pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Over Priors for Boosting Control of Prosthetic Hands
Authors: Valentina Gregori
Date: 2016-05-24 00:00:00
Tasks: Electromyography (EMG), Learning Theory

The Electromyography (EMG) signal is the electrical activity produced by
cells of skeletal muscles in order to provide a movement. The non-invasive
prosthetic hand works with several electrodes, placed on the stump of an
amputee, that record this signal. In order to favour the control of prosthesis,
the EMG signal is analyzed with algorithms based on machine learning theory to
decide the movement that the subject is going to do. In order to obtain a
significant control of the prosthesis and avoid mismatch between desired and
performed movements, a long training period is needed when we use the
traditional algorithm of machine learning (i.e. Support Vector Machines). An
actual challenge in this field concerns the reduction of the time necessary for
an amputee to learn how to use the prosthesis. Recently, several algorithms
that exploit a form of prior knowledge have been proposed. In general, we refer
to prior knowledge as a past experience available in the form of models. In our
case an amputee, that attempts to perform some movements with the prosthesis,
could use experience from different subjects that are already able to perform
those movements. The aim of this work is to verify, with a computational
investigation, if for an amputee this kind of previous experience is useful in
order to reduce the training time and boost the prosthetic control.
Furthermore, we want to understand if and how the final results change when the
previous knowledge of intact or amputated subjects is used for a new amputee.
Our experiments indicate that: (1) the use of experience, from other subjects
already trained to perform a task, makes us able to reduce the training time of
about an order of magnitude; (2) it seems that an amputee that tries to learn
to use the prosthesis doesn't reach different results when he/she exploits
previous experience of amputees or intact.
Random Fourier Features for Operator-Valued Kernels
Authors: Romain Brault, Florence d'Alché-Buc, Markus Heinonen
Date: 2016-05-09 00:00:00
Tasks: Multi-Task Learning, Translation

Devoted to multi-task learning and structured output learning,
operator-valued kernels provide a flexible tool to build vector-valued
functions in the context of Reproducing Kernel Hilbert Spaces. To scale up
these methods, we extend the celebrated Random Fourier Feature methodology to
get an approximation of operator-valued kernels. We propose a general principle
for Operator-valued Random Fourier Feature construction relying on a
generalization of Bochner's theorem for translation-invariant operator-valued
Mercer kernels. We prove the uniform convergence of the kernel approximation
for bounded and unbounded operator random Fourier features using appropriate
Bernstein matrix concentration inequality. An experimental proof-of-concept
shows the quality of the approximation and the efficiency of the corresponding
linear models on example datasets.
Hierarchical Memory Networks
Authors: Sarath Chandar, Sungjin Ahn, Hugo Larochelle, Pascal Vincent, Gerald Tesauro, Yoshua Bengio
Date: 2016-05-24 00:00:00
Tasks: Hard Attention, Question Answering, Reinforcement Learning

Memory networks are neural networks with an explicit memory component that
can be both read and written to by the network. The memory is often addressed
in a soft way using a softmax function, making end-to-end training with
backpropagation possible. However, this is not computationally scalable for
applications which require the network to read from extremely large memories.
On the other hand, it is well known that hard attention mechanisms based on
reinforcement learning are challenging to train successfully. In this paper, we
explore a form of hierarchical memory network, which can be considered as a
hybrid between hard and soft attention memory networks. The memory is organized
in a hierarchical structure such that reading from it is done with less
computation than soft attention over a flat memory, while also being easier to
train than hard attention over a flat memory. Specifically, we propose to
incorporate Maximum Inner Product Search (MIPS) in the training and inference
procedures for our hierarchical memory network. We explore the use of various
state-of-the art approximate MIPS techniques and report results on
SimpleQuestions, a challenging large scale factoid question answering task.
Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm
Authors: Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
Date: 2016-05-05 00:00:00

