pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction
Authors: Hossein Soleimani, James Hensman, Suchi Saria
Date: 2017-08-16 00:00:00
Tasks: Gaussian Processes, Imputation, Prediction, Time Series, Time Series Analysis

Missing data and noisy observations pose significant challenges for reliably
predicting events from irregularly sampled multivariate time series
(longitudinal) data. Imputation methods, which are typically used for
completing the data prior to event prediction, lack a principled mechanism to
account for the uncertainty due to missingness. Alternatively, state-of-the-art
joint modeling techniques can be used for jointly modeling the longitudinal and
event data and compute event probabilities conditioned on the longitudinal
observations. These approaches, however, make strong parametric assumptions and
do not easily scale to multivariate signals with many observations. Our
proposed approach consists of several key innovations. First, we develop a
flexible and scalable joint model based upon sparse multiple-output Gaussian
processes. Unlike state-of-the-art joint models, the proposed model can explain
highly challenging structure including non-Gaussian noise while scaling to
large data. Second, we derive an optimal policy for predicting events using the
distribution of the event occurrence estimated by the joint model. The derived
policy trades-off the cost of a delayed detection versus incorrect assessments
and abstains from making decisions when the estimated event probability does
not satisfy the derived confidence criteria. Experiments on a large dataset
show that the proposed framework significantly outperforms state-of-the-art
techniques in event prediction.
Learning Chinese Word Representations From Glyphs Of Characters
Authors: Tzu-Ray Su, Hung-Yi Lee
Date: 2017-08-16 00:00:00
Proceeding: EMNLP 2017 9

In this paper, we propose new methods to learn Chinese word representations.
Chinese characters are composed of graphical components, which carry rich
semantics. It is common for a Chinese learner to comprehend the meaning of a
word from these graphical components. As a result, we propose models that
enhance word representations by character glyphs. The character glyph features
are directly learned from the bitmaps of characters by convolutional
auto-encoder(convAE), and the glyph features improve Chinese word
representations which are already enhanced by character embeddings. Another
contribution in this paper is that we created several evaluation datasets in
traditional Chinese and made them public.
Frequentist coverage and sup-norm convergence rate in Gaussian process regression
Authors: Yun Yang, Anirban Bhattacharya, Debdeep Pati
Date: 2017-08-16 00:00:00
Tasks: regression

Gaussian process (GP) regression is a powerful interpolation technique due to
its flexibility in capturing non-linearity. In this paper, we provide a general
framework for understanding the frequentist coverage of point-wise and
simultaneous Bayesian credible sets in GP regression. As an intermediate
result, we develop a Bernstein von-Mises type result under supremum norm in
random design GP regression. Identifying both the mean and covariance function
of the posterior distribution of the Gaussian process as regularized
$M$-estimators, we show that the sampling distribution of the posterior mean
function and the centered posterior distribution can be respectively
approximated by two population level GPs. By developing a comparison inequality
between two GPs, we provide exact characterization of frequentist coverage
probabilities of Bayesian point-wise credible intervals and simultaneous
credible bands of the regression function. Our results show that inference
based on GP regression tends to be conservative; when the prior is
under-smoothed, the resulting credible intervals and bands have minimax-optimal
sizes, with their frequentist coverage converging to a non-degenerate value
between their nominal level and one. As a byproduct of our theory, we show that
the GP regression also yields minimax-optimal posterior contraction rate
relative to the supremum norm, which provides a positive evidence to the long
standing problem on optimal supremum norm contraction rate in GP regression.
Sublinear Time Low-Rank Approximation of Positive Semidefinite Matrices
Authors: Cameron Musco, David P. Woodruff
Date: 2017-04-11 00:00:00

