pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization
Authors: Zheng Wen, Benjamin Van Roy
Date: 2013-07-18 00:00:00
Tasks: Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the problem of reinforcement learning over episodes of a
finite-horizon deterministic system and as a solution propose optimistic
constraint propagation (OCP), an algorithm designed to synthesize efficient
exploration and value function generalization. We establish that when the true
value function lies within a given hypothesis class, OCP selects optimal
actions over all but at most K episodes, where K is the eluder dimension of the
given hypothesis class. We establish further efficiency and asymptotic
performance guarantees that apply even if the true value function does not lie
in the given hypothesis class, for the special case where the hypothesis class
is the span of pre-specified indicator functions over disjoint sets. We also
discuss the computational complexity of OCP and present computational results
involving two illustrative examples.
Object Boundary Guided Semantic Segmentation
Authors: Qin Huang, Chunyang Xia, Wenchao Zheng, Yuhang Song, Hao Xu, C. -C. Jay Kuo
Date: 2016-03-31 00:00:00
Tasks: Object, Object Localization, Segmentation, Semantic Segmentation

Semantic segmentation is critical to image content understanding and object
localization. Recent development in fully-convolutional neural network (FCN)
has enabled accurate pixel-level labeling. One issue in previous works is that
the FCN based method does not exploit the object boundary information to
delineate segmentation details since the object boundary label is ignored in
the network training. To tackle this problem, we introduce a double branch
fully convolutional neural network, which separates the learning of the
desirable semantic class labeling with mask-level object proposals guided by
relabeled boundaries. This network, called object boundary guided FCN
(OBG-FCN), is able to integrate the distinct properties of object shape and
class features elegantly in a fully convolutional way with a designed masking
architecture. We conduct experiments on the PASCAL VOC segmentation benchmark,
and show that the end-to-end trainable OBG-FCN system offers great improvement
in optimizing the target semantic segmentation quality.
Computer Aided Restoration of Handwritten Character Strokes
Authors: Barak Sober, David Levin
Date: 2016-02-23 00:00:00

This work suggests a new variational approach to the task of computer aided
restoration of incomplete characters, residing in a highly noisy document. We
model character strokes as the movement of a pen with a varying radius.
Following this model, a cubic spline representation is being utilized to
perform gradient descent steps, while maintaining interpolation at some initial
(manually sampled) points. The proposed algorithm was utilized in the process
of restoring approximately 1000 ancient Hebrew characters (dating to ca.
8th-7th century BCE), some of which are presented herein and show that the
algorithm yields plausible results when applied on deteriorated documents.
Fundamental Parameters of Main-Sequence Stars in an Instant with Machine Learning
Authors: Earl P. Bellinger, George C. Angelou, Saskia Hekker, Sarbani Basu, Warrick Ball, Elisabeth Guggenberger
Date: 2016-07-06 00:00:00
Tasks: BIG-bench Machine Learning

Owing to the remarkable photometric precision of space observatories like
Kepler, stellar and planetary systems beyond our own are now being
characterized en masse for the first time. These characterizations are pivotal
for endeavors such as searching for Earth-like planets and solar twins,
understanding the mechanisms that govern stellar evolution, and tracing the
dynamics of our Galaxy. The volume of data that is becoming available, however,
brings with it the need to process this information accurately and rapidly.
While existing methods can constrain fundamental stellar parameters such as
ages, masses, and radii from these observations, they require substantial
computational efforts to do so.
  We develop a method based on machine learning for rapidly estimating
fundamental parameters of main-sequence solar-like stars from classical and
asteroseismic observations. We first demonstrate this method on a
hare-and-hound exercise and then apply it to the Sun, 16 Cyg A & B, and 34
planet-hosting candidates that have been observed by the Kepler spacecraft. We
find that our estimates and their associated uncertainties are comparable to
the results of other methods, but with the additional benefit of being able to
explore many more stellar parameters while using much less computation time. We
furthermore use this method to present evidence for an empirical diffusion-mass
relation. Our method is open source and freely available for the community to
use.
  The source code for all analyses and for all figures appearing in this
manuscript can be found electronically at
https://github.com/earlbellinger/asteroseismology
VideoLSTM Convolves, Attends and Flows for Action Recognition
Authors: Zhenyang Li, Efstratios Gavves, Mihir Jain, Cees G. M. Snoek
Date: 2016-07-06 00:00:00
Tasks: Action Classification, Action Localization, Action Recognition, General Classification, Temporal Action Localization

