pwc-archive/papers-with-abstracts huggingface dataset:
Combining ConvNets with Hand-Crafted Features for Action Recognition Based on an HMM-SVM Classifier
Authors: Pichao Wang, Zhaoyang Li, Yonghong Hou, Wanqing Li
Date: 2016-02-01 00:00:00
Tasks: Action Recognition, Temporal Action Localization

This paper proposes a new framework for RGB-D-based action recognition that
takes advantages of hand-designed features from skeleton data and deeply
learned features from depth maps, and exploits effectively both the local and
global temporal information. Specifically, depth and skeleton data are firstly
augmented for deep learning and making the recognition insensitive to view
variance. Secondly, depth sequences are segmented using the hand-crafted
features based on skeleton joints motion histogram to exploit the local
temporal information. All training se gments are clustered using an Infinite
Gaussian Mixture Model (IGMM) through Bayesian estimation and labelled for
training Convolutional Neural Networks (ConvNets) on the depth maps. Thus, a
depth sequence can be reliably encoded into a sequence of segment labels.
Finally, the sequence of labels is fed into a joint Hidden Markov Model and
Support Vector Machine (HMM-SVM) classifier to explore the global temporal
information for final recognition.
Learning Data Triage: Linear Decoding Works for Compressive MRI
Authors: Yen-Huan Li, Volkan Cevher
Date: 2016-02-01 00:00:00

The standard approach to compressive sampling considers recovering an unknown
deterministic signal with certain known structure, and designing the
sub-sampling pattern and recovery algorithm based on the known structure. This
approach requires looking for a good representation that reveals the signal
structure, and solving a non-smooth convex minimization problem (e.g., basis
pursuit). In this paper, another approach is considered: We learn a good
sub-sampling pattern based on available training signals, without knowing the
signal structure in advance, and reconstruct an accordingly sub-sampled signal
by computationally much cheaper linear reconstruction. We provide a theoretical
guarantee on the recovery error, and show via experiments on real-world MRI
data the effectiveness of the proposed compressive MRI scheme.
Algorithm-Induced Prior for Image Restoration
Authors: Stanley H. Chan
Date: 2016-02-01 00:00:00
Tasks: Denoising, Image Inpainting, Image Restoration

This paper studies a type of image priors that are constructed implicitly
through the alternating direction method of multiplier (ADMM) algorithm, called
the algorithm-induced prior. Different from classical image priors which are
defined before running the reconstruction algorithm, algorithm-induced priors
are defined by the denoising procedure used to replace one of the two modules
in the ADMM algorithm. Since such prior is not explicitly defined, analyzing
the performance has been difficult in the past.
  Focusing on the class of symmetric smoothing filters, this paper presents an
explicit expression of the prior induced by the ADMM algorithm. The new prior
is reminiscent to the conventional graph Laplacian but with stronger
reconstruction performance. It can also be shown that the overall
reconstruction has an efficient closed-form implementation if the associated
symmetric smoothing filter is low rank. The results are validated with
experiments on image inpainting.
Improving Vertebra Segmentation through Joint Vertebra-Rib Atlases
Authors: Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers
Date: 2016-02-01 00:00:00
Tasks: Computed Tomography (CT), Segmentation

Accurate spine segmentation allows for improved identification and
quantitative characterization of abnormalities of the vertebra, such as
vertebral fractures. However, in existing automated vertebra segmentation
methods on computed tomography (CT) images, leakage into nearby bones such as
ribs occurs due to the close proximity of these visibly intense structures in a
3D CT volume. To reduce this error, we propose the use of joint vertebra-rib
atlases to improve the segmentation of vertebrae via multi-atlas joint label
fusion. Segmentation was performed and evaluated on CTs containing 106 thoracic
and lumbar vertebrae from 10 pathological and traumatic spine patients on an
individual vertebra level basis. Vertebra atlases produced errors where the
segmentation leaked into the ribs. The use of joint vertebra-rib atlases
produced a statistically significant increase in the Dice coefficient from 92.5
$\pm$ 3.1% to 93.8 $\pm$ 2.1% for the left and right transverse processes and a
decrease in the mean and max surface distance from 0.75 $\pm$ 0.60mm and 8.63
$\pm$ 4.44mm to 0.30 $\pm$ 0.27mm and 3.65 $\pm$ 2.87mm, respectively.
A Deep Learning Based Fast Image Saliency Detection Algorithm
Authors: Hengyue Pan, Hui Jiang
Date: 2016-02-01 00:00:00
Tasks: Deep Learning, Saliency Detection, Superpixels

