pwc-archive/papers-with-abstracts huggingface dataset:
Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problems
Authors: Hoi-To Wai, Jean Lafond, Anna Scaglione, Eric Moulines
Date: 2016-12-05 00:00:00
Tasks: Matrix Completion, Sparse Learning

Decentralized optimization algorithms have received much attention due to the
recent advances in network information processing. However, conventional
decentralized algorithms based on projected gradient descent are incapable of
handling high dimensional constrained problems, as the projection step becomes
computationally prohibitive to compute. To address this problem, this paper
adopts a projection-free optimization approach, a.k.a.~the Frank-Wolfe (FW) or
conditional gradient algorithm. We first develop a decentralized FW (DeFW)
algorithm from the classical FW algorithm. The convergence of the proposed
algorithm is studied by viewing the decentralized algorithm as an inexact FW
algorithm. Using a diminishing step size rule and letting $t$ be the iteration
number, we show that the DeFW algorithm's convergence rate is ${\cal O}(1/t)$
for convex objectives; is ${\cal O}(1/t^2)$ for strongly convex objectives with
the optimal solution in the interior of the constraint set; and is ${\cal
O}(1/\sqrt{t})$ towards a stationary point for smooth but non-convex
objectives. We then show that a consensus-based DeFW algorithm meets the above
guarantees with two communication rounds per iteration. Furthermore, we
demonstrate the advantages of the proposed DeFW algorithm on low-complexity
robust matrix completion and communication efficient sparse learning. Numerical
results on synthetic and real data are presented to support our findings.
Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation
Authors: Sheng Zou, Hao Sun, Alina Zare
Date: 2017-03-17 00:00:00
Tasks: Hyperspectral Unmixing

A semi-supervised Partial Membership Latent Dirichlet Allocation approach is
developed for hyperspectral unmixing and endmember estimation while accounting
for spectral variability and spatial information. Partial Membership Latent
Dirichlet Allocation is an effective approach for spectral unmixing while
representing spectral variability and leveraging spatial information. In this
work, we extend Partial Membership Latent Dirichlet Allocation to incorporate
any available (imprecise) label information to help guide unmixing.
Experimental results on two hyperspectral datasets show that the proposed
semi-supervised PM-LDA can yield improved hyperspectral unmixing and endmember
estimation results.
Learning Recurrent Span Representations for Extractive Question Answering
Authors: Kenton Lee, Shimi Salant, Tom Kwiatkowski, Ankur Parikh, Dipanjan Das, Jonathan Berant
Date: 2016-11-04 00:00:00
Tasks: Answer Selection, Extractive Question-Answering, Natural Language Understanding, Question Answering, Reading Comprehension

The reading comprehension task, that asks questions about a given evidence
document, is a central problem in natural language understanding. Recent
formulations of this task have typically focused on answer selection from a set
of candidates pre-defined manually or through the use of an external NLP
pipeline. However, Rajpurkar et al. (2016) recently released the SQuAD dataset
in which the answers can be arbitrary strings from the supplied text. In this
paper, we focus on this answer extraction task, presenting a novel model
architecture that efficiently builds fixed length representations of all spans
in the evidence document with a recurrent network. We show that scoring
explicit span representations significantly improves performance over other
approaches that factor the prediction into separate predictions about words or
start and end markers. Our approach improves upon the best published results of
Wang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.'s
baseline by > 50%.
Global Entity Ranking Across Multiple Languages
Authors: Prantik Bhattacharyya, Nemanja Spasojevic
Date: 2017-03-17 00:00:00

We present work on building a global long-tailed ranking of entities across
multiple languages using Wikipedia and Freebase knowledge bases. We identify
multiple features and build a model to rank entities using a ground-truth
dataset of more than 10 thousand labels. The final system ranks 27 million
entities with 75% precision and 48% F1 score. We provide performance evaluation
and empirical evidence of the quality of ranking across languages, and open the
final ranked lists for future research.
Nonconvex One-bit Single-label Multi-label Learning
Authors: Shuang Qiu, Tingjin Luo, Jieping Ye, Ming Lin
Date: 2017-03-17 00:00:00
Tasks: Multi-Label Learning

