pwc-archive/papers-with-abstracts huggingface dataset:
Posterior sampling for reinforcement learning: worst-case regret bounds
Authors: Shipra Agrawal, Randy Jia
Date: 2017-05-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

We present an algorithm based on posterior sampling (aka Thompson sampling) that achieves near-optimal worst-case regret bounds when the underlying Markov Decision Process (MDP) is communicating with a finite, though unknown, diameter. Our main result is a high probability regret upper bound of $\tilde{O}(DS\sqrt{AT})$ for any communicating MDP with $S$ states, $A$ actions and diameter $D$. Here, regret compares the total reward achieved by the algorithm to the total expected reward of an optimal infinite-horizon undiscounted average reward policy, in time horizon $T$. This result closely matches the known lower bound of $\Omega(\sqrt{DSAT})$. Our techniques involve proving some novel results about the anti-concentration of Dirichlet distribution, which may be of independent interest.
Segmentation of 3D High-frequency Ultrasound Images of Human Lymph Nodes Using Graph Cut with Energy Functional Adapted to Local Intensity Distribution
Authors: Jen-wei Kuo, Jonathan Mamou, Yao Wang, Emi Saegusa-Beecroft, Junji Machi, Ernest J. Feleppa
Date: 2017-05-19 00:00:00

Previous studies by our group have shown that three-dimensional
high-frequency quantitative ultrasound methods have the potential to
differentiate metastatic lymph nodes from cancer-free lymph nodes dissected
from human cancer patients. To successfully perform these methods inside the
lymph node parenchyma, an automatic segmentation method is highly desired to
exclude the surrounding thin layer of fat from quantitative ultrasound
processing and accurately correct for ultrasound attenuation. In high-frequency
ultrasound images of lymph nodes, the intensity distribution of lymph node
parenchyma and fat varies spatially because of acoustic attenuation and
focusing effects. Thus, the intensity contrast between two object regions
(e.g., lymph node parenchyma and fat) is also spatially varying. In our
previous work, nested graph cut demonstrated its ability to simultaneously
segment lymph node parenchyma, fat, and the outer phosphate-buffered saline
bath even when some boundaries are lost because of acoustic attenuation and
focusing effects. This paper describes a novel approach called graph cut with
locally adaptive energy to further deal with spatially varying distributions of
lymph node parenchyma and fat caused by inhomogeneous acoustic attenuation. The
proposed method achieved Dice similarity coefficients of 0.937+-0.035 when
compared to expert manual segmentation on a representative dataset consisting
of 115 three-dimensional lymph node images obtained from colorectal cancer
patients.
A Lightweight Regression Method to Infer Psycholinguistic Properties for Brazilian Portuguese
Authors: Leandro B. dos Santos, Magali S. Duran, Nathan S. Hartmann, Arnaldo Candido Jr., Gustavo H. Paetzold, Sandra M. Aluisio
Date: 2017-05-19 00:00:00
Tasks: regression, Text Simplification

Psycholinguistic properties of words have been used in various approaches to
Natural Language Processing tasks, such as text simplification and readability
assessment. Most of these properties are subjective, involving costly and
time-consuming surveys to be gathered. Recent approaches use the limited
datasets of psycholinguistic properties to extend them automatically to large
lexicons. However, some of the resources used by such approaches are not
available to most languages. This study presents a method to infer
psycholinguistic properties for Brazilian Portuguese (BP) using regressors
built with a light set of features usually available for less resourced
languages: word length, frequency lists, lexical databases composed of school
dictionaries and word embedding models. The correlations between the properties
inferred are close to those obtained by related works. The resulting resource
contains 26,874 words in BP annotated with concreteness, age of acquisition,
imageability and subjective frequency.
Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips
Authors: Antonio Jimeno Yepes, Jianbin Tang, Benjamin Scott Mashford
Date: 2017-05-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, GPU

