pwc-archive/papers-with-abstracts huggingface dataset:
Body movement to sound interface with vector autoregressive hierarchical hidden Markov models
Authors: Dimitrije Marković, Borjana Valčić, Nebojša Malešević
Date: 2016-10-26 00:00:00
Tasks: Bayesian Inference, General Classification, Onset Detection

Interfacing a kinetic action of a person to an action of a machine system is
an important research topic in many application areas. One of the key factors
for intimate human-machine interaction is the ability of the control algorithm
to detect and classify different user commands with shortest possible latency,
thus making a highly correlated link between cause and effect. In our research,
we focused on the task of mapping user kinematic actions into sound samples.
The presented methodology relies on the wireless sensor nodes equipped with
inertial measurement units and the real-time algorithm dedicated for early
detection and classification of a variety of movements/gestures performed by a
user. The core algorithm is based on the approximate Bayesian inference of
Vector Autoregressive Hierarchical Hidden Markov Models (VAR-HHMM), where
models database is derived from the set of motion gestures. The performance of
the algorithm was compared with an online version of the K-nearest neighbours
(KNN) algorithm, where we used offline expert based classification as the
benchmark. In almost all of the evaluation metrics (e.g. confusion matrix,
recall and precision scores) the VAR-HHMM algorithm outperformed KNN.
Furthermore, the VAR-HHMM algorithm, in some cases, achieved faster movement
onset detection compared with the offline standard. The proposed concept,
although envisioned for movement-to-sound application, could be implemented in
other human-machine interfaces.
New Liftable Classes for First-Order Probabilistic Inference
Authors: Seyed Mehran Kazemi, Angelika Kimmig, Guy Van Den Broeck, David Poole
Date: 2016-10-26 00:00:00
Proceeding: NeurIPS 2016 12

Statistical relational models provide compact encodings of probabilistic
dependencies in relational domains, but result in highly intractable graphical
models. The goal of lifted inference is to carry out probabilistic inference
without needing to reason about each individual separately, by instead treating
exchangeable, undistinguished objects as a whole. In this paper, we study the
domain recursion inference rule, which, despite its central role in early
theoretical results on domain-lifted inference, has later been believed
redundant. We show that this rule is more powerful than expected, and in fact
significantly extends the range of models for which lifted inference runs in
time polynomial in the number of individuals in the domain. This includes an
open problem called S4, the symmetric transitivity model, and a first-order
logic encoding of the birthday paradox. We further identify new classes S2FO2
and S2RU of domain-liftable theories, which respectively subsume FO2 and
recursively unary theories, the largest classes of domain-liftable theories
known so far, and show that using domain recursion can achieve exponential
speedup even in theories that cannot fully be lifted with the existing set of
inference rules.
Estimating the concentration of gold nanoparticles incorporated on Natural Rubber membranes using Multi-Level Starlet Optimal Segmentation
Authors: Alexandre Fioravante de Siqueira, Flávio Camargo Cabrera, Aylton Pagamisse, Aldo Eloizo Job
Date: 2016-10-26 00:00:00
Tasks: Segmentation

This study consolidates Multi-Level Starlet Segmentation (MLSS) and
Multi-Level Starlet Optimal Segmentation (MLSOS), techniques for
photomicrograph segmentation that use starlet wavelet detail levels to separate
areas of interest in an input image. Several segmentation levels can be
obtained using Multi-Level Starlet Segmentation; after that, Matthews
correlation coefficient (MCC) is used to choose an optimal segmentation level,
giving rise to Multi-Level Starlet Optimal Segmentation. In this paper, MLSOS
is employed to estimate the concentration of gold nanoparticles with diameter
around 47 nm, reducted on natural rubber membranes. These samples were used on
the construction of SERS/SERRS substrates and in the study of natural rubber
membranes with incorporated gold nanoparticles influence on Leishmania
braziliensis physiology. Precision, recall and accuracy are used to evaluate
the segmentation performance, and MLSOS presents accuracy greater than 88% for
this application.
Counterfactual Reasoning about Intent for Interactive Navigation in Dynamic Environments
Authors: A. Bordallo, F. Previtali, N. Nardelli, S. Ramamoorthy
Date: 2016-10-26 00:00:00
Tasks: counterfactual, Counterfactual Reasoning, Decision Making, Motion Planning, Robot Navigation, Visual Tracking