In this paper, we propose a fast deep learning method for object saliency
detection using convolutional neural networks. In our approach, we use a
gradient descent method to iteratively modify the input images based on the
pixel-wise gradients to reduce a pre-defined cost function, which is defined to
measure the class-specific objectness and clamp the class-irrelevant outputs to
maintain image background. The pixel-wise gradients can be efficiently computed
using the back-propagation algorithm. We further apply SLIC superpixels and LAB
color based low level saliency features to smooth and refine the gradients. Our
methods are quite computationally efficient, much faster than other deep
learning based saliency methods. Experimental results on two benchmark tasks,
namely Pascal VOC 2012 and MSRA10k, have shown that our proposed methods can
generate high-quality salience maps, at least comparable with many slow and
complicated deep learning methods. Comparing with the pure low-level methods,
our approach excels in handling many difficult images, which contain complex
background, highly-variable salient objects, multiple objects, and/or very
small salient objects.
TED: A Tolerant Edit Distance for Segmentation Evaluation
Authors: Jan Funke, Francesc Moreno-Noguer, Albert Cardona, Matthew Cook
Date: 2015-03-08 00:00:00

In this paper, we present a novel error measure to compare a segmentation
against ground truth. This measure, which we call Tolerant Edit Distance (TED),
is motivated by two observations: (1) Some errors, like small boundary shifts,
are tolerable in practice. Which errors are tolerable is application dependent
and should be a parameter of the measure. (2) Non-tolerable errors have to be
corrected manually. The time needed to do so should be reflected by the error
measure. Using integer linear programming, the TED finds the minimal weighted
sum of split and merge errors exceeding a given tolerance criterion, and thus
provides a time-to-fix estimate. In contrast to commonly used measures like
Rand index or variation of information, the TED (1) does not count small, but
tolerable, differences, (2) provides intuitive numbers, (3) gives a time-to-fix
estimate, and (4) can localize and classify the type of errors. By supporting
both isotropic and anisotropic volumes and having a flexible tolerance
criterion, the TED can be adapted to different requirements. On example
segmentations for 3D neuron segmentation, we demonstrate that the TED is
capable of counting topological errors, while ignoring small boundary shifts.
Towards a Cognitive Routing Engine for Software Defined Networks
Authors: Frederic Francois, Erol Gelenbe
Date: 2016-02-01 00:00:00

Most Software Defined Networks (SDN) traffic engineering applications use
excessive and frequent global monitoring in order to find the optimal
Quality-of-Service (QoS) paths for the current state of the network. In this
work, we present the motivations, architecture and initial evaluation of a SDN
application called Cognitive Routing Engine (CRE) which is able to find
near-optimal paths for a user-specified QoS while using a very small monitoring
overhead compared to global monitoring which is required to guarantee that
optimal paths are found. Smaller monitoring overheads bring the advantage of
smaller response time for the SDN controllers and switches. The initial
evaluation of CRE on a SDN representation of the GEANT academic network shows
that it is possible to find near-optimal paths with a small optimality gap of
1.65% while using 9.5 times less monitoring.
An Iterative Deep Learning Framework for Unsupervised Discovery of Speech Features and Linguistic Units with Applications on Spoken Term Detection
Authors: Cheng-Tao Chung, Cheng-Yu Tsai, Hsiang-Hung Lu, Chia-Hsiang Liu, Hung-Yi Lee, Lin-shan Lee
Date: 2016-02-01 00:00:00