Deep Neural Networks (DNN) achieve human level performance in many image
analytics tasks but DNNs are mostly deployed to GPU platforms that consume a
considerable amount of power. New hardware platforms using lower precision
arithmetic achieve drastic reductions in power consumption. More recently,
brain-inspired spiking neuromorphic chips have achieved even lower power
consumption, on the order of milliwatts, while still offering real-time
processing.
  However, for deploying DNNs to energy efficient neuromorphic chips the
incompatibility between continuous neurons and synaptic weights of traditional
DNNs, discrete spiking neurons and synapses of neuromorphic chips need to be
overcome. Previous work has achieved this by training a network to learn
continuous probabilities, before it is deployed to a neuromorphic architecture,
such as IBM TrueNorth Neurosynaptic System, by random sampling these
probabilities.
  The main contribution of this paper is a new learning algorithm that learns a
TrueNorth configuration ready for deployment. We achieve this by training
directly a binary hardware crossbar that accommodates the TrueNorth axon
configuration constrains and we propose a different neuron model.
  Results of our approach trained on electroencephalogram (EEG) data show a
significant improvement with previous work (76% vs 86% accuracy) while
maintaining state of the art performance on the MNIST handwritten data set.
Parameter Adaptation and Criticality in Particle Swarm Optimization
Authors: Carlos Garcia Cordero
Date: 2017-05-19 00:00:00

Generality is one of the main advantages of heuristic algorithms, as such,
multiple parameters are exposed to the user with the objective of allowing them
to shape the algorithms to their specific needs. Parameter selection,
therefore, becomes an intrinsic problem of every heuristic algorithm. Selecting
good parameter values relies not only on knowledge related to the problem at
hand, but to the algorithms themselves. This research explores the usage of
self-organized criticality to reduce user interaction in the process of
selecting suitable parameters for particle swarm optimization (PSO) heuristics.
A particle swarm variant (named Adaptive PSO) with self-organized criticality
is developed and benchmarked against the standard PSO. Criticality is observed
in the dynamic behaviour of this swarm and excellent results are observed in
the long run. In contrast with the standard PSO, the Adaptive PSO does not
stagnate at any point in time, balancing the concepts of exploration and
exploitation better. A software platform for experimenting with particle
swarms, called PSO Laboratory, is also developed. This software is used to test
the standard PSO as well as all other PSO variants developed in the process of
creating the Adaptive PSO. As the software is intended to be of aid to future
and related research, special attention has been put in the development of a
friendly graphical user interface. Particle swarms are executed in real time,
allowing users to experiment by changing parameters on-the-fly.
A Survey of Neuromorphic Computing and Neural Networks in Hardware
Authors: Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J. Douglas Birdwell, Mark E. Dean, Garrett S. Rose, James S. Plank
Date: 2017-05-19 00:00:00

Neuromorphic computing has come to refer to a variety of brain-inspired
computers, devices, and models that contrast the pervasive von Neumann computer
architecture. This biologically inspired approach has created highly connected
synthetic neurons and synapses that can be used to model neuroscience theories
as well as solve challenging machine learning problems. The promise of the
technology is to create a brain-like ability to learn and adapt, but the
technical challenges are significant, starting with an accurate neuroscience
model of how the brain works, to finding materials and engineering
breakthroughs to build devices to support these models, to creating a
programming framework so the systems can learn, to creating applications with
brain-like capabilities. In this work, we provide a comprehensive survey of the
research and motivations for neuromorphic computing over its history. We begin
with a 35-year review of the motivations and drivers of neuromorphic computing,
then look at the major research areas of the field, which we define as
neuro-inspired models, algorithms and learning approaches, hardware and
devices, supporting systems, and finally applications. We conclude with a broad
discussion on the major research topics that need to be addressed in the coming
years to see the promise of neuromorphic computing fulfilled. The goals of this
work are to provide an exhaustive review of the research conducted in
neuromorphic computing since the inception of the term, and to motivate further
work by illuminating gaps in the field where new research is needed.
The Kinetics Human Action Video Dataset
Authors: Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, Mustafa Suleyman, Andrew Zisserman
Date: 2017-05-19 00:00:00
Tasks: Action Classification, General Classification