Many modern robotics applications require robots to function autonomously in
dynamic environments including other decision making agents, such as people or
other robots. This calls for fast and scalable interactive motion planning.
This requires models that take into consideration the other agent's intended
actions in one's own planning. We present a real-time motion planning framework
that brings together a few key components including intention inference by
reasoning counterfactually about potential motion of the other agents as they
work towards different goals. By using a light-weight motion model, we achieve
efficient iterative planning for fluid motion when avoiding pedestrians, in
parallel with goal inference for longer range movement prediction. This
inference framework is coupled with a novel distributed visual tracking method
that provides reliable and robust models for the current belief-state of the
monitored environment. This combined approach represents a computationally
efficient alternative to previously studied policy learning methods that often
require significant offline training or calibration and do not yet scale to
densely populated environments. We validate this framework with experiments
involving multi-robot and human-robot navigation. We further validate the
tracker component separately on much larger scale unconstrained pedestrian data
sets.
Probabilistic Linear Multistep Methods
Authors: Onur Teymur, Konstantinos Zygalakis, Ben Calderhead
Date: 2016-10-26 00:00:00
Proceeding: NeurIPS 2016 12

We present a derivation and theoretical investigation of the Adams-Bashforth
and Adams-Moulton family of linear multistep methods for solving ordinary
differential equations, starting from a Gaussian process (GP) framework. In the
limit, this formulation coincides with the classical deterministic methods,
which have been used as higher-order initial value problem solvers for over a
century. Furthermore, the natural probabilistic framework provided by the GP
formulation allows us to derive probabilistic versions of these methods, in the
spirit of a number of other probabilistic ODE solvers presented in the recent
literature. In contrast to higher-order Runge-Kutta methods, which require
multiple intermediate function evaluations per step, Adams family methods make
use of previous function evaluations, so that increased accuracy arising from a
higher-order multistep approach comes at very little additional computational
cost. We show that through a careful choice of covariance function for the GP,
the posterior mean and standard deviation over the numerical solution can be
made to exactly coincide with the value given by the deterministic method and
its local truncation error respectively. We provide a rigorous proof of the
convergence of these new methods, as well as an empirical investigation (up to
fifth order) demonstrating their convergence rates in practice.
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs
Authors: Shahin Jabbari, Ryan Rogers, Aaron Roth, Zhiwei Steven Wu
Date: 2015-06-06 00:00:00
Proceeding: NeurIPS 2016 12

We define and study the problem of predicting the solution to a linear
program (LP) given only partial information about its objective and
constraints. This generalizes the problem of learning to predict the purchasing
behavior of a rational agent who has an unknown objective function, that has
been studied under the name "Learning from Revealed Preferences". We give
mistake bound learning algorithms in two settings: in the first, the objective
of the LP is known to the learner but there is an arbitrary, fixed set of
constraints which are unknown. Each example is defined by an additional known
constraint and the goal of the learner is to predict the optimal solution of
the LP given the union of the known and unknown constraints. This models the
problem of predicting the behavior of a rational agent whose goals are known,
but whose resources are unknown. In the second setting, the objective of the LP
is unknown, and changing in a controlled way. The constraints of the LP may
also change every day, but are known. An example is given by a set of
constraints and partial information about the objective, and the task of the
learner is again to predict the optimal solution of the partially known LP.
Video Analysis of "YouTube Funnies" to Aid the Study of Human Gait and Falls - Preliminary Results and Proof of Concept
Authors: Babak Taati, Pranay Lohia, Avril Mansfield, Ahmed Ashraf
Date: 2016-10-26 00:00:00

