jun Tani, Tetsuya OGATA, Angelo Cangelosi
Date: 2016-05-11 00:00:00
Tasks: Language Acquisition, Sentence

The paper presents a neurorobotics cognitive model to explain the
understanding and generalisation of nouns and verbs combinations when a vocal
command consisting of a verb-noun sentence is provided to a humanoid robot.
This generalisation process is done via the grounding process: different
objects are being interacted, and associated, with different motor behaviours,
following a learning approach inspired by developmental language acquisition in
infants. This cognitive model is based on Multiple Time-scale Recurrent Neural
Networks (MTRNN).With the data obtained from object manipulation tasks with a
humanoid robot platform, the robotic agent implemented with this model can
ground the primitive embodied structure of verbs through training with
verb-noun combination samples. Moreover, we show that a functional hierarchical
architecture, based on MTRNN, is able to generalise and produce novel
combinations of noun-verb sentences. Further analyses of the learned network
dynamics and representations also demonstrate how the generalisation is
possible via the exploitation of this functional hierarchical recurrent
network.
Labeled Multi-Bernoulli Tracking for Industrial Mobile Platform Safety
Authors: Tharindu Rathnayake, Reza Hoseinnezhad, Ruwan Tennakoon, Alireza Bab-Hadiashar
Date: 2016-04-20 00:00:00

This paper presents a track-before-detect labeled multi-Bernoulli filter
tailored for industrial mobile platform safety applications. We derive two
application specific separable likelihood functions that capture the geometric
shape and colour information of the human targets who are wearing a high
visible vest. These likelihoods are then used in a labeled multi-Bernoulli
filter with a novel two step Bayesian update. Preliminary simulation results
show that the proposed solution can successfully track human workers wearing a
luminous yellow colour vest in an industrial environment.
Action Recognition in Video Using Sparse Coding and Relative Features
Authors: Anali Alfaro, Domingo Mery, Alvaro Soto
Date: 2016-05-10 00:00:00
Proceeding: CVPR 2016 6
Tasks: Action Recognition, Temporal Action Localization

This work presents an approach to category-based action recognition in video
using sparse coding techniques. The proposed approach includes two main
contributions: i) A new method to handle intra-class variations by decomposing
each video into a reduced set of representative atomic action acts or
key-sequences, and ii) A new video descriptor, ITRA: Inter-Temporal Relational
Act Descriptor, that exploits the power of comparative reasoning to capture
relative similarity relations among key-sequences. In terms of the method to
obtain key-sequences, we introduce a loss function that, for each video, leads
to the identification of a sparse set of representative key-frames capturing
both, relevant particularities arising in the input video, as well as relevant
generalities arising in the complete class collection. In terms of the method
to obtain the ITRA descriptor, we introduce a novel scheme to quantify relative
intra and inter-class similarities among local temporal patterns arising in the
videos. The resulting ITRA descriptor demonstrates to be highly effective to
discriminate among action categories. As a result, the proposed approach
reaches remarkable action recognition performance on several popular benchmark
datasets, outperforming alternative state-of-the-art techniques by a large
margin.