We study an extreme scenario in multi-label learning where each training
instance is endowed with a single one-bit label out of multiple labels. We
formulate this problem as a non-trivial special case of one-bit rank-one matrix
sensing and develop an efficient non-convex algorithm based on alternating
power iteration. The proposed algorithm is able to recover the underlying
low-rank matrix model with linear convergence. For a rank-$k$ model with $d_1$
features and $d_2$ classes, the proposed algorithm achieves $O(\epsilon)$
recovery error after retrieving $O(k^{1.5}d_1 d_2/\epsilon)$ one-bit labels
within $O(kd)$ memory. Our bound is nearly optimal in the order of
$O(1/\epsilon)$. This significantly improves the state-of-the-art sampling
complexity of one-bit multi-label learning. We perform experiments to verify
our theory and evaluate the performance of the proposed algorithm.
Correlation Clustering with Low-Rank Matrices
Authors: Nate Veldt, Anthony Wirth, David F. Gleich
Date: 2016-11-21 00:00:00
Tasks: Clustering

Correlation clustering is a technique for aggregating data based on
qualitative information about which pairs of objects are labeled 'similar' or
'dissimilar.' Because the optimization problem is NP-hard, much of the previous
literature focuses on finding approximation algorithms. In this paper we
explore how to solve the correlation clustering objective exactly when the data
to be clustered can be represented by a low-rank matrix. We prove in particular
that correlation clustering can be solved in polynomial time when the
underlying matrix is positive semidefinite with small constant rank, but that
the task remains NP-hard in the presence of even one negative eigenvalue. Based
on our theoretical results, we develop an algorithm for efficiently "solving"
low-rank positive semidefinite correlation clustering by employing a procedure
for zonotope vertex enumeration. We demonstrate the effectiveness and speed of
our algorithm by using it to solve several clustering problems on both
synthetic and real-world data.
PSF field learning based on Optimal Transport Distances
Authors: F. M. Ngolè Mboula, J. -L. Starck
Date: 2017-03-17 00:00:00
Tasks: Astronomy, Dimensionality Reduction

Context: in astronomy, observing large fractions of the sky within a
reasonable amount of time implies using large field-of-view (fov) optical
instruments that typically have a spatially varying Point Spread Function
(PSF). Depending on the scientific goals, galaxies images need to be corrected
for the PSF whereas no direct measurement of the PSF is available. Aims: given
a set of PSFs observed at random locations, we want to estimate the PSFs at
galaxies locations for shapes measurements correction. Contributions: we
propose an interpolation framework based on Sliced Optimal Transport. A
non-linear dimension reduction is first performed based on local pairwise
approximated Wasserstein distances. A low dimensional representation of the
unknown PSFs is then estimated, which in turn is used to derive representations
of those PSFs in the Wasserstein metric. Finally, the interpolated PSFs are
calculated as approximated Wasserstein barycenters. Results: the proposed
method was tested on simulated monochromatic PSFs of the Euclid space mission
telescope (to be launched in 2020). It achieves a remarkable accuracy in terms
of pixels values and shape compared to standard methods such as Inverse
Distance Weighting or Radial Basis Function based interpolation methods.
Block CUR: Decomposing Matrices using Groups of Columns
Authors: Urvashi Oswal, Swayambhoo Jain, Kevin S. Xu, Brian Eriksson
Date: 2017-03-17 00:00:00
Tasks: Distributed Computing