We describe the DeepMind Kinetics human action video dataset. The dataset
contains 400 human action classes, with at least 400 video clips for each
action. Each clip lasts around 10s and is taken from a different YouTube video.
The actions are human focussed and cover a broad range of classes including
human-object interactions such as playing instruments, as well as human-human
interactions such as shaking hands. We describe the statistics of the dataset,
how it was collected, and give some baseline performance figures for neural
network architectures trained and tested for human action classification on
this dataset. We also carry out a preliminary analysis of whether imbalance in
the dataset leads to bias in the classifiers.
Atari games and Intel processors
Authors: Robert Adamski, Tomasz Grel, Maciej Klimek, Henryk Michalewski
Date: 2017-05-19 00:00:00
Tasks: Atari Games, BIG-bench Machine Learning, CPU, Deep Reinforcement Learning, Math, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The asynchronous nature of the state-of-the-art reinforcement learning
algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes
them exceptionally suitable for CPU computations. However, given the fact that
deep reinforcement learning often deals with interpreting visual information, a
large part of the train and inference time is spent performing convolutions. In
this work we present our results on learning strategies in Atari games using a
Convolutional Neural Network, the Math Kernel Library and TensorFlow 0.11rc0
machine learning framework. We also analyze effects of asynchronous
computations on the convergence of reinforcement learning algorithms.
Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut
Authors: Zhenfang Hu, Gang Pan, Yueming Wang, Zhaohui Wu
Date: 2014-03-25 00:00:00
Tasks: Clustering, Dimensionality Reduction

Dimensionality reduction, cluster analysis, and sparse representation are
basic components in machine learning. However, their relationships have not yet
been fully investigated. In this paper, we find that the spectral graph theory
underlies a series of these elementary methods and can unify them into a
complete framework. The methods include PCA, K-means, Laplacian eigenmap (LE),
ratio cut (Rcut), and a new sparse representation method developed by us,
called spectral sparse representation (SSR). Further, extended relations to
conventional over-complete sparse representations (e.g., method of optimal
directions, KSVD), manifold learning (e.g., kernel PCA, multidimensional
scaling, Isomap, locally linear embedding), and subspace clustering (e.g.,
sparse subspace clustering, low-rank representation) are incorporated. We show
that, under an ideal condition from the spectral graph theory, PCA, K-means,
LE, and Rcut are unified together. And when the condition is relaxed, the
unification evolves to SSR, which lies in the intermediate between PCA/LE and
K-mean/Rcut. An efficient algorithm, NSCrt, is developed to solve the sparse
codes of SSR. SSR combines merits of both sides: its sparse codes reduce
dimensionality of data meanwhile revealing cluster structure. For its inherent
relation to cluster analysis, the codes of SSR can be directly used for
clustering. Scut, a clustering approach derived from SSR reaches the
state-of-the-art performance in the spectral clustering family. The one-shot
solution obtained by Scut is comparable to the optimal result of K-means that
are run many times. Experiments on various data sets demonstrate the properties
and strengths of SSR, NSCrt, and Scut.
Local Shape Spectrum Analysis for 3D Facial Expression Recognition
Authors: Dmytro Derkach, Federico M. Sukno
Date: 2017-05-19 00:00:00
Tasks: 3D Facial Expression Recognition, 3D geometry, Facial Expression Recognition, Facial Expression Recognition (FER)