We study the K-armed dueling bandit problem, a variation of the standard
stochastic bandit problem where the feedback is limited to relative comparisons
of a pair of arms. The hardness of recommending Copeland winners, the arms that
beat the greatest number of other arms, is characterized by deriving an
asymptotic regret bound. We propose Copeland Winners Relative Minimum Empirical
Divergence (CW-RMED) and derive an asymptotically optimal regret bound for it.
However, it is not known whether the algorithm can be efficiently computed or
not. To address this issue, we devise an efficient version (ECW-RMED) and
derive its asymptotic regret bound. Experimental comparisons of dueling bandit
algorithms show that ECW-RMED significantly outperforms existing ones.
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays
Authors: Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
Date: 2015-06-02 00:00:00
Tasks: Thompson Sampling

We discuss a multiple-play multi-armed bandit (MAB) problem in which several
arms are selected at each round. Recently, Thompson sampling (TS), a randomized
algorithm with a Bayesian spirit, has attracted much attention for its
empirically excellent performance, and it is revealed to have an optimal regret
bound in the standard single-play MAB problem. In this paper, we propose the
multiple-play Thompson sampling (MP-TS) algorithm, an extension of TS to the
multiple-play MAB problem, and discuss its regret analysis. We prove that MP-TS
for binary rewards has the optimal regret upper bound that matches the regret
lower bound provided by Anantharam et al. (1987). Therefore, MP-TS is the first
computationally efficient algorithm with optimal regret. A set of computer
simulations was also conducted, which compared MP-TS with state-of-the-art
algorithms. We also propose a modification of MP-TS, which is shown to have
better empirical performance.
Semiparametric energy-based probabilistic models
Authors: Jan Humplik, Gašper Tkačik
Date: 2016-05-24 00:00:00
Tasks: Attribute

Probabilistic models can be defined by an energy function, where the
probability of each state is proportional to the exponential of the state's
negative energy. This paper considers a generalization of energy-based models
in which the probability of a state is proportional to an arbitrary positive,
strictly decreasing, and twice differentiable function of the state's energy.
The precise shape of the nonlinear map from energies to unnormalized
probabilities has to be learned from data together with the parameters of the
energy function. As a case study we show that the above generalization of a
fully visible Boltzmann machine yields an accurate model of neural activity of
retinal ganglion cells. We attribute this success to the model's ability to
easily capture distributions whose probabilities span a large dynamic range, a
possible consequence of latent variables that globally couple the system.
Similar features have recently been observed in many datasets, suggesting that
our new method has wide applicability.
Quickest Moving Object Detection
Authors: Dong Lao, Ganesh Sundaramoorthi
Date: 2016-05-24 00:00:00
Tasks: Change Detection, Motion Segmentation, Moving Object Detection, Object, object-detection, Object Detection, Segmentation

We present a general framework and method for simultaneous detection and
segmentation of an object in a video that moves (or comes into view of the
camera) at some unknown time in the video. The method is an online approach
based on motion segmentation, and it operates under dynamic backgrounds caused
by a moving camera or moving nuisances. The goal of the method is to detect and
segment the object as soon as it moves. Due to stochastic variability in the
video and unreliability of the motion signal, several frames are needed to
reliably detect the object. The method is designed to detect and segment with
minimum delay subject to a constraint on the false alarm rate. The method is
derived as a problem of Quickest Change Detection. Experiments on a dataset
show the effectiveness of our method in minimizing detection delay subject to
false alarm constraints.
Experiments in Linear Template Combination using Genetic Algorithms
Authors: Nikhilesh Bhatnagar, Radhika Mamidi
Date: 2016-05-24 00:00:00
Tasks: Sentence, Text Generation