We present a new architecture for end-to-end sequence learning of actions in
video, we call VideoLSTM. Rather than adapting the video to the peculiarities
of established recurrent or convolutional architectures, we adapt the
architecture to fit the requirements of the video medium. Starting from the
soft-Attention LSTM, VideoLSTM makes three novel contributions. First, video
has a spatial layout. To exploit the spatial correlation we hardwire
convolutions in the soft-Attention LSTM architecture. Second, motion not only
informs us about the action content, but also guides better the attention
towards the relevant spatio-temporal locations. We introduce motion-based
attention. And finally, we demonstrate how the attention from VideoLSTM can be
used for action localization by relying on just the action class label.
Experiments and comparisons on challenging datasets for action classification
and localization support our claims.
Measuring dependence powerfully and equitably
Authors: Yakir A. Reshef, David N. Reshef, Hilary K. Finucane, Pardis C. Sabeti, Michael M. Mitzenmacher
Date: 2015-05-09 00:00:00

Given a high-dimensional data set we often wish to find the strongest relationships within it. A common strategy is to evaluate a measure of dependence on every variable pair and retain the highest-scoring pairs for follow-up. This strategy works well if the statistic used is equitable [Reshef et al. 2015a], i.e., if, for some measure of noise, it assigns similar scores to equally noisy relationships regardless of relationship type (e.g., linear, exponential, periodic). In this paper, we introduce and characterize a population measure of dependence called MIC*. We show three ways that MIC* can be viewed: as the population value of MIC, a highly equitable statistic from [Reshef et al. 2011], as a canonical "smoothing" of mutual information, and as the supremum of an infinite sequence defined in terms of optimal one-dimensional partitions of the marginals of the joint distribution. Based on this theory, we introduce an efficient approach for computing MIC* from the density of a pair of random variables, and we define a new consistent estimator MICe for MIC* that is efficiently computable. In contrast, there is no known polynomial-time algorithm for computing the original equitable statistic MIC. We show through simulations that MICe has better bias-variance properties than MIC. We then introduce and prove the consistency of a second statistic, TICe, that is a trivial side-product of the computation of MICe and whose goal is powerful independence testing rather than equitability. We show in simulations that MICe and TICe have good equitability and power against independence respectively. The analyses here complement a more in-depth empirical evaluation of several leading measures of dependence [Reshef et al. 2015b] that shows state-of-the-art performance for MICe and TICe.
Rolling Horizon Coevolutionary Planning for Two-Player Video Games
Authors: Jialin Liu, Diego Pérez-Liébana, Simon M. Lucas
Date: 2016-07-06 00:00:00
Tasks: Decision Making, Vocal Bursts Valence Prediction

This paper describes a new algorithm for decision making in two-player
real-time video games. As with Monte Carlo Tree Search, the algorithm can be
used without heuristics and has been developed for use in general video game
AI. The approach is to extend recent work on rolling horizon evolutionary
planning, which has been shown to work well for single-player games, to two (or
in principle many) player games. To select an action the algorithm co-evolves
two (or in the general case N) populations, one for each player, where each
individual is a sequence of actions for the respective player. The fitness of
each individual is evaluated by playing it against a selection of
action-sequences from the opposing population. When choosing an action to take
in the game, the first action is chosen from the fittest member of the
population for that player. The new algorithm is compared with a number of
general video game AI algorithms on three variations of a two-player space
battle game, with promising results.
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Authors: Baochen Sun, Kate Saenko
Date: 2016-07-06 00:00:00
Tasks: Domain Adaptation, Domain Generalization, Image Classification, Unsupervised Domain Adaptation