We show how to compute a relative-error low-rank approximation to any
positive semidefinite (PSD) matrix in sublinear time, i.e., for any $n \times
n$ PSD matrix $A$, in $\tilde O(n \cdot poly(k/\epsilon))$ time we output a
rank-$k$ matrix $B$, in factored form, for which $\|A-B\|_F^2 \leq
(1+\epsilon)\|A-A_k\|_F^2$, where $A_k$ is the best rank-$k$ approximation to
$A$. When $k$ and $1/\epsilon$ are not too large compared to the sparsity of
$A$, our algorithm does not need to read all entries of the matrix. Hence, we
significantly improve upon previous $nnz(A)$ time algorithms based on oblivious
subspace embeddings, and bypass an $nnz(A)$ time lower bound for general
matrices (where $nnz(A)$ denotes the number of non-zero entries in the matrix).
We prove time lower bounds for low-rank approximation of PSD matrices, showing
that our algorithm is close to optimal. Finally, we extend our techniques to
give sublinear time algorithms for low-rank approximation of $A$ in the (often
stronger) spectral norm metric $\|A-B\|_2^2$ and for ridge regression on PSD
matrices.
An Improved Neural Segmentation Method Based on U-NET
Authors: Chenyang Xu, Mengxin Li
Date: 2017-08-16 00:00:00
Tasks: Segmentation

Neural segmentation has a great impact on the smooth implementation of local
anesthesia surgery. At present, the network for the segmentation includes U-NET
[1] and SegNet [2]. U-NET network has short training time and less training
parameters, but the depth is not deep enough. SegNet network has deeper
structure, but it needs longer training time, and more training samples. In
this paper, we propose an improved U-NET neural network for the segmentation.
This network deepens the original structure through importing residual network.
Compared with U-NET and SegNet, the improved U-NET network has fewer training
parameters, shorter training time and get a great improvement in segmentation
effect. The improved U-NET network structure has a good application scene in
neural segmentation.
A Survey of Parallel A*
Authors: Alex Fukunaga, Adi Botea, Yuu Jinnai, Akihiro Kishimoto
Date: 2017-08-16 00:00:00
Tasks: Survey

A* is a best-first search algorithm for finding optimal-cost paths in graphs.
A* benefits significantly from parallelism because in many applications, A* is
limited by memory usage, so distributed memory implementations of A* that use
all of the aggregate memory on the cluster enable problems that can not be
solved by serial, single-machine implementations to be solved. We survey
approaches to parallel A*, focusing on decentralized approaches to A* which
partition the state space among processors. We also survey approaches to
parallel, limited-memory variants of A* such as parallel IDA*.
Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning
Authors: Asim Munawar, Phongtharin Vinayavekhin, Giovanni De Magistris
Date: 2017-08-16 00:00:00
Tasks: Anomaly Detection, Data Compression

Generative models are widely used for unsupervised learning with various
applications, including data compression and signal restoration. Training
methods for such systems focus on the generality of the network given limited
amount of training data. A less researched type of techniques concerns
generation of only a single type of input. This is useful for applications such
as constraint handling, noise reduction and anomaly detection. In this paper we
present a technique to limit the generative capability of the network using
negative learning. The proposed method searches the solution in the gradient
direction for the desired input and in the opposite direction for the undesired
input. One of the application can be anomaly detection where the undesired
inputs are the anomalous data. In the results section we demonstrate the
features of the algorithm using MNIST handwritten digit dataset and latter
apply the technique to a real-world obstacle detection problem. The results
clearly show that the proposed learning technique can significantly improve the
performance for anomaly detection.
Privacy-Enabled Biometric Search
Authors: Scott Streit, Brian Streit, Stephen Suffian
Date: 2017-08-16 00:00:00
Tasks: Management, Novel Concepts