Natural Language Generation systems typically have two parts - strategic
('what to say') and tactical ('how to say'). We present our experiments in
building an unsupervised corpus-driven template based tactical NLG system. We
consider templates as a sequence of words containing gaps. Our idea is based on
the observation that templates are grammatical locally (within their textual
span). We posit the construction of a sentence as a highly restricted sequence
of such templates. This work is an attempt to explore the resulting search
space using Genetic Algorithms to arrive at acceptable solutions. We present a
baseline implementation of this approach which outputs gapped text.
Non-Gaussian Random Generators in Bacteria Foraging Algorithm for Multiobjective Optimization
Authors: Timothy Ganesan, Pandian Vasant, Irraivan Elamvazuthi
Date: 2016-05-24 00:00:00
Tasks: Multiobjective Optimization, Sand

Random generators or stochastic engines are a key component in the structure
of metaheuristic algorithms. This work investigates the effects of non-Gaussian
stochastic engines on the performance of metaheuristics when solving a
real-world optimization problem. In this work, the bacteria foraging algorithm
(BFA) was employed in tandem with four random generators (stochastic engines).
The stochastic engines operate using the Weibull distribution, Gamma
distribution, Gaussian distribution and a chaotic mechanism. The two
non-Gaussian distributions are the Weibull and Gamma distributions. In this
work, the approaches developed were implemented on the real-world
multi-objective resin bonded sand mould problem. The Pareto frontiers obtained
were benchmarked using two metrics; the hyper volume indicator (HVI) and the
proposed Average Explorative Rate (AER) metric. Detail discussions from various
perspectives on the effects of non-Gaussian random generators in metaheuristics
are provided.
Spatio-Temporal Image Boundary Extrapolation
Authors: Apratim Bhattacharyya, Mateusz Malinowski, Mario Fritz
Date: 2016-05-24 00:00:00
Tasks: Video Segmentation, Video Semantic Segmentation

Boundary prediction in images as well as video has been a very active topic
of research and organizing visual information into boundaries and segments is
believed to be a corner stone of visual perception. While prior work has
focused on predicting boundaries for observed frames, our work aims at
predicting boundaries of future unobserved frames. This requires our model to
learn about the fate of boundaries and extrapolate motion patterns. We
experiment on established real-world video segmentation dataset, which provides
a testbed for this new task. We show for the first time spatio-temporal
boundary extrapolation in this challenging scenario. Furthermore, we show
long-term prediction of boundaries in situations where the motion is governed
by the laws of physics. We successfully predict boundaries in a billiard
scenario without any assumptions of a strong parametric model or any object
notion. We argue that our model has with minimalistic model assumptions derived
a notion of 'intuitive physics' that can be applied to novel scenes.
Consistency Analysis for the Doubly Stochastic Dirichlet Process
Authors: Xing Sun, Nelson H. C. Yung, Edmund Y. Lam, Hayden K. -H. So
Date: 2016-05-24 00:00:00

This technical report proves components consistency for the Doubly Stochastic
Dirichlet Process with exponential convergence of posterior probability. We
also present the fundamental properties for DSDP as well as inference
algorithms. Simulation toy experiment and real-world experiment results for
single and multi-cluster also support the consistency proof. This report is
also a support document for the paper "Computationally Efficient Hyperspectral
Data Learning Based on the Doubly Stochastic Dirichlet Process".
Multi-Level Analysis and Annotation of Arabic Corpora for Text-to-Sign Language MT
Authors: Abdelaziz Lakhfif, Mohammed T. Laskri, Eric Atwell
Date: 2016-05-24 00:00:00

In this paper, we present an ongoing effort in lexical semantic analysis and
annotation of Modern Standard Arabic (MSA) text, a semi automatic annotation
tool concerned with the morphologic, syntactic, and semantic levels of
description.
Combining Recurrent and Convolutional Neural Networks for Relation Classification
Authors: Ngoc Thang Vu, Heike Adel, Pankaj Gupta, Hinrich Schütze
Date: 2016-05-24 00:00:00
Proceeding: NAACL 2016 6
Tasks: Classification, General Classification, Relation, Relation Classification