A common problem in large-scale data analysis is to approximate a matrix
using a combination of specifically sampled rows and columns, known as CUR
decomposition. Unfortunately, in many real-world environments, the ability to
sample specific individual rows or columns of the matrix is limited by either
system constraints or cost. In this paper, we consider matrix approximation by
sampling predefined \emph{blocks} of columns (or rows) from the matrix. We
present an algorithm for sampling useful column blocks and provide novel
guarantees for the quality of the approximation. This algorithm has application
in problems as diverse as biometric data analysis to distributed computing. We
demonstrate the effectiveness of the proposed algorithms for computing the
Block CUR decomposition of large matrices in a distributed setting with
multiple nodes in a compute cluster, where such blocks correspond to columns
(or rows) of the matrix stored on the same node, which can be retrieved with
much less overhead than retrieving individual columns stored across different
nodes. In the biometric setting, the rows correspond to different users and
columns correspond to users' biometric reaction to external stimuli, {\em
e.g.,}~watching video content, at a particular time instant. There is
significant cost in acquiring each user's reaction to lengthy content so we
sample a few important scenes to approximate the biometric response. An
individual time sample in this use case cannot be queried in isolation due to
the lack of context that caused that biometric reaction. Instead, collections
of time segments ({\em i.e.,} blocks) must be presented to the user. The
practical application of these algorithms is shown via experimental results
using real-world user biometric data from a content testing environment.
ALLSAT compressed with wildcards: From CNF's to orthogonal DNF's by imposing the clauses one by one
Authors: Marcel Wild
Date: 2016-08-30 00:00:00

We present a novel technique for converting a Boolean CNF into an orthogonal DNF, aka exclusive sum of products. Our method (which will be pitted against a hardwired command from Mathematica) zooms in on the models of the CNF by imposing its clauses one by one. Clausal Imposition invites parallelization, and wildcards beyond the common don't-care symbol compress the output. The method is most efficient for few but large clauses. Generalizing clauses one can in fact impose superclauses. By definition, superclauses are obtained from clauses by substituting each positive litereal by an arbitrary conjunction of positive literals.
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing
Authors: Jie Xu, Lixing Chen, Shaolei Ren
Date: 2017-03-17 00:00:00
Tasks: Edge-computing, Management, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Mobile edge computing (a.k.a. fog computing) has recently emerged to enable
in-situ processing of delay-sensitive applications at the edge of mobile
networks. Providing grid power supply in support of mobile edge computing,
however, is costly and even infeasible (in certain rugged or under-developed
areas), thus mandating on-site renewable energy as a major or even sole power
supply in increasingly many scenarios. Nonetheless, the high intermittency and
unpredictability of renewable energy make it very challenging to deliver a high
quality of service to users in energy harvesting mobile edge computing systems.
In this paper, we address the challenge of incorporating renewables into mobile
edge computing and propose an efficient reinforcement learning-based resource
management algorithm, which learns on-the-fly the optimal policy of dynamic
workload offloading (to the centralized cloud) and edge server provisioning to
minimize the long-term system cost (including both service delay and
operational cost). Our online learning algorithm uses a decomposition of the
(offline) value iteration and (online) reinforcement learning, thus achieving a
significant improvement of learning rate and run-time performance when compared
to standard reinforcement learning algorithms such as Q-learning. We prove the
convergence of the proposed algorithm and analytically show that the learned
policy has a simple monotone structure amenable to practical implementation.
Our simulation results validate the efficacy of our algorithm, which
significantly improves the edge computing performance compared to fixed or
myopic optimization schemes and conventional reinforcement learning algorithms.
Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction
Authors: Seyed Mohammad Taheri, Hamidreza Mahyar, Mohammad Firouzi, Elahe Ghalebi K., Radu Grosu, Ali Movaghar
Date: 2016-12-05 00:00:00
Tasks: Recommendation Systems, Relation, Relation Extraction