Biometrics have a long-held hope of replacing passwords by establishing a
non-repudiated identity and providing authentication with convenience.
Convenience drives consumers toward biometrics-based access management
solutions. Unlike passwords, biometrics cannot be script-injected; however,
biometric data is considered highly sensitive due to its personal nature and
unique association with users. Biometrics differ from passwords in that
compromised passwords may be reset. Compromised biometrics offer no such
relief. A compromised biometric offers unlimited risk in privacy (anyone can
view the biometric) and authentication (anyone may use the biometric).
Standards such as the Biometric Open Protocol Standard (BOPS) (IEEE 2410-2016)
provide a detailed mechanism to authenticate biometrics based on pre-enrolled
devices and a previous identity by storing the biometric in encrypted form.
This paper describes a biometric-agnostic approach that addresses the privacy
concerns of biometrics through the implementation of BOPS. Specifically, two
novel concepts are introduced. First, a biometric is applied to a neural
network to create a feature vector. This neural network alone can be used for
one-to-one matching (authentication), but would require a search in linear time
for the one-to-many case (identity lookup). The classifying algorithm described
in this paper addresses this concern by producing normalized floating-point
values for each feature vector. This allows authentication lookup to occur in
up to polynomial time, allowing for search in encrypted biometric databases
with speed, accuracy and privacy.
Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa
Authors: Michael T. Lash, Yuqi Sun, Xun Zhou, Charles F. Lynch, W. Nick Street
Date: 2017-08-15 00:00:00
Tasks: Clustering

Neural networks are capable of learning rich, nonlinear feature
representations shown to be beneficial in many predictive tasks. In this work,
we use these models to explore the use of geographical features in predicting
colorectal cancer survival curves for patients in the state of Iowa, spanning
the years 1989 to 2012. Specifically, we compare model performance using a
newly defined metric -- area between the curves (ABC) -- to assess (a) whether
survival curves can be reasonably predicted for colorectal cancer patients in
the state of Iowa, (b) whether geographical features improve predictive
performance, and (c) whether a simple binary representation or richer, spectral
clustering-based representation perform better. Our findings suggest that
survival curves can be reasonably estimated on average, with predictive
performance deviating at the five-year survival mark. We also find that
geographical features improve predictive performance, and that the best
performance is obtained using richer, spectral analysis-elicited features.
User-driven Intelligent Interface on the Basis of Multimodal Augmented Reality and Brain-Computer Interaction for People with Functional Disabilities
Authors: S. Stirenko, Yu. Gordienko, T. Shemsedinov, O. Alienin, Yu. Kochura, N. Gordienko, A. Rojbi, J. R. López Benito, E. Artetxe González
Date: 2017-04-12 00:00:00
Tasks: Brain Computer Interface

The analysis of the current integration attempts of some modes and use cases
of user-machine interaction is presented. The new concept of the user-driven
intelligent interface is proposed on the basis of multimodal augmented reality
and brain-computer interaction for various applications: in disabilities
studies, education, home care, health care, etc. The several use cases of
multimodal augmentation are presented. The perspectives of the better human
comprehension by the immediate feedback through neurophysical channels by means
of brain-computer interaction are outlined. It is shown that brain-computer
interface (BCI) technology provides new strategies to overcome limits of the
currently available user interfaces, especially for people with functional
disabilities. The results of the previous studies of the low end consumer and
open-source BCI-devices allow us to conclude that combination of machine
learning (ML), multimodal interactions (visual, sound, tactile) with BCI will
profit from the immediate feedback from the actual neurophysical reactions
classified by ML methods. In general, BCI in combination with other modes of AR
interaction can deliver much more information than these types of interaction
themselves. Even in the current state the combined AR-BCI interfaces could
provide the highly adaptable and personal services, especially for people with
functional disabilities.
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts
Authors: Marcos V. Treviso, Christopher D. Shulby, Sandra M. Aluisio
Date: 2017-08-15 00:00:00
Tasks: Boundary Detection, Sentence, Word Embeddings