Because falls are funny, YouTube and other video sharing sites contain a
large repository of real-life falls. We propose extracting gait and balance
information from these videos to help us better understand some of the factors
that contribute to falls. Proof-of-concept is explored in a single video
containing multiple (n=14) falls/non-falls in the presence of an unexpected
obstacle. The analysis explores: computing spatiotemporal parameters of gait in
a video captured from an arbitrary viewpoint; the relationship between
parameters of gait from the last few steps before the obstacle and falling vs.
not falling; and the predictive capacity of a multivariate model in predicting
a fall in the presence of an unexpected obstacle. Homography transformations
correct the perspective projection distortion and allow for the consistent
tracking of gait parameters as an individual walks in an arbitrary direction in
the scene. A synthetic top view allows for computing the average stride length
and a synthetic side view allows for measuring up and down motions of the head.
In leave-one-out cross-validation, we were able to correctly predict whether a
person would fall or not in 11 out of the 14 cases (78.6%), just by looking at
the average stride length and the range of vertical head motion during the 1-4
most recent steps prior to reaching the obstacle.
Kernel Bayesian Inference with Posterior Regularization
Authors: Yang Song, Jun Zhu, Yong Ren
Date: 2016-07-07 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Bayesian Inference, regression

We propose a vector-valued regression problem whose solution is equivalent to
the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior
distribution. This equivalence provides a new understanding of kernel Bayesian
inference. Moreover, the optimization problem induces a new regularization for
the posterior embedding estimator, which is faster and has comparable
performance to the squared regularization in kernel Bayes' rule. This
regularization coincides with a former thresholding approach used in kernel
POMDPs whose consistency remains to be established. Our theoretical work solves
this open problem and provides consistency analysis in regression settings.
Based on our optimizational formulation, we propose a flexible Bayesian
posterior regularization framework which for the first time enables us to put
regularization at the distribution level. We apply this method to nonparametric
state-space filtering tasks with extremely nonlinear dynamics and show
performance gains over all other baselines.
Automatic and Manual Segmentation of Hippocampus in Epileptic Patients MRI
Authors: Mohammad-Parsa Hosseini, Mohammad-Reza Nazem-Zadeh, Dario Pompili, Kourosh Jafari-Khouzani, Kost Elisevich, Hamid Soltanian-Zadeh
Date: 2016-10-24 00:00:00
Tasks: Hippocampus, Segmentation

The hippocampus is a seminal structure in the most common surgically-treated
form of epilepsy. Accurate segmentation of the hippocampus aids in establishing
asymmetry regarding size and signal characteristics in order to disclose the
likely site of epileptogenicity. With sufficient refinement, it may ultimately
aid in the avoidance of invasive monitoring with its expense and risk for the
patient. To this end, a reliable and consistent method for segmentation of the
hippocampus from magnetic resonance imaging (MRI) is needed. In this work, we
present a systematic and statistical analysis approach for evaluation of
automated segmentation methods in order to establish one that reliably
approximates the results achieved by manual tracing of the hippocampus.
Content Selection in Data-to-Text Systems: A Survey
Authors: Dimitra Gkatzia
Date: 2016-10-26 00:00:00
Tasks: Data-to-Text Generation, Survey, Text Generation

Data-to-text systems are powerful in generating reports from data
automatically and thus they simplify the presentation of complex data. Rather
than presenting data using visualisation techniques, data-to-text systems use
natural (human) language, which is the most common way for human-human
communication. In addition, data-to-text systems can adapt their output content
to users' preferences, background or interests and therefore they can be
pleasant for users to interact with. Content selection is an important part of
every data-to-text system, because it is the module that determines which from
the available information should be conveyed to the user. This survey initially
introduces the field of data-to-text generation, describes the general
data-to-text system architecture and then it reviews the state-of-the-art
content selection methods. Finally, it provides recommendations for choosing an
approach and discusses opportunities for future research.
Single Pass PCA of Matrix Products
Authors: Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis
Date: 2016-10-21 00:00:00
Proceeding: NeurIPS 2016 12