Recommendation plays an increasingly important role in our daily lives.
Recommender systems automatically suggest items to users that might be
interesting for them. Recent studies illustrate that incorporating social trust
in Matrix Factorization methods demonstrably improves accuracy of rating
prediction. Such approaches mainly use the trust scores explicitly expressed by
users. However, it is often challenging to have users provide explicit trust
scores of each other. There exist quite a few works, which propose Trust
Metrics to compute and predict trust scores between users based on their
interactions. In this paper, first we present how social relation can be
extracted from users' ratings to items by describing Hellinger distance between
users in recommender systems. Then, we propose to incorporate the predicted
trust scores into social matrix factorization models. By analyzing social
relation extraction from three well-known real-world datasets, which both:
trust and recommendation data available, we conclude that using the implicit
social relation in social recommendation techniques has almost the same
performance compared to the actual trust scores explicitly expressed by users.
Hence, we build our method, called Hell-TrustSVD, on top of the
state-of-the-art social recommendation technique to incorporate both the
extracted implicit social relations and ratings given by users on the
prediction of items for an active user. To the best of our knowledge, this is
the first work to extend TrustSVD with extracted social trust information. The
experimental results support the idea of employing implicit trust into matrix
factorization whenever explicit trust is not available, can perform much better
than the state-of-the-art approaches in user rating prediction.
Credal Networks under Epistemic Irrelevance
Authors: Jasper De Bock
Date: 2017-01-27 00:00:00

A credal network under epistemic irrelevance is a generalised type of
Bayesian network that relaxes its two main building blocks. On the one hand,
the local probabilities are allowed to be partially specified. On the other
hand, the assessments of independence do not have to hold exactly.
Conceptually, these two features turn credal networks under epistemic
irrelevance into a powerful alternative to Bayesian networks, offering a more
flexible approach to graph-based multivariate uncertainty modelling. However,
in practice, they have long been perceived as very hard to work with, both
theoretically and computationally.
  The aim of this paper is to demonstrate that this perception is no longer
justified. We provide a general introduction to credal networks under epistemic
irrelevance, give an overview of the state of the art, and present several new
theoretical results. Most importantly, we explain how these results can be
combined to allow for the design of recursive inference methods. We provide
numerous concrete examples of how this can be achieved, and use these to
demonstrate that computing with credal networks under epistemic irrelevance is
most definitely feasible, and in some cases even highly efficient. We also
discuss several philosophical aspects, including the lack of symmetry, how to
deal with probability zero, the interpretation of lower expectations, the
axiomatic status of graphoid properties, and the difference between updating
and conditioning.
Auxiliary Multimodal LSTM for Audio-visual Speech Recognition and Lipreading
Authors: Chunlin Tian, Weijun Ji
Date: 2017-01-16 00:00:00
Tasks: Audio-Visual Speech Recognition, Automatic Speech Recognition, Automatic Speech Recognition (ASR), General Classification, image-classification, Image Classification, Lipreading, speech-recognition, Speech Recognition, Visual Speech Recognition

The Aduio-visual Speech Recognition (AVSR) which employs both the video and
audio information to do Automatic Speech Recognition (ASR) is one of the
application of multimodal leaning making ASR system more robust and accuracy.
The traditional models usually treated AVSR as inference or projection but
strict prior limits its ability. As the revival of deep learning, Deep Neural
Networks (DNN) becomes an important toolkit in many traditional classification
tasks including ASR, image classification, natural language processing. Some
DNN models were used in AVSR like Multimodal Deep Autoencoders (MDAEs),
Multimodal Deep Belief Network (MDBN) and Multimodal Deep Boltzmann Machine
(MDBM) that actually work better than traditional methods. However, such DNN
models have several shortcomings: (1) They don't balance the modal fusion and
temporal fusion, or even haven't temporal fusion; (2)The architecture of these
models isn't end-to-end, the training and testing getting cumbersome. We
propose a DNN model, Auxiliary Multimodal LSTM (am-LSTM), to overcome such
weakness. The am-LSTM could be trained and tested once, moreover easy to train
and preventing overfitting automatically. The extensibility and flexibility are
also take into consideration. The experiments show that am-LSTM is much better
than traditional methods and other DNN models in three datasets.
Smartphone Based Colorimetric Detection via Machine Learning
Authors: Ali Y. Mutlu, Volkan Kılıç, Gizem K. Özdemir, Abdullah Bayram, Nesrin Horzum, Mehmet E. Solmaz
Date: 2017-03-17 00:00:00
Tasks: BIG-bench Machine Learning, Specificity