This paper is motivated by the automation of neuropsychological tests
involving discourse analysis in the retellings of narratives by patients with
potential cognitive impairment. In this scenario the task of sentence boundary
detection in speech transcripts is important as discourse analysis involves the
application of Natural Language Processing tools, such as taggers and parsers,
which depend on the sentence as a processing unit. Our aim in this paper is to
verify which embedding induction method works best for the sentence boundary
detection task, specifically whether it be those which were proposed to capture
semantic, syntactic or morphological similarities.
Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks
Authors: Marcos Vinícius Treviso, Christopher Shulby, Sandra Maria Aluísio
Date: 2016-10-02 00:00:00
Proceeding: EACL 2017 4
Tasks: POS, Segmentation, Sentence, Sentence segmentation, Word Embeddings

Automated discourse analysis tools based on Natural Language Processing (NLP)
aiming at the diagnosis of language-impairing dementias generally extract
several textual metrics of narrative transcripts. However, the absence of
sentence boundary segmentation in the transcripts prevents the direct
application of NLP methods which rely on these marks to function properly, such
as taggers and parsers. We present the first steps taken towards automatic
neuropsychological evaluation based on narrative discourse analysis, presenting
a new automatic sentence segmentation method for impaired speech. Our model
uses recurrent convolutional neural networks with prosodic, Part of Speech
(PoS) features, and word embeddings. It was evaluated intrinsically on
impaired, spontaneous speech, as well as, normal, prepared speech, and presents
better results for healthy elderly (CTL) (F1 = 0.74) and Mild Cognitive
Impairment (MCI) patients (F1 = 0.70) than the Conditional Random Fields method
(F1 = 0.55 and 0.53, respectively) used in the same context of our study. The
results suggest that our model is robust for impaired speech and can be used in
automated discourse analysis tools to differentiate narratives produced by MCI
and CTL.
System Identification through Online Sparse Gaussian Process Regression with Input Noise
Authors: Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden, Michel Verhaegen
Date: 2016-01-29 00:00:00
Tasks: regression

There has been a growing interest in using non-parametric regression methods
like Gaussian Process (GP) regression for system identification. GP regression
does traditionally have three important downsides: (1) it is computationally
intensive, (2) it cannot efficiently implement newly obtained measurements
online, and (3) it cannot deal with stochastic (noisy) input points. In this
paper we present an algorithm tackling all these three issues simultaneously.
The resulting Sparse Online Noisy Input GP (SONIG) regression algorithm can
incorporate new noisy measurements in constant runtime. A comparison has shown
that it is more accurate than similar existing regression algorithms. When
applied to non-linear black-box system modeling, its performance is competitive
with existing non-linear ARX models.
Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies
Authors: Daniel Hein, Alexander Hentschel, Thomas Runkler, Steffen Udluft
Date: 2016-10-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Fuzzy controllers are efficient and interpretable system controllers for
continuous state and action spaces. To date, such controllers have been
constructed manually or trained automatically either using expert-generated
problem-specific cost functions or incorporating detailed knowledge about the
optimal control strategy. Both requirements for automatic training processes
are not found in most real-world reinforcement learning (RL) problems. In such
applications, online learning is often prohibited for safety reasons because
online learning requires exploration of the problem's dynamics during policy
training. We introduce a fuzzy particle swarm reinforcement learning (FPSRL)
approach that can construct fuzzy RL policies solely by training parameters on
world models that simulate real system dynamics. These world models are created
by employing an autonomous machine learning technique that uses previously
generated transition samples of a real system. To the best of our knowledge,
this approach is the first to relate self-organizing fuzzy controllers to
model-based batch RL. Therefore, FPSRL is intended to solve problems in domains
where online learning is prohibited, system dynamics are relatively easy to
model from previously generated default policy transition samples, and it is
expected that a relatively easily interpretable control policy exists. The
efficiency of the proposed approach with problems from such domains is
demonstrated using three standard RL benchmarks, i.e., mountain car, cart-pole
balancing, and cart-pole swing-up. Our experimental results demonstrate
high-performing, interpretable fuzzy policies.
Visual Semantic Planning using Deep Successor Representations
Authors: Yuke Zhu, Daniel Gordon, Eric Kolve, Dieter Fox, Li Fei-Fei, Abhinav Gupta, Roozbeh Mottaghi, Ali Farhadi
Date: 2017-05-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: Imitation Learning, Reinforcement Learning, Reinforcement Learning (RL)

