pwc-archive/papers-with-abstracts huggingface dataset:
Method to Assess the Temporal Persistence of Potential Biometric Features: Application to Oculomotor, and Gait-Related Databases
Authors: Lee Friedman, Ioannis Rigas, Mark S. Nixon, Oleg V. Komogortsev
Date: 2016-09-13 00:00:00

Although temporal persistence, or permanence, is a well understood
requirement for optimal biometric features, there is no general agreement on
how to assess temporal persistence. We suggest that the best way to assess
temporal persistence is to perform a test-retest study, and assess test-retest
reliability. For ratio-scale features that are normally distributed, this is
best done using the Intraclass Correlation Coefficient (ICC). For 10 distinct
data sets (8 eye-movement related, and 2 gait related), we calculated the
test-retest reliability ('Temporal persistence') of each feature, and compared
biometric performance of high-ICC features to lower ICC features, and to the
set of all features. We demonstrate that using a subset of only high-ICC
features produced superior Rank-1-Identification Rate (Rank-1-IR) performance
in 9 of 10 databases (p = 0.01, one-tailed). For Equal Error Rate (EER), using
a subset of only high-ICC features produced superior performance in 8 of 10
databases (p = 0.055, one-tailed). In general, then, prescreening potential
biometric features, and choosing only highly reliable features will yield
better performance than lower ICC features or than the set of all features
combined. We hypothesize that this would likely be the case for any biometric
modality where the features can be expressed as quantitative values on an
interval or ratio scale, assuming an adequate number of relatively independent
features.
Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxy Data
Authors: David Lawlor, Tamás Budavári, Michael W. Mahoney
Date: 2016-09-13 00:00:00

We apply a novel spectral graph technique, that of locally-biased
semi-supervised eigenvectors, to study the diversity of galaxies. This
technique permits us to characterize empirically the natural variations in
observed spectra data, and we illustrate how this approach can be used in an
exploratory manner to highlight both large-scale global as well as small-scale
local structure in Sloan Digital Sky Survey (SDSS) data. We use this method in
a way that simultaneously takes into account the measurements of spectral lines
as well as the continuum shape. Unlike Principal Component Analysis, this
method does not assume that the Euclidean distance between galaxy spectra is a
good global measure of similarity between all spectra, but instead it only
assumes that local difference information between similar spectra is reliable.
Moreover, unlike other nonlinear dimensionality methods, this method can be
used to characterize very finely both small-scale local as well as large-scale
global properties of realistic noisy data. The power of the method is
demonstrated on the SDSS Main Galaxy Sample by illustrating that the derived
embeddings of spectra carry an unprecedented amount of information. By using a
straightforward global or unsupervised variant, we observe that the main
features correlate strongly with star formation rate and that they clearly
separate active galactic nuclei. Computed parameters of the method can be used
to describe line strengths and their interdependencies. By using a
locally-biased or semi-supervised variant, we are able to focus on typical
variations around specific objects of astronomical interest. We present several
examples illustrating that this approach can enable new discoveries in the data
as well as a detailed understanding of very fine local structure that would
otherwise be overwhelmed by large-scale noise and global trends in the data.
Diagnostic Prediction Using Discomfort Drawings with IBTM
Authors: Cheng Zhang, Hedvig Kjellstrom, Carl Henrik Ek, Bo C. Bertilson
Date: 2016-07-27 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Diagnostic, Prediction