Deep neural networks are able to learn powerful representations from large
quantities of labeled input data, however they cannot always generalize well
across changes in input distributions. Domain adaptation algorithms have been
proposed to compensate for the degradation in performance due to domain shift.
In this paper, we address the case when the target domain is unlabeled,
requiring unsupervised adaptation. CORAL is a "frustratingly easy" unsupervised
domain adaptation method that aligns the second-order statistics of the source
and target distributions with a linear transformation. Here, we extend CORAL to
learn a nonlinear transformation that aligns correlations of layer activations
in deep neural networks (Deep CORAL). Experiments on standard benchmark
datasets show state-of-the-art performance.
Lattice Structure of Variable Precision Rough Sets
Authors: Sumita Basu
Date: 2016-07-06 00:00:00
Tasks: General Classification

The main purpose of this paper is to study the lattice structure of variable
precision rough sets. The notion of variation in precision of rough sets have
been further extended to variable precision rough set with variable
classification error and its algebraic properties are also studied.
A Modified Activation Function with Improved Run-Times For Neural Networks
Authors: Vincent Ike Anireh, Emmanuel Ndidi Osegi
Date: 2016-07-06 00:00:00

In this paper we present a modified version of the Hyperbolic Tangent
Activation Function as a learning unit generator for neural networks. The
function uses an integer calibration constant as an approximation to the Euler
number, e, based on a quadratic Real Number Formula (RNF) algorithm and an
adaptive normalization constraint on the input activations to avoid the
vanishing gradient. We demonstrate the effectiveness of the proposed
modification using a hypothetical and real world dataset and show that lower
run-times can be achieved by learning algorithms using this function leading to
improved speed-ups and learning accuracies during training.
A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Coping with Gene Ontology-based Features
Authors: Cen Wan, Alex A. Freitas
Date: 2016-07-06 00:00:00

The Tree Augmented Naive Bayes classifier is a type of probabilistic
graphical model that can represent some feature dependencies. In this work, we
propose a Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes
(HRE-TAN) algorithm, which considers removing the hierarchical redundancy
during the classifier learning process, when coping with data containing
hierarchically structured features. The experiments showed that HRE-TAN obtains
significantly better predictive performance than the conventional Tree
Augmented Naive Bayes classifier, and enhanced the robustness against
imbalanced class distributions, in aging-related gene datasets with Gene
Ontology terms used as features.
An Application of Network Lasso Optimization For Ride Sharing Prediction
Authors: Shaona Ghosh, Kevin Page, David De Roure
Date: 2016-06-10 00:00:00
Tasks: Clustering, Prediction

Ride sharing has important implications in terms of environmental, social and
individual goals by reducing carbon footprints, fostering social interactions
and economizing commuter costs. The ride sharing systems that are commonly
available lack adaptive and scalable techniques that can simultaneously learn
from the large scale data and predict in real-time dynamic fashion. In this
paper, we study such a problem towards a smart city initiative, where a generic
ride sharing system is conceived capable of making predictions about ride share
opportunities based on the historically recorded data while satisfying
real-time ride requests. Underpinning the system is an application of a
powerful machine learning convex optimization framework called Network Lasso
that uses the Alternate Direction Method of Multipliers (ADMM) optimization for
learning and dynamic prediction. We propose an application of a robust and
scalable unified optimization framework within the ride sharing case-study. The
application of Network Lasso framework is capable of jointly optimizing and
clustering different rides based on their spatial and model similarity. The
prediction from the framework clusters new ride requests, making accurate price
prediction based on the clusters, detecting hidden correlations in the data and
allowing fast convergence due to the network topology. We provide an empirical
evaluation of the application of ADMM network Lasso on real trip record and
simulated data, proving their effectiveness since the mean squared error of the
algorithm's prediction is minimized on the test rides.
Guided Alignment Training for Topic-Aware Neural Machine Translation
Authors: Wenhu Chen, Evgeny Matusov, Shahram Khadivi, Jan-Thorsten Peter
Date: 2016-07-06 00:00:00
Proceeding: AMTA 2016 10
Tasks: Decoder, Domain Adaptation, Machine Translation, NMT, Translation, Word Alignment