A crucial capability of real-world intelligent agents is their ability to
plan a sequence of actions to achieve their goals in the visual world. In this
work, we address the problem of visual semantic planning: the task of
predicting a sequence of actions from visual observations that transform a
dynamic environment from an initial state to a goal state. Doing so entails
knowledge about objects and their affordances, as well as actions and their
preconditions and effects. We propose learning these through interacting with a
visual and dynamic environment. Our proposed solution involves bootstrapping
reinforcement learning with imitation learning. To ensure cross task
generalization, we develop a deep predictive model based on successor
representations. Our experimental results show near optimal results across a
wide range of tasks in the challenging THOR environment.
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation
Authors: Chuang Gan, Yandong Li, Haoxiang Li, Chen Sun, Boqing Gong
Date: 2017-08-15 00:00:00
Proceeding: ICCV 2017 10
Tasks: Language Modeling, Language Modelling, Multiple-choice, Question Answering, Segmentation, Semantic Segmentation, Visual Question Answering, Visual Question Answering (VQA)

Rich and dense human labeled datasets are among the main enabling factors for
the recent advance on vision-language understanding. Many seemingly distant
annotations (e.g., semantic segmentation and visual question answering (VQA))
are inherently connected in that they reveal different levels and perspectives
of human understandings about the same visual scenes --- and even the same set
of images (e.g., of COCO). The popularity of COCO correlates those annotations
and tasks. Explicitly linking them up may significantly benefit both individual
tasks and the unified vision and language modeling. We present the preliminary
work of linking the instance segmentations provided by COCO to the questions
and answers (QAs) in the VQA dataset, and name the collected links visual
questions and segmentation answers (VQS). They transfer human supervision
between the previously separate tasks, offer more effective leverage to
existing problems, and also open the door for new research problems and models.
We study two applications of the VQS data in this paper: supervised attention
for VQA and a novel question-focused semantic segmentation task. For the
former, we obtain state-of-the-art results on the VQA real multiple-choice task
by simply augmenting the multilayer perceptrons with some attention features
that are learned using the segmentation-QA links as explicit supervision. To
put the latter in perspective, we study two plausible methods and compare them
to an oracle method assuming that the instance segmentations are given at the
test stage.
Identifying Harm Events in Clinical Care through Medical Narratives
Authors: Arman Cohan, Allan Fong, Raj Ratwani, Nazli Goharian
Date: 2017-08-15 00:00:00

Preventable medical errors are estimated to be among the leading causes of
injury and death in the United States. To prevent such errors, healthcare
systems have implemented patient safety and incident reporting systems. These
systems enable clinicians to report unsafe conditions and cases where patients
have been harmed due to errors in medical care. These reports are narratives in
natural language and while they provide detailed information about the
situation, it is non-trivial to perform large scale analysis for identifying
common causes of errors and harm to the patients. In this work, we present a
method based on attentive convolutional and recurrent networks for identifying
harm events in patient care and categorize the harm based on its severity
level. We demonstrate that our methods can significantly improve the
performance over existing methods in identifying harm in clinical care.
Deep 6-DOF Tracking
Authors: Mathieu Garon, Jean-François Lalonde
Date: 2017-03-28 00:00:00

We present a temporal 6-DOF tracking method which leverages deep learning to
achieve state-of-the-art performance on challenging datasets of real world
capture. Our method is both more accurate and more robust to occlusions than
the existing best performing approaches while maintaining real-time
performance. To assess its efficacy, we evaluate our approach on several
challenging RGBD sequences of real objects in a variety of conditions. Notably,
we systematically evaluate robustness to occlusions through a series of
sequences where the object to be tracked is increasingly occluded. Finally, our
approach is purely data-driven and does not require any hand-designed features:
robust tracking is automatically learned from data.