In this paper, we explore the possibility to apply machine learning to make
diagnostic predictions using discomfort drawings. A discomfort drawing is an
intuitive way for patients to express discomfort and pain related symptoms.
These drawings have proven to be an effective method to collect patient data
and make diagnostic decisions in real-life practice. A dataset from real-world
patient cases is collected for which medical experts provide diagnostic labels.
Next, we use a factorized multimodal topic model, Inter-Battery Topic Model
(IBTM), to train a system that can make diagnostic predictions given an unseen
discomfort drawing. The number of output diagnostic labels is determined by
using mean-shift clustering on the discomfort drawing. Experimental results
show reasonable predictions of diagnostic labels given an unseen discomfort
drawing. Additionally, we generate synthetic discomfort drawings with IBTM
given a diagnostic label, which results in typical cases of symptoms. The
positive result indicates a significant potential of machine learning to be
used for parts of the pain diagnostic process and to be a decision support
system for physicians and other health care personnel.
Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms
Authors: Matthew Moskewicz, Forrest Iandola, Kurt Keutzer
Date: 2016-06-01 00:00:00
Tasks: Code Generation, Cultural Vocal Bursts Intensity Prediction

The popularity of neural networks (NNs) spans academia, industry, and popular
culture. In particular, convolutional neural networks (CNNs) have been applied
to many image based machine learning tasks and have yielded strong results. The
availability of hardware/software systems for efficient training and deployment
of large and/or deep CNN models has been, and continues to be, an important
consideration for the field. Early systems for NN computation focused on
leveraging existing dense linear algebra techniques and libraries. Current
approaches use low-level machine specific programming and/or closed-source,
purpose-built vendor libraries. In this work, we present an open source system
that, compared to existing approaches, achieves competitive computational speed
while achieving higher portability. We achieve this by targeting the
vendor-neutral OpenCL platform using a code-generation approach. We argue that
our approach allows for both: (1) the rapid development of new computational
kernels for existing hardware targets, and (2) the rapid tuning of existing
computational kernels for new hardware targets. Results are presented for a
case study of targeting the Qualcomm Snapdragon 820 mobile computing platform
for CNN deployment.
Information Theoretic Structure Learning with Confidence
Authors: Kevin R. Moon, Morteza Noshad, Salimeh Yasaei Sekeh, Alfred O. Hero III
Date: 2016-09-13 00:00:00
Tasks: Two-sample testing

Information theoretic measures (e.g. the Kullback Liebler divergence and
Shannon mutual information) have been used for exploring possibly nonlinear
multivariate dependencies in high dimension. If these dependencies are assumed
to follow a Markov factor graph model, this exploration process is called
structure discovery. For discrete-valued samples, estimates of the information
divergence over the parametric class of multinomial models lead to structure
discovery methods whose mean squared error achieves parametric convergence
rates as the sample size grows. However, a naive application of this method to
continuous nonparametric multivariate models converges much more slowly. In
this paper we introduce a new method for nonparametric structure discovery that
uses weighted ensemble divergence estimators that achieve parametric
convergence rates and obey an asymptotic central limit theorem that facilitates
hypothesis testing and other types of statistical validation.
Learning to Sketch Human Facial Portraits using Personal Styles by Case-Based Reasoning
Authors: Bingwen Jin, Songhua Xu, Weidong Geng
Date: 2016-07-10 00:00:00
Tasks: parameter estimation, Retrieval

This paper employs case-based reasoning (CBR) to capture the personal styles
of individual artists and generate the human facial portraits from photos
accordingly. For each human artist to be mimicked, a series of cases are
firstly built-up from her/his exemplars of source facial photo and hand-drawn
sketch, and then its stylization for facial photo is transformed as a
style-transferring process of iterative refinement by looking-for and applying
best-fit cases in a sense of style optimization. Two models, fitness evaluation
model and parameter estimation model, are learned for case retrieval and
adaptation respectively from these cases. The fitness evaluation model is to
decide which case is best-fitted to the sketching of current interest, and the
parameter estimation model is to automate case adaptation. The resultant sketch
is synthesized progressively with an iterative loop of retrieval and adaptation
of candidate cases until the desired aesthetic style is achieved. To explore
the effectiveness and advantages of the novel approach, we experimentally
compare the sketch portraits generated by the proposed method with that of a
state-of-the-art example-based facial sketch generation algorithm as well as a
couple commercial software packages. The comparisons reveal that our CBR based
synthesis method for facial portraits is superior both in capturing and
reproducing artists' personal illustration styles to the peer methods.
Crafting a multi-task CNN for viewpoint estimation
Authors: Francisco Massa, Renaud Marlet, Mathieu Aubry
Date: 2016-09-13 00:00:00
Tasks: General Classification, Viewpoint Estimation