In this paper, we propose an effective way for biasing the attention
mechanism of a sequence-to-sequence neural machine translation (NMT) model
towards the well-studied statistical word alignment models. We show that our
novel guided alignment training approach improves translation quality on
real-life e-commerce texts consisting of product titles and descriptions,
overcoming the problems posed by many unknown words and a large type/token
ratio. We also show that meta-data associated with input texts such as topic or
category information can significantly improve translation quality when used as
an additional signal to the decoder part of the network. With both novel
features, the BLEU score of the NMT system on a product title set improves from
18.6 to 21.3%. Even larger MT quality gains are obtained through domain
adaptation of a general domain NMT system to e-commerce data. The developed NMT
system also performs well on the IWSLT speech translation task, where an
ensemble of four variant systems outperforms the phrase-based baseline by 2.1%
BLEU absolute.
Bayesian Nonparametrics for Sparse Dynamic Networks
Authors: Cian Naik, Francois Caron, Judith Rousseau, Yee Whye Teh, Konstantina Palla
Date: 2016-07-06 00:00:00
Tasks: Articles

In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks. A positive parameter is associated to each node of a network, which models the sociability of that node. Sociabilities are assumed to evolve over time, and are modelled via a dynamic point process model. The model is able to capture long term evolution of the sociabilities. Moreover, it yields sparse graphs, where the number of edges grows subquadratically with the number of nodes. The evolution of the sociabilities is described by a tractable time-varying generalised gamma process. We provide some theoretical insights into the model and apply it to three datasets: a simulated network, a network of hyperlinks between communities on Reddit, and a network of co-occurences of words in Reuters news articles after the September 11th attacks.
Artificial neural networks and fuzzy logic for recognizing alphabet characters and mathematical symbols
Authors: Giuseppe Airò Farulla, Tiziana Armano, Anna Capietto, Nadir Murru, Rosaria Rossini
Date: 2016-07-06 00:00:00
Tasks: Image Segmentation, Optical Character Recognition, Optical Character Recognition (OCR), Semantic Segmentation

Optical Character Recognition software (OCR) are important tools for
obtaining accessible texts. We propose the use of artificial neural networks
(ANN) in order to develop pattern recognition algorithms capable of recognizing
both normal texts and formulae. We present an original improvement of the
backpropagation algorithm. Moreover, we describe a novel image segmentation
algorithm that exploits fuzzy logic for separating touching characters.
Sacrificing information for the greater good: how to select photometric bands for optimal accuracy
Authors: Kristoffer Stensbo-Smidt, Fabian Gieseke, Christian Igel, Andrew Zirm, Kim Steenstrup Pedersen
Date: 2015-11-17 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, Model Selection