We report the application of machine learning to smartphone based
colorimetric detection of pH values. The strip images were used as the training
set for Least Squares-Support Vector Machine (LS-SVM) classifier algorithms
that were able to successfully classify the distinct pH values. The difference
in the obtained image formats was found not to significantly affect the
performance of the proposed machine learning approach. Moreover, the influence
of the illumination conditions on the perceived color of pH strips was
investigated and further experiments were carried out to study effect of color
change on the learning model. Test results on JPEG, RAW and RAW-corrected image
formats captured in different lighting conditions lead to perfect
classification accuracy, sensitivity and specificity, which proves that the
colorimetric detection using machine learning based systems is able to adapt to
various experimental conditions and is a great candidate for smartphone based
sensing in paper-based colorimetric assays.
Color Orchestra: Ordering Color Palettes for Interpolation and Prediction
Authors: Huy Q. Phan, Hongbo Fu, Antoni B. Chan
Date: 2017-03-17 00:00:00
Tasks: Density Estimation, Prediction

Color theme or color palette can deeply influence the quality and the feeling
of a photograph or a graphical design. Although color palettes may come from
different sources such as online crowd-sourcing, photographs and graphical
designs, in this paper, we consider color palettes extracted from fine art
collections, which we believe to be an abundant source of stylistic and unique
color themes. We aim to capture color styles embedded in these collections by
means of statistical models and to build practical applications upon these
models. As artists often use their personal color themes in their paintings,
making these palettes appear frequently in the dataset, we employed density
estimation to capture the characteristics of palette data. Via density
estimation, we carried out various predictions and interpolations on palettes,
which led to promising applications such as photo-style exploration, real-time
color suggestion, and enriched photo recolorization. It was, however,
challenging to apply density estimation to palette data as palettes often come
as unordered sets of colors, which make it difficult to use conventional
metrics on them. To this end, we developed a divide-and-conquer sorting
algorithm to rearrange the colors in the palettes in a coherent order, which
allows meaningful interpolation between color palettes. To confirm the
performance of our model, we also conducted quantitative experiments on
datasets of digitized paintings collected from the Internet and received
favorable results.
Implicit Gradient Neural Networks with a Positive-Definite Mass Matrix for Online Linear Equations Solving
Authors: Ke Chen
Date: 2017-03-17 00:00:00

Motivated by the advantages achieved by implicit analogue net for solving
online linear equations, a novel implicit neural model is designed based on
conventional explicit gradient neural networks in this letter by introducing a
positive-definite mass matrix. In addition to taking the advantages of the
implicit neural dynamics, the proposed implicit gradient neural networks can
still achieve globally exponential convergence to the unique theoretical
solution of linear equations and also global stability even under no-solution
and multi-solution situations. Simulative results verify theoretical
convergence analysis on the proposed neural dynamics.
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Authors: Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, Georg Langs
Date: 2017-03-17 00:00:00
Tasks: Anomaly Detection, Generative Adversarial Network, Unsupervised Anomaly Detection

Obtaining models that capture imaging markers relevant for disease
progression and treatment monitoring is challenging. Models are typically based
on large amounts of data with annotated examples of known markers aiming at
automating detection. High annotation effort and the limitation to a vocabulary
of known markers limit the power of such approaches. Here, we perform
unsupervised learning to identify anomalies in imaging data as candidates for
markers. We propose AnoGAN, a deep convolutional generative adversarial network
to learn a manifold of normal anatomical variability, accompanying a novel
anomaly scoring scheme based on the mapping from image space to a latent space.
Applied to new data, the model labels anomalies, and scores image patches
indicating their fit into the learned distribution. Results on optical
coherence tomography images of the retina demonstrate that the approach
correctly identifies anomalous images, such as images containing retinal fluid
or hyperreflective foci.