Convolutional Neural Networks (CNNs) were recently shown to provide
state-of-the-art results for object category viewpoint estimation. However
different ways of formulating this problem have been proposed and the competing
approaches have been explored with very different design choices. This paper
presents a comparison of these approaches in a unified setting as well as a
detailed analysis of the key factors that impact performance. Followingly, we
present a new joint training method with the detection task and demonstrate its
benefit. We also highlight the superiority of classification approaches over
regression approaches, quantify the benefits of deeper architectures and
extended training data, and demonstrate that synthetic data is beneficial even
when using ImageNet training data. By combining all these elements, we
demonstrate an improvement of approximately 5% mAVP over previous
state-of-the-art results on the Pascal3D+ dataset. In particular for their most
challenging 24 view classification task we improve the results from 31.1% to
36.1% mAVP.
VIPLFaceNet: An Open Source Deep Face Recognition SDK
Authors: Xin Liu, Meina Kan, Wanglong Wu, Shiguang Shan, Xilin Chen
Date: 2016-09-13 00:00:00
Tasks: CPU, Face Recognition

Robust face representation is imperative to highly accurate face recognition.
In this work, we propose an open source face recognition method with deep
representation named as VIPLFaceNet, which is a 10-layer deep convolutional
neural network with 7 convolutional layers and 3 fully-connected layers.
Compared with the well-known AlexNet, our VIPLFaceNet takes only 20% training
time and 60% testing time, but achieves 40\% drop in error rate on the
real-world face recognition benchmark LFW. Our VIPLFaceNet achieves 98.60% mean
accuracy on LFW using one single network. An open-source C++ SDK based on
VIPLFaceNet is released under BSD license. The SDK takes about 150ms to process
one face image in a single thread on an i7 desktop CPU. VIPLFaceNet provides a
state-of-the-art start point for both academic and industrial face recognition
applications.
Training with Exploration Improves a Greedy Stack-LSTM Parser
Authors: Miguel Ballesteros, Yoav Goldberg, Chris Dyer, Noah A. Smith
Date: 2016-03-11 00:00:00
Tasks: Chinese Dependency Parsing, Dependency Parsing

We adapt the greedy Stack-LSTM dependency parser of Dyer et al. (2015) to
support a training-with-exploration procedure using dynamic oracles(Goldberg
and Nivre, 2013) instead of cross-entropy minimization. This form of training,
which accounts for model predictions at training time rather than assuming an
error-free action history, improves parsing accuracies for both English and
Chinese, obtaining very strong results for both languages. We discuss some
modifications needed in order to get training with exploration to work well for
a probabilistic neural-network.
Optimal learning with Bernstein Online Aggregation
Authors: Olivier Wintenberger
Date: 2014-04-04 00:00:00

We introduce a new recursive aggregation procedure called Bernstein Online
Aggregation (BOA). The exponential weights include an accuracy term and a
second order term that is a proxy of the quadratic variation as in Hazan and
Kale (2010). This second term stabilizes the procedure that is optimal in
different senses. We first obtain optimal regret bounds in the deterministic
context. Then, an adaptive version is the first exponential weights algorithm
that exhibits a second order bound with excess losses that appears first in
Gaillard et al. (2014). The second order bounds in the deterministic context
are extended to a general stochastic context using the cumulative predictive
risk. Such conversion provides the main result of the paper, an inequality of a
novel type comparing the procedure with any deterministic aggregation procedure
for an integrated criteria. Then we obtain an observable estimate of the excess
of risk of the BOA procedure. To assert the optimality, we consider finally the
iid case for strongly convex and Lipschitz continuous losses and we prove that
the optimal rate of aggregation of Tsybakov (2003) is achieved. The batch
version of the BOA procedure is then the first adaptive explicit algorithm that
satisfies an optimal oracle inequality with high probability.
Instrumenting an SMT Solver to Solve Hybrid Network Reachability Problems
Authors: Daniel Bryce, Sergiy Bogomolov, Alexander Heinz, Christian Schilling
Date: 2016-09-13 00:00:00
Tasks: Variable Selection