Large-scale surveys make huge amounts of photometric data available. Because
of the sheer amount of objects, spectral data cannot be obtained for all of
them. Therefore it is important to devise techniques for reliably estimating
physical properties of objects from photometric information alone. These
estimates are needed to automatically identify interesting objects worth a
follow-up investigation as well as to produce the required data for a
statistical analysis of the space covered by a survey. We argue that machine
learning techniques are suitable to compute these estimates accurately and
efficiently. This study promotes a feature selection algorithm, which selects
the most informative magnitudes and colours for a given task of estimating
physical quantities from photometric data alone. Using k nearest neighbours
regression, a well-known non-parametric machine learning method, we show that
using the found features significantly increases the accuracy of the
estimations compared to using standard features and standard methods. We
illustrate the usefulness of the approach by estimating specific star formation
rates (sSFRs) and redshifts (photo-z's) using only the broad-band photometry
from the Sloan Digital Sky Survey (SDSS). For estimating sSFRs, we demonstrate
that our method produces better estimates than traditional spectral energy
distribution (SED) fitting. For estimating photo-z's, we show that our method
produces more accurate photo-z's than the method employed by SDSS. The study
highlights the general importance of performing proper model selection to
improve the results of machine learning systems and how feature selection can
provide insights into the predictive relevance of particular input features.
Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data
Authors: Gerasimos Spanakis, Gerhard Weiss, Anne Roefs
Date: 2016-07-06 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Ecological Momentary Assessment (EMA) data is organized in multiple levels
(per-subject, per-day, etc.) and this particular structure should be taken into
account in machine learning algorithms used in EMA like decision trees and its
variants. We propose a new algorithm called BBT (standing for Bagged Boosted
Trees) that is enhanced by a over/under sampling method and can provide better
estimates for the conditional class probability function. Experimental results
on a real-world dataset show that BBT can benefit EMA data classification and
performance.
CUNet: A Compact Unsupervised Network for Image Classification
Authors: Le Dong, Ling He, Gaipeng Kong, Qianni Zhang, Xiaochun Cao, Ebroul Izquierdo
Date: 2016-07-06 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

In this paper, we propose a compact network called CUNet (compact
unsupervised network) to counter the image classification challenge. Different
from the traditional convolutional neural networks learning filters by the
time-consuming stochastic gradient descent, CUNet learns the filter bank from
diverse image patches with the simple K-means, which significantly avoids the
requirement of scarce labeled training images, reduces the training
consumption, and maintains the high discriminative ability. Besides, we propose
a new pooling method named weighted pooling considering the different weight
values of adjacent neurons, which helps to improve the robustness to small
image distortions. In the output layer, CUNet integrates the feature maps
gained in the last hidden layer, and straightforwardly computes histograms in
non-overlapped blocks. To reduce feature redundancy, we implement the
max-pooling operation on adjacent blocks to select the most competitive
features. Comprehensive experiments are conducted to demonstrate the
state-of-the-art classification performances with CUNet on CIFAR-10, STL-10,
MNIST and Caltech101 benchmark datasets.
On Sampling and Greedy MAP Inference of Constrained Determinantal Point Processes
Authors: Tarun Kathuria, Amit Deshpande
Date: 2016-07-06 00:00:00
Tasks: Clustering, Diversity, Point Processes

Subset selection problems ask for a small, diverse yet representative subset
of the given data. When pairwise similarities are captured by a kernel, the
determinants of submatrices provide a measure of diversity or independence of
items within a subset. Matroid theory gives another notion of independence,
thus giving rise to optimization and sampling questions about Determinantal
Point Processes (DPPs) under matroid constraints. Partition constraints, as a
special case, arise naturally when incorporating additional labeling or
clustering information, besides the kernel, in DPPs. Finding the maximum
determinant submatrix under matroid constraints on its row/column indices has
been previously studied. However, the corresponding question of sampling from
DPPs under matroid constraints has been unresolved, beyond the simple
cardinality constrained k-DPPs. We give the first polynomial time algorithm to
sample exactly from DPPs under partition constraints, for any constant number
of partitions. We complement this by a complexity theoretic barrier that rules
out such a result under general matroid constraints. Our experiments indicate
that partition-constrained DPPs offer more flexibility and more diversity than
k-DPPs and their naive extensions, while being reasonably efficient in running
time. We also show that a simple greedy initialization followed by local search
gives improved approximation guarantees for the problem of MAP inference from
k- DPPs on well-conditioned kernels. Our experiments show that this improvement
is significant for larger values of k, supporting our theoretical result.