We investigate the problem of facial expression recognition using 3D data.
Building from one of the most successful frameworks for facial analysis using
exclusively 3D geometry, we extend the analysis from a curve-based
representation into a spectral representation, which allows a complete
description of the underlying surface that can be further tuned to the desired
level of detail. Spectral representations are based on the decomposition of the
geometry in its spatial frequency components, much like a Fourier transform,
which are related to intrinsic characteristics of the surface. In this work, we
propose the use of Graph Laplacian Features (GLF), which results from the
projection of local surface patches into a common basis obtained from the Graph
Laplacian eigenspace. We test the proposed approach in the BU-3DFE database in
terms of expressions and Action Units recognition. Our results confirm that the
proposed GLF produces consistently higher recognition rates than the
curves-based approach, thanks to a more complete description of the surface,
while requiring a lower computational complexity. We also show that the GLF
outperform the most popular alternative approach for spectral representation,
Shape- DNA, which is based on the Laplace Beltrami Operator and cannot provide
a stable basis that guarantee that the extracted signatures for the different
patches are directly comparable.
CDS Rate Construction Methods by Machine Learning Techniques
Authors: Raymond Brummelhuis, Zhongmin Luo
Date: 2017-05-19 00:00:00
Tasks: BIG-bench Machine Learning, Management

Regulators require financial institutions to estimate counterparty default
risks from liquid CDS quotes for the valuation and risk management of OTC
derivatives. However, the vast majority of counterparties do not have liquid
CDS quotes and need proxy CDS rates. Existing methods cannot account for
counterparty-specific default risks; we propose to construct proxy CDS rates by
associating to illiquid counterparty liquid CDS Proxy based on Machine Learning
Techniques. After testing 156 classifiers from 8 most popular classifier
families, we found that some classifiers achieve highly satisfactory accuracy
rates. Furthermore, we have rank-ordered the performances and investigated
performance variations amongst and within the 8 classifier families. This paper
is, to the best of our knowledge, the first systematic study of CDS Proxy
construction by Machine Learning techniques, and the first systematic
classifier comparison study based entirely on financial market data. Its
findings both confirm and contrast existing classifier performance literature.
Given the typically highly correlated nature of financial data, we investigated
the impact of correlation on classifier performance. The techniques used in
this paper should be of interest for financial institutions seeking a CDS Proxy
method, and can serve for proxy construction for other financial variables.
Some directions for future research are indicated.
Evaluation of Direct Haptic 4D Volume Rendering of Partially Segmented Data for Liver Puncture Simulation
Authors: Andre Mastmeyer, Dirk Fortmeier, Heinz Handels
Date: 2017-05-19 00:00:00

This work presents an evaluation study using a force feedback evaluation
framework for a novel direct needle force volume rendering concept in the
context of liver puncture simulation. PTC/PTCD puncture interventions targeting
the bile ducts have been selected to illustrate this concept. The haptic
algorithms of the simulator system are based on (1) partially segmented patient
image data and (2) a non-linear spring model effective at organ borders. The
primary aim is to quantitatively evaluate force errors caused by our patient
modeling approach, in comparison to haptic force output obtained from using
gold-standard, completely manually-segmented data. The evaluation of the force
algorithms compared to a force output from fully manually segmented
gold-standard patient models, yields a low mean of 0.12 N root mean squared
force error and up to 1.6 N for systematic maximum absolute errors. Force
errors were evaluated on 31,222 preplanned test paths from 10 patients. Only
twelve percent of the emitted forces along these paths were affected by errors.
This is the first study evaluating haptic algorithms with deformable virtual
patients in silico. We prove haptic rendering plausibility on a very high
number of test paths. Important errors are below just noticeable differences
for the hand-arm system.
Practical Algorithms for Best-K Identification in Multi-Armed Bandits
Authors: Haotian Jiang, Jian Li, Mingda Qiao
Date: 2017-05-19 00:00:00
Tasks: Multi-Armed Bandits

In the Best-$K$ identification problem (Best-$K$-Arm), we are given $N$
stochastic bandit arms with unknown reward distributions. Our goal is to
identify the $K$ arms with the largest means with high confidence, by drawing
samples from the arms adaptively. This problem is motivated by various
practical applications and has attracted considerable attention in the past
decade. In this paper, we propose new practical algorithms for the Best-$K$-Arm
problem, which have nearly optimal sample complexity bounds (matching the lower
bound up to logarithmic factors) and outperform the state-of-the-art algorithms
for the Best-$K$-Arm problem (even for $K=1$) in practice.
Richardson-Lucy Deblurring for Moving Light Field Cameras
Authors: Donald G. Dansereau, Anders Eriksson, Jürgen Leitner
Date: 2016-06-14 00:00:00
Tasks: Deblurring, Depth Estimation