This paper investigates two different neural architectures for the task of
relation classification: convolutional neural networks and recurrent neural
networks. For both models, we demonstrate the effect of different architectural
choices. We present a new context representation for convolutional neural
networks for relation classification (extended middle context). Furthermore, we
propose connectionist bi-directional recurrent neural networks and introduce
ranking loss for their optimization. Finally, we show that combining
convolutional and recurrent neural networks using a simple voting scheme is
accurate enough to improve results. Our neural models achieve state-of-the-art
results on the SemEval 2010 relation classification task.
Minimum Regret Search for Single- and Multi-Task Optimization
Authors: Jan Hendrik Metzen
Date: 2016-02-02 00:00:00
Tasks: Bayesian Optimization

We propose minimum regret search (MRS), a novel acquisition function for
Bayesian optimization. MRS bears similarities with information-theoretic
approaches such as entropy search (ES). However, while ES aims in each query at
maximizing the information gain with respect to the global maximum, MRS aims at
minimizing the expected simple regret of its ultimate recommendation for the
optimum. While empirically ES and MRS perform similar in most of the cases, MRS
produces fewer outliers with high simple regret than ES. We provide empirical
results both for a synthetic single-task optimization problem as well as for a
simulated multi-task robotic control problem.
DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images
Authors: Zhuoyao Zhong, Lianwen Jin, Shuye Zhang, Ziyong Feng
Date: 2016-05-24 00:00:00
Tasks: Region Proposal, Text Classification, Text Detection

In this paper, we develop a novel unified framework called DeepText for text
region proposal generation and text detection in natural images via a fully
convolutional neural network (CNN). First, we propose the inception region
proposal network (Inception-RPN) and design a set of text characteristic prior
bounding boxes to achieve high word recall with only hundred level candidate
proposals. Next, we present a powerful textdetection network that embeds
ambiguous text category (ATC) information and multilevel region-of-interest
pooling (MLRP) for text and non-text classification and accurate localization.
Finally, we apply an iterative bounding box voting scheme to pursue high recall
in a complementary manner and introduce a filtering algorithm to retain the
most suitable bounding box, while removing redundant inner and outer boxes for
each text instance. Our approach achieves an F-measure of 0.83 and 0.85 on the
ICDAR 2011 and 2013 robust text detection benchmarks, outperforming previous
state-of-the-art results.
Optimization Beyond Prediction: Prescriptive Price Optimization
Authors: Shinji Ito, Ryohei Fujimaki
Date: 2016-05-18 00:00:00
Tasks: Prediction

This paper addresses a novel data science problem, prescriptive price
optimization, which derives the optimal price strategy to maximize future
profit/revenue on the basis of massive predictive formulas produced by machine
learning. The prescriptive price optimization first builds sales forecast
formulas of multiple products, on the basis of historical data, which reveal
complex relationships between sales and prices, such as price elasticity of
demand and cannibalization. Then, it constructs a mathematical optimization
problem on the basis of those predictive formulas. We present that the
optimization problem can be formulated as an instance of binary quadratic
programming (BQP). Although BQP problems are NP-hard in general and
computationally intractable, we propose a fast approximation algorithm using a
semi-definite programming (SDP) relaxation, which is closely related to the
Goemans-Williamson's Max-Cut approximation. Our experiments on simulation and
real retail datasets show that our prescriptive price optimization
simultaneously derives the optimal prices of tens/hundreds products with
practical computational time, that potentially improve 8.2% of gross profit of
those products.
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections
Authors: Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang
Date: 2013-04-19 00:00:00