PDDL+ planning has its semantics rooted in hybrid automata (HA) and recent
work has shown that it can be modeled as a network of HAs. Addressing the
complexity of nonlinear PDDL+ planning as HAs requires both space and time
efficient reasoning. Unfortunately, existing solvers either do not address
nonlinear dynamics or do not natively support networks of automata.
  We present a new algorithm, called HNSolve, which guides the variable
selection of the dReal Satisfiability Modulo Theories (SMT) solver while
reasoning about network encodings of nonlinear PDDL+ planning as HAs. HNSolve
tightly integrates with dReal by solving a discrete abstraction of the HA
network. HNSolve finds composite runs on the HA network that ignore continuous
variables, but respect mode jumps and synchronization labels. HNSolve
admissibly detects dead-ends in the discrete abstraction, and posts conflict
clauses that prune the SMT solver's search. We evaluate the benefits of our
HNSolve algorithm on PDDL+ benchmark problems and demonstrate its performance
with respect to prior work.
A Unified Gender-Aware Age Estimation
Authors: Qing Tian, Songcan Chen, Xiaoyang Tan
Date: 2016-09-13 00:00:00
Tasks: Age Estimation

Human age estimation has attracted increasing researches due to its wide
applicability in such as security monitoring and advertisement recommendation.
Although a variety of methods have been proposed, most of them focus only on
the age-specific facial appearance. However, biological researches have shown
that not only gender but also the aging difference between the male and the
female inevitably affect the age estimation. To our knowledge, so far there
have been two methods that have concerned the gender factor. The first is a
sequential method which first classifies the gender and then performs age
estimation respectively for classified male and female. Although it promotes
age estimation performance because of its consideration on the gender semantic
difference, an accumulation risk of estimation errors is unavoidable. To
overcome drawbacks of the sequential strategy, the second is to regress the age
appended with the gender by concatenating their labels as two dimensional
output using Partial Least Squares (PLS). Although leading to promotion of age
estimation performance, such a concatenation not only likely confuses the
semantics between the gender and age, but also ignores the aging discrepancy
between the male and the female. In order to overcome their shortcomings, in
this paper we propose a unified framework to perform gender-aware age
estimation. The proposed method considers and utilizes not only the semantic
relationship between the gender and the age, but also the aging discrepancy
between the male and the female. Finally, experimental results demonstrate not
only the superiority of our method in performance, but also its good
interpretability in revealing the aging discrepancy.
Towards Deep Compositional Networks
Authors: Domen Tabernik, Matej Kristan, Jeremy L. Wyatt, Aleš Leonardis
Date: 2016-09-13 00:00:00

Hierarchical feature learning based on convolutional neural networks (CNN)
has recently shown significant potential in various computer vision tasks.
While allowing high-quality discriminative feature learning, the downside of
CNNs is the lack of explicit structure in features, which often leads to
overfitting, absence of reconstruction from partial observations and limited
generative abilities. Explicit structure is inherent in hierarchical
compositional models, however, these lack the ability to optimize a
well-defined cost function. We propose a novel analytic model of a basic unit
in a layered hierarchical model with both explicit compositional structure and
a well-defined discriminative cost function. Our experiments on two datasets
show that the proposed compositional model performs on a par with standard CNNs
on discriminative tasks, while, due to explicit modeling of the structure in
the feature units, affording a straight-forward visualization of parts and
faster inference due to separability of the units. Actions
Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups
Authors: Chi Xu, Lakshmi Narasimhan Govindarajan, Yu Zhang, Li Cheng
Date: 2016-09-13 00:00:00
Tasks: Action Recognition, Pose Estimation, regression, Temporal Action Localization