In this work we aim to discover high quality speech features and linguistic
units directly from unlabeled speech data in a zero resource scenario. The
results are evaluated using the metrics and corpora proposed in the Zero
Resource Speech Challenge organized at Interspeech 2015. A Multi-layered
Acoustic Tokenizer (MAT) was proposed for automatic discovery of multiple sets
of acoustic tokens from the given corpus. Each acoustic token set is specified
by a set of hyperparameters that describe the model configuration. These sets
of acoustic tokens carry different characteristics fof the given corpus and the
language behind, thus can be mutually reinforced. The multiple sets of token
labels are then used as the targets of a Multi-target Deep Neural Network
(MDNN) trained on low-level acoustic features. Bottleneck features extracted
from the MDNN are then used as the feedback input to the MAT and the MDNN
itself in the next iteration. We call this iterative deep learning framework
the Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN), which
generates both high quality speech features for the Track 1 of the Challenge
and acoustic tokens for the Track 2 of the Challenge. In addition, we performed
extra experiments on the same corpora on the application of query-by-example
spoken term detection. The experimental results showed the iterative deep
learning framework of MAT-DNN improved the detection performance due to better
underlying speech features and acoustic tokens.
A Survey on Contextual Multi-armed Bandits
Authors: Li Zhou
Date: 2015-08-13 00:00:00
Tasks: Multi-Armed Bandits, Survey

In this survey we cover a few stochastic and adversarial contextual bandit
algorithms. We analyze each algorithm's assumption and regret bound.
Probabilistic Models for Computerized Adaptive Testing: Experiments
Authors: Martin Plajner, Jiří Vomlel
Date: 2016-01-28 00:00:00

This paper follows previous research we have already performed in the area of
Bayesian networks models for CAT. We present models using Item Response Theory
(IRT - standard CAT method), Bayesian networks, and neural networks. We
conducted simulated CAT tests on empirical data. Results of these tests are
presented for each model separately and compared.
Evolutionary stability implies asymptotic stability under multiplicative weights
Authors: Ioannis Avramopoulos
Date: 2016-01-27 00:00:00

We show that evolutionarily stable states in general (nonlinear) population
games (which can be viewed as continuous vector fields constrained on a
polytope) are asymptotically stable under a multiplicative weights dynamic
(under appropriate choices of a parameter called the learning rate or step
size, which we demonstrate to be crucial to achieve convergence, as otherwise
even chaotic behavior is possible to manifest). Our result implies that
evolutionary theories based on multiplicative weights are compatible (in
principle, more general) with those based on the notion of evolutionary
stability. However, our result further establishes multiplicative weights as a
nonlinear programming primitive (on par with standard nonlinear programming
methods) since various nonlinear optimization problems, such as finding
Nash/Wardrop equilibria in nonatomic congestion games, which are well-known to
be equipped with a convex potential function, and finding strict local maxima
of quadratic programming problems, are special cases of the problem of
computing evolutionarily stable states in nonlinear population games.
Differentially Private Online Learning for Cloud-Based Video Recommendation with Multimedia Big Data in Social Networks
Authors: Pan Zhou, Yingxue Zhou, Dapeng Wu, Hai Jin
Date: 2015-09-01 00:00:00
Tasks: Privacy Preserving, Recommendation Systems

With the rapid growth in multimedia services and the enormous offers of video
contents in online social networks, users have difficulty in obtaining their
interests. Therefore, various personalized recommendation systems have been
proposed. However, they ignore that the accelerated proliferation of social
media data has led to the big data era, which has greatly impeded the process
of video recommendation. In addition, none of them has considered both the
privacy of users' contexts (e,g., social status, ages and hobbies) and video
service vendors' repositories, which are extremely sensitive and of significant
commercial value. To handle the problems, we propose a cloud-assisted
differentially private video recommendation system based on distributed online
learning. In our framework, service vendors are modeled as distributed
cooperative learners, recommending videos according to user's context, while
simultaneously adapting the video-selection strategy based on user-click
feedback to maximize total user clicks (reward). Considering the sparsity and
heterogeneity of big social media data, we also propose a novel geometric
differentially private model, which can greatly reduce the performance
(recommendation accuracy) loss. Our simulation shows the proposed algorithms
outperform other existing methods and keep a delicate balance between computing
accuracy and privacy preserving level.
Scene Invariant Crowd Segmentation and Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)
Authors: Parthipan Siva, Mohammad Javad Shafiee, Mike Jamieson, Alexander Wong
Date: 2016-02-01 00:00:00
Tasks: Crowd Counting, Segmentation