We consider stochastic strongly convex optimization with a complex inequality
constraint. This complex inequality constraint may lead to computationally
expensive projections in algorithmic iterations of the stochastic gradient
descent~(SGD) methods. To reduce the computation costs pertaining to the
projections, we propose an Epoch-Projection Stochastic Gradient
Descent~(Epro-SGD) method. The proposed Epro-SGD method consists of a sequence
of epochs; it applies SGD to an augmented objective function at each iteration
within the epoch, and then performs a projection at the end of each epoch.
Given a strongly convex optimization and for a total number of $T$ iterations,
Epro-SGD requires only $\log(T)$ projections, and meanwhile attains an optimal
convergence rate of $O(1/T)$, both in expectation and with a high probability.
To exploit the structure of the optimization problem, we propose a proximal
variant of Epro-SGD, namely Epro-ORDA, based on the optimal regularized dual
averaging method. We apply the proposed methods on real-world applications; the
empirical results demonstrate the effectiveness of our methods.
Structured learning of metric ensembles with application to person re-identification
Authors: Sakrapee Paisitkriangkrai, Lin Wu, Chunhua Shen, Anton Van Den Hengel
Date: 2015-11-27 00:00:00
Tasks: Metric Learning, Person Re-Identification

Matching individuals across non-overlapping camera networks, known as person
re-identification, is a fundamentally challenging problem due to the large
visual appearance changes caused by variations of viewpoints, lighting, and
occlusion. Approaches in literature can be categoried into two streams: The
first stream is to develop reliable features against realistic conditions by
combining several visual features in a pre-defined way; the second stream is to
learn a metric from training data to ensure strong inter-class differences and
intra-class similarities. However, seeking an optimal combination of visual
features which is generic yet adaptive to different benchmarks is a unsoved
problem, and metric learning models easily get over-fitted due to the scarcity
of training data in person re-identification. In this paper, we propose two
effective structured learning based approaches which explore the adaptive
effects of visual features in recognizing persons in different benchmark data
sets. Our framework is built on the basis of multiple low-level visual features
with an optimal ensemble of their metrics. We formulate two optimization
algorithms, CMCtriplet and CMCstruct, which directly optimize evaluation
measures commonly used in person re-identification, also known as the
Cumulative Matching Characteristic (CMC) curve.
Cost-aware Pre-training for Multiclass Cost-sensitive Deep Learning
Authors: Yu-An Chung, Hsuan-Tien Lin, Shao-Wen Yang
Date: 2015-11-30 00:00:00
Tasks: Deep Learning, General Classification

Deep learning has been one of the most prominent machine learning techniques
nowadays, being the state-of-the-art on a broad range of applications where
automatic feature extraction is needed. Many such applications also demand
varying costs for different types of mis-classification errors, but it is not
clear whether or how such cost information can be incorporated into deep
learning to improve performance. In this work, we propose a novel cost-aware
algorithm that takes into account the cost information into not only the
training stage but also the pre-training stage of deep learning. The approach
allows deep learning to conduct automatic feature extraction with the cost
information effectively. Extensive experimental results demonstrate that the
proposed approach outperforms other deep learning models that do not digest the
cost information in the pre-training stage.
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Authors: Nicolas Papernot, Patrick McDaniel, Ian Goodfellow
Date: 2016-05-24 00:00:00
Tasks: BIG-bench Machine Learning

Many machine learning models are vulnerable to adversarial examples: inputs
that are specially crafted to cause a machine learning model to produce an
incorrect output. Adversarial examples that affect one model often affect
another model, even if the two models have different architectures or were
trained on different training sets, so long as both models were trained to
perform the same task. An attacker may therefore train their own substitute
model, craft adversarial examples against the substitute, and transfer them to
a victim model, with very little information about the victim. Recent work has
further developed a technique that uses the victim model as an oracle to label
a synthetic training set for the substitute, so the attacker need not even
collect a training set to mount the attack. We extend these recent techniques
using reservoir sampling to greatly enhance the efficiency of the training
procedure for the substitute model. We introduce new transferability attacks
between previously unexplored (substitute, victim) pairs of machine learning
model classes, most notably SVMs and decision trees. We demonstrate our attacks
on two commercial machine learning classification systems from Amazon (96.19%
misclassification rate) and Google (88.94%) using only 800 queries of the
victim model, thereby showing that existing machine learning approaches are in
general vulnerable to systematic black-box attacks regardless of their
structure.