In this paper we present a new algorithm for computing a low rank
approximation of the product $A^TB$ by taking only a single pass of the two
matrices $A$ and $B$. The straightforward way to do this is to (a) first sketch
$A$ and $B$ individually, and then (b) find the top components using PCA on the
sketch. Our algorithm in contrast retains additional summary information about
$A,B$ (e.g. row and column norms etc.) and uses this additional information to
obtain an improved approximation from the sketches. Our main analytical result
establishes a comparable spectral norm guarantee to existing two-pass methods;
in addition we also provide results from an Apache Spark implementation that
shows better computational and statistical performance on real-world and
synthetic evaluation datasets.
Generating Videos with Scene Dynamics
Authors: Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
Date: 2016-09-08 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Action Classification, Future prediction, General Classification, Generative Adversarial Network, Representation Learning, Self-Supervised Action Recognition, Video Generation, Video Recognition, Video Understanding

We capitalize on large amounts of unlabeled video in order to learn a model
of scene dynamics for both video recognition tasks (e.g. action classification)
and video generation tasks (e.g. future prediction). We propose a generative
adversarial network for video with a spatio-temporal convolutional architecture
that untangles the scene's foreground from the background. Experiments suggest
this model can generate tiny videos up to a second at full frame rate better
than simple baselines, and we show its utility at predicting plausible futures
of static images. Moreover, experiments and visualizations show the model
internally learns useful features for recognizing actions with minimal
supervision, suggesting scene dynamics are a promising signal for
representation learning. We believe generative video models can impact many
applications in video understanding and simulation.
Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks
Authors: Carsten Schnober, Steffen Eger, Erik-Lân Do Dinh, Iryna Gurevych
Date: 2016-10-25 00:00:00
Proceeding: COLING 2016 12
Tasks: Decoder, Grapheme-to-Phoneme Conversion, Lemmatization, Optical Character Recognition (OCR), speech-recognition, Speech Recognition, Spelling Correction

We analyze the performance of encoder-decoder neural models and compare them
with well-known established methods. The latter represent different classes of
traditional approaches that are applied to the monotone sequence-to-sequence
tasks OCR post-correction, spelling correction, grapheme-to-phoneme conversion,
and lemmatization. Such tasks are of practical relevance for various
higher-level research fields including digital humanities, automatic text
correction, and speech recognition. We investigate how well generic
deep-learning approaches adapt to these tasks, and how they perform in
comparison with established and more specialized methods, including our own
adaptation of pruned CRFs.
Quantum-enhanced machine learning
Authors: Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel
Date: 2016-10-26 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The emerging field of quantum machine learning has the potential to
substantially aid in the problems and scope of artificial intelligence. This is
only enhanced by recent successes in the field of classical machine learning.
In this work we propose an approach for the systematic treatment of machine
learning, from the perspective of quantum information. Our approach is general
and covers all three main branches of machine learning: supervised,
unsupervised and reinforcement learning. While quantum improvements in
supervised and unsupervised learning have been reported, reinforcement learning
has received much less attention. Within our approach, we tackle the problem of
quantum enhancements in reinforcement learning as well, and propose a
systematic scheme for providing improvements. As an example, we show that
quadratic improvements in learning efficiency, and exponential improvements in
performance over limited time periods, can be obtained for a broad class of
learning problems.
An Improved Approach for Prediction of Parkinson's Disease using Machine Learning Techniques
Authors: Kamal Nayan Reddy Challa, Venkata Sasank Pagolu, Ganapati Panda, Babita Majhi
Date: 2016-10-26 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, regression