The problem of automated crowd segmentation and counting has garnered
significant interest in the field of video surveillance. This paper proposes a
novel scene invariant crowd segmentation and counting algorithm designed with
high accuracy yet low computational complexity in mind, which is key for
widespread industrial adoption. A novel low-complexity, scale-normalized
feature called Histogram of Moving Gradients (HoMG) is introduced for highly
effective spatiotemporal representation of individuals and crowds within a
video. Real-time crowd segmentation is achieved via boosted cascade of weak
classifiers based on sliding-window HoMG features, while linear SVM regression
of crowd-region HoMG features is employed for real-time crowd counting.
Experimental results using multi-camera crowd datasets show that the proposed
algorithm significantly outperform state-of-the-art crowd counting algorithms,
as well as achieve very promising crowd segmentation results, thus
demonstrating the efficacy of the proposed method for highly-accurate,
real-time video-driven crowd analysis.
ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening
Authors: Ahmed M. Alaa, Kyeong H. Moon, William Hsu, Mihaela van der Schaar
Date: 2016-02-01 00:00:00
Tasks: Diagnostic

Breast cancer screening policies attempt to achieve timely diagnosis by the
regular screening of apparently healthy women. Various clinical decisions are
needed to manage the screening process; those include: selecting the screening
tests for a woman to take, interpreting the test outcomes, and deciding whether
or not a woman should be referred to a diagnostic test. Such decisions are
currently guided by clinical practice guidelines (CPGs), which represent a
one-size-fits-all approach that are designed to work well on average for a
population, without guaranteeing that it will work well uniformly over that
population. Since the risks and benefits of screening are functions of each
patients features, personalized screening policies that are tailored to the
features of individuals are needed in order to ensure that the right tests are
recommended to the right woman. In order to address this issue, we present
ConfidentCare: a computer-aided clinical decision support system that learns a
personalized screening policy from the electronic health record (EHR) data.
ConfidentCare operates by recognizing clusters of similar patients, and
learning the best screening policy to adopt for each cluster. A cluster of
patients is a set of patients with similar features (e.g. age, breast density,
family history, etc.), and the screening policy is a set of guidelines on what
actions to recommend for a woman given her features and screening test scores.
ConfidentCare algorithm ensures that the policy adopted for every cluster of
patients satisfies a predefined accuracy requirement with a high level of
confidence. We show that our algorithm outperforms the current CPGs in terms of
cost-efficiency and false positive rates.
Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers
Authors: Yijun Xiao, Kyunghyun Cho
Date: 2016-02-01 00:00:00
Tasks: Classification, Document Classification, General Classification

Document classification tasks were primarily tackled at word level. Recent
research that works with character-level inputs shows several benefits over
word-level approaches such as natural incorporation of morphemes and better
handling of rare words. We propose a neural network architecture that utilizes
both convolution and recurrent layers to efficiently encode character inputs.
We validate the proposed model on eight large scale document classification
tasks and compare with character-level convolution-only models. It achieves
comparable performances with much less parameters.
Semi-supervised K-means++
Authors: Jordan Yoder, Carey E. Priebe
Date: 2016-02-01 00:00:00
Tasks: Clustering

Traditionally, practitioners initialize the {\tt k-means} algorithm with
centers chosen uniformly at random. Randomized initialization with uneven
weights ({\tt k-means++}) has recently been used to improve the performance
over this strategy in cost and run-time. We consider the k-means problem with
semi-supervised information, where some of the data are pre-labeled, and we
seek to label the rest according to the minimum cost solution. By extending the
{\tt k-means++} algorithm and analysis to account for the labels, we derive an
improved theoretical bound on expected cost and observe improved performance in
simulated and real data examples. This analysis provides theoretical
justification for a roughly linear semi-supervised clustering algorithm.
A Spectral Series Approach to High-Dimensional Nonparametric Regression
Authors: Ann B. Lee, Rafael Izbicki
Date: 2016-02-01 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