We generalize Richardson-Lucy (RL) deblurring to 4-D light fields by
replacing the convolution steps with light field rendering of motion blur. The
method deals correctly with blur caused by 6-degree-of-freedom camera motion in
complex 3-D scenes, without performing depth estimation. We introduce a novel
regularization term that maintains parallax information in the light field
while reducing noise and ringing. We demonstrate the method operating
effectively on rendered scenes and scenes captured using an off-the-shelf light
field camera. An industrial robot arm provides repeatable and known
trajectories, allowing us to establish quantitative performance in complex 3-D
scenes. Qualitative and quantitative results confirm the effectiveness of the
method, including commonly occurring cases for which previously published
methods fail. We include mathematical proof that the algorithm converges to the
maximum-likelihood estimate of the unblurred scene under Poisson noise. We
expect extension to blind methods to be possible following the generalization
of 2-D Richardson-Lucy to blind deconvolution.
Affine-Gradient Based Local Binary Pattern Descriptor for Texture Classiffication
Authors: You Hao, Shirui Li, Hanlin Mo, Hua Li
Date: 2017-05-19 00:00:00
Tasks: feature selection, General Classification, Texture Classification

We present a novel Affine-Gradient based Local Binary Pattern (AGLBP)
descriptor for texture classification. It is very hard to describe complicated
texture using single type information, such as Local Binary Pattern (LBP),
which just utilizes the sign information of the difference between the pixel
and its local neighbors. Our descriptor has three characteristics: 1) In order
to make full use of the information contained in the texture, the
Affine-Gradient, which is different from Euclidean-Gradient and invariant to
affine transformation is incorporated into AGLBP. 2) An improved method is
proposed for rotation invariance, which depends on the reference direction
calculating respect to local neighbors. 3) Feature selection method,
considering both the statistical frequency and the intraclass variance of the
training dataset, is also applied to reduce the dimensionality of descriptors.
Experiments on three standard texture datasets, Outex12, Outex10 and KTH-TIPS2,
are conducted to evaluate the performance of AGLBP. The results show that our
proposed descriptor gets better performance comparing to some state-of-the-art
rotation texture descriptors in texture classification.
Fiber Orientation Estimation Guided by a Deep Network
Authors: Chuyang Ye, Jerry L. Prince
Date: 2017-05-19 00:00:00

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for
noninvasively imaging the brain's white matter tracts. The fiber orientation
(FO) is a key feature computed from dMRI for fiber tract reconstruction.
Because the number of FOs in a voxel is usually small, dictionary-based sparse
reconstruction has been used to estimate FOs with a relatively small number of
diffusion gradients. However, accurate FO estimation in regions with complex FO
configurations in the presence of noise can still be challenging. In this work
we explore the use of a deep network for FO estimation in a dictionary-based
framework and propose an algorithm named Fiber Orientation Reconstruction
guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a
smaller dictionary encoding coarse basis FOs to represent the diffusion
signals. To estimate the mixture fractions of the dictionary atoms (and thus
coarse FOs), a deep network is designed specifically for solving the sparse
reconstruction problem. Here, the smaller dictionary is used to reduce the
computational cost of training. Second, the coarse FOs inform the final FO
estimation, where a larger dictionary encoding dense basis FOs is used and a
weighted l1-norm regularized least squares problem is solved to encourage FOs
that are consistent with the network output. FORDN was evaluated and compared
with state-of-the-art algorithms that estimate FOs using sparse reconstruction
on simulated and real dMRI data, and the results demonstrate the benefit of
using a deep network for FO estimation.