Pose estimation, tracking, and action recognition of articulated objects from
depth images are important and challenging problems, which are normally
considered separately. In this paper, a unified paradigm based on Lie group
theory is proposed, which enables us to collectively address these related
problems. Our approach is also applicable to a wide range of articulated
objects. Empirically it is evaluated on lab animals including mouse and fish,
as well as on human hand. On these applications, it is shown to deliver
competitive results compared to the state-of-the-arts, and non-trivial
baselines including convolutional neural networks and regression forest
methods.
Learning conditional independence structure for high-dimensional uncorrelated vector processes
Authors: Nguyen Tran Quang, Alexander Jung
Date: 2016-09-13 00:00:00
Tasks: Model Selection, Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

We formulate and analyze a graphical model selection method for inferring the
conditional independence graph of a high-dimensional nonstationary Gaussian
random process (time series) from a finite-length observation. The observed
process samples are assumed uncorrelated over time and having a time-varying
marginal distribution. The selection method is based on testing conditional
variances obtained for small subsets of process components. This allows to cope
with the high-dimensional regime, where the sample size can be (drastically)
smaller than the process dimension. We characterize the required sample size
such that the proposed selection method is successful with high probability.
Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting
Authors: Daniele Calandriello, Alessandro Lazaric, Michal Valko
Date: 2016-09-13 00:00:00

We derive a new proof to show that the incremental resparsification algorithm
proposed by Kelner and Levin (2013) produces a spectral sparsifier in high
probability. We rigorously take into account the dependencies across subsequent
resparsifications using martingale inequalities, fixing a flaw in the original
analysis.
Graph Aggregation
Authors: Ulle Endriss, Umberto Grandi
Date: 2016-09-13 00:00:00
Tasks: Abstract Argumentation

Graph aggregation is the process of computing a single output graph that
constitutes a good compromise between several input graphs, each provided by a
different source. One needs to perform graph aggregation in a wide variety of
situations, e.g., when applying a voting rule (graphs as preference orders),
when consolidating conflicting views regarding the relationships between
arguments in a debate (graphs as abstract argumentation frameworks), or when
computing a consensus between several alternative clusterings of a given
dataset (graphs as equivalence relations). In this paper, we introduce a formal
framework for graph aggregation grounded in social choice theory. Our focus is
on understanding which properties shared by the individual input graphs will
transfer to the output graph returned by a given aggregation rule. We consider
both common properties of graphs, such as transitivity and reflexivity, and
arbitrary properties expressible in certain fragments of modal logic. Our
results establish several connections between the types of properties preserved
under aggregation and the choice-theoretic axioms satisfied by the rules used.
The most important of these results is a powerful impossibility theorem that
generalises Arrow's seminal result for the aggregation of preference orders to
a large collection of different types of graphs.
An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs: Finding Patterns for Human Associations in DBpedia
Authors: Jörn Hees, Rouven Bauer, Joachim Folz, Damian Borth, Andreas Dengel
Date: 2016-07-25 00:00:00

Efficient usage of the knowledge provided by the Linked Data community is
often hindered by the need for domain experts to formulate the right SPARQL
queries to answer questions. For new questions they have to decide which
datasets are suitable and in which terminology and modelling style to phrase
the SPARQL query.
  In this work we present an evolutionary algorithm to help with this
challenging task. Given a training list of source-target node-pair examples our
algorithm can learn patterns (SPARQL queries) from a SPARQL endpoint. The
learned patterns can be visualised to form the basis for further investigation,
or they can be used to predict target nodes for new source nodes.
  Amongst others, we apply our algorithm to a dataset of several hundred human
associations (such as "circle - square") to find patterns for them in DBpedia.
We show the scalability of the algorithm by running it against a SPARQL
endpoint loaded with > 7.9 billion triples. Further, we use the resulting
SPARQL queries to mimic human associations with a Mean Average Precision (MAP)
of 39.9 % and a Recall@10 of 63.9 %.