A key question in modern statistics is how to make fast and reliable
inferences for complex, high-dimensional data. While there has been much
interest in sparse techniques, current methods do not generalize well to data
with nonlinear structure. In this work, we present an orthogonal series
estimator for predictors that are complex aggregate objects, such as natural
images, galaxy spectra, trajectories, and movies. Our series approach ties
together ideas from kernel machine learning, and Fourier methods. We expand the
unknown regression on the data in terms of the eigenfunctions of a kernel-based
operator, and we take advantage of orthogonality of the basis with respect to
the underlying data distribution, P, to speed up computations and tuning of
parameters. If the kernel is appropriately chosen, then the eigenfunctions
adapt to the intrinsic geometry and dimension of the data. We provide
theoretical guarantees for a radial kernel with varying bandwidth, and we
relate smoothness of the regression function with respect to P to sparsity in
the eigenbasis. Finally, using simulated and real-world data, we systematically
compare the performance of the spectral series approach with classical kernel
smoothing, k-nearest neighbors regression, kernel ridge regression, and
state-of-the-art manifold and local regression methods.
Adaptive Subgradient Methods for Online AUC Maximization
Authors: Yi Ding, Peilin Zhao, Steven C. H. Hoi, Yew-Soon Ong
Date: 2016-02-01 00:00:00

Learning for maximizing AUC performance is an important research problem in
Machine Learning and Artificial Intelligence. Unlike traditional batch learning
methods for maximizing AUC which often suffer from poor scalability, recent
years have witnessed some emerging studies that attempt to maximize AUC by
single-pass online learning approaches. Despite their encouraging results
reported, the existing online AUC maximization algorithms often adopt simple
online gradient descent approaches that fail to exploit the geometrical
knowledge of the data observed during the online learning process, and thus
could suffer from relatively larger regret. To address the above limitation, in
this work, we explore a novel algorithm of Adaptive Online AUC Maximization
(AdaOAM) which employs an adaptive gradient method that exploits the knowledge
of historical gradients to perform more informative online learning. The new
adaptive updating strategy of the AdaOAM is less sensitive to the parameter
settings and maintains the same time complexity as previous non-adaptive
counterparts. Additionally, we extend the algorithm to handle high-dimensional
sparse data (SAdaOAM) and address sparsity in the solution by performing lazy
gradient updating. We analyze the theoretical bounds and evaluate their
empirical performance on various types of data sets. The encouraging empirical
results obtained clearly highlighted the effectiveness and efficiency of the
proposed algorithms.
Bandits meet Computer Architecture: Designing a Smartly-allocated Cache
Authors: Yonatan Glassner, Koby Crammer
Date: 2016-01-31 00:00:00
Tasks: Multi-Armed Bandits

In many embedded systems, such as imaging sys- tems, the system has a single
designated purpose, and same threads are executed repeatedly. Profiling thread
behavior, allows the system to allocate each thread its resources in a way that
improves overall system performance. We study an online resource al-
locationproblem,wherearesourcemanagersimulta- neously allocates resources
(exploration), learns the impact on the different consumers (learning) and im-
proves allocation towards optimal performance (ex- ploitation). We build on the
rich framework of multi- armed bandits and present online and offline algo-
rithms. Through extensive experiments with both synthetic data and real-world
cache allocation to threads we show the merits and properties of our al-
gorithms
Bit-Planes: Dense Subpixel Alignment of Binary Descriptors
Authors: Hatem Alismail, Brett Browning, Simon Lucey
Date: 2016-01-31 00:00:00

Binary descriptors have been instrumental in the recent evolution of
computationally efficient sparse image alignment algorithms. Increasingly,
however, the vision community is interested in dense image alignment methods,
which are more suitable for estimating correspondences from high frame rate
cameras as they do not rely on exhaustive search. However, classic dense
alignment approaches are sensitive to illumination change. In this paper, we
propose an easy to implement and low complexity dense binary descriptor, which
we refer to as bit-planes, that can be seamlessly integrated within a
multi-channel Lucas & Kanade framework. This novel approach combines the
robustness of binary descriptors with the speed and accuracy of dense alignment
methods. The approach is demonstrated on a template tracking problem achieving
state-of-the-art robustness and faster than real-time performance on consumer
laptops (400+ fps on a single core Intel i7) and hand-held mobile devices (100+
fps on an iPad Air 2).