Parkinson's disease (PD) is one of the major public health problems in the
world. It is a well-known fact that around one million people suffer from
Parkinson's disease in the United States whereas the number of people suffering
from Parkinson's disease worldwide is around 5 million. Thus, it is important
to predict Parkinson's disease in early stages so that early plan for the
necessary treatment can be made. People are mostly familiar with the motor
symptoms of Parkinson's disease, however, an increasing amount of research is
being done to predict the Parkinson's disease from non-motor symptoms that
precede the motor ones. If an early and reliable prediction is possible then a
patient can get a proper treatment at the right time. Nonmotor symptoms
considered are Rapid Eye Movement (REM) sleep Behaviour Disorder (RBD) and
olfactory loss. Developing machine learning models that can help us in
predicting the disease can play a vital role in early prediction. In this
paper, we extend a work which used the non-motor features such as RBD and
olfactory loss. Along with this the extended work also uses important
biomarkers. In this paper, we try to model this classifier using different
machine learning models that have not been used before. We developed automated
diagnostic models using Multilayer Perceptron, BayesNet, Random Forest and
Boosted Logistic Regression. It has been observed that Boosted Logistic
Regression provides the best performance with an impressive accuracy of 97.159
% and the area under the ROC curve was 98.9%. Thus, it is concluded that these
models can be used for early prediction of Parkinson's disease.
Relevant sparse codes with variational information bottleneck
Authors: Matthew Chalk, Olivier Marre, Gasper Tkacik
Date: 2016-05-24 00:00:00
Proceeding: NeurIPS 2016 12

In many applications, it is desirable to extract only the relevant aspects of
data. A principled way to do this is the information bottleneck (IB) method,
where one seeks a code that maximizes information about a 'relevance' variable,
Y, while constraining the information encoded about the original data, X.
Unfortunately however, the IB method is computationally demanding when data are
high-dimensional and/or non-gaussian. Here we propose an approximate
variational scheme for maximizing a lower bound on the IB objective, analogous
to variational EM. Using this method, we derive an IB algorithm to recover
features that are both relevant and sparse. Finally, we demonstrate how
kernelized versions of the algorithm can be used to address a broad range of
problems with non-linear relation between X and Y.
Word Embeddings and Their Use In Sentence Classification Tasks
Authors: Amit Mandelbaum, Adi Shalev
Date: 2016-10-26 00:00:00
Tasks: Classification, General Classification, Sentence, Sentence Classification, Word Embeddings

This paper have two parts. In the first part we discuss word embeddings. We
discuss the need for them, some of the methods to create them, and some of
their interesting properties. We also compare them to image embeddings and see
how word embedding and image embedding can be combined to perform different
tasks. In the second part we implement a convolutional neural network trained
on top of pre-trained word vectors. The network is used for several
sentence-level classification tasks, and achieves state-of-art (or comparable)
results, demonstrating the great power of pre-trainted word embeddings over
random ones.
A self-tuning Firefly algorithm to tune the parameters of Ant Colony System (ACSFA)
Authors: M. K. A. Ariyaratne, T. G. I. Fernando, S. Weerakoon
Date: 2016-10-26 00:00:00
Tasks: Job Shop Scheduling, Scheduling

Ant colony system (ACS) is a promising approach which has been widely used in
problems such as Travelling Salesman Problems (TSP), Job shop scheduling
problems (JSP) and Quadratic Assignment problems (QAP). In its original
implementation, parameters of the algorithm were selected by trial and error
approach. Over the last few years, novel approaches have been proposed on
adapting the parameters of ACS in improving its performance. The aim of this
paper is to use a framework introduced for self-tuning optimization algorithms
combined with the firefly algorithm (FA) to tune the parameters of the ACS
solving symmetric TSP problems. The FA optimizes the problem specific
parameters of ACS while the parameters of the FA are tuned by the selected
framework itself. With this approach, the user neither has to work with the
parameters of ACS nor the parameters of FA. Using common symmetric TSP problems
we demonstrate that the framework fits well for the ACS. A detailed statistical
analysis further verifies the goodness of the new ACS over the existing ACS and
also of the other techniques used to tune the parameters of ACS.