pwc-archive/papers-with-abstracts huggingface dataset:
Machine learning for accelerating effective property prediction for poroelasticity problem in stochastic media
Authors: Maria Vasilyeva, Aleksey Tyrylgin
Date: 2018-10-03 00:00:00
Tasks: BIG-bench Machine Learning, Property Prediction

In this paper, we consider a numerical homogenization of the poroelasticity
problem with stochastic properties. The proposed method based on the
construction of the deep neural network (DNN) for fast calculation of the
effective properties for a coarse grid approximation of the problem. We train
neural networks on the set of the selected realizations of the local microscale
stochastic fields and macroscale characteristics (permeability and elasticity
tensors). We construct a deep learning method through convolutional neural
network (CNN) to learn a map between stochastic fields and effective
properties. Numerical results are presented for two and three-dimensional model
problems and show that proposed method provide fast and accurate effective
property predictions.
Deep Fundamental Matrix Estimation without Correspondences
Authors: Omid Poursaeed, Guandao Yang, Aditya Prakash, Qiuren Fang, Hanqing Jiang, Bharath Hariharan, Serge Belongie
Date: 2018-10-03 00:00:00

Estimating fundamental matrices is a classic problem in computer vision.
Traditional methods rely heavily on the correctness of estimated key-point
correspondences, which can be noisy and unreliable. As a result, it is
difficult for these methods to handle image pairs with large occlusion or
significantly different camera poses. In this paper, we propose novel neural
network architectures to estimate fundamental matrices in an end-to-end manner
without relying on point correspondences. New modules and layers are introduced
in order to preserve mathematical properties of the fundamental matrix as a
homogeneous rank-2 matrix with seven degrees of freedom. We analyze performance
of the proposed models using various metrics on the KITTI dataset, and show
that they achieve competitive performance with traditional methods without the
need for extracting correspondences.
A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation
Authors: Kaung Khin, Philipp Burckhardt, Rema Padman
Date: 2018-10-03 00:00:00
Tasks: Deep Learning, De-identification, Word Embeddings

De-identification is the process of removing 18 protected health information
(PHI) from clinical notes in order for the text to be considered not
individually identifiable. Recent advances in natural language processing (NLP)
has allowed for the use of deep learning techniques for the task of
de-identification. In this paper, we present a deep learning architecture that
builds on the latest NLP advances by incorporating deep contextualized word
embeddings and variational drop out Bi-LSTMs. We test this architecture on two
gold standard datasets and show that the architecture achieves state-of-the-art
performance on both data sets while also converging faster than other systems
without the use of dictionaries or other knowledge sources.
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
Authors: Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B. Tenenbaum, Antonio Torralba
Date: 2018-10-03 00:00:00
Proceeding: ICLR 2019 5
Tasks: Inductive Bias

Real-life control tasks involve matters of various substances---rigid or soft
bodies, liquid, gas---each with distinct physical behaviors. This poses
challenges to traditional rigid-body physics engines. Particle-based simulators
have been developed to model the dynamics of these complex scenes; however,
relying on approximation techniques, their simulation often deviates from
real-world physics, especially in the long term. In this paper, we propose to
learn a particle-based simulator for complex control tasks. Combining learning
with particle-based systems brings in two major benefits: first, the learned
simulator, just like other particle-based systems, acts widely on objects of
different materials; second, the particle-based representation poses strong
inductive bias for learning: particles of the same type have the same dynamics
within. This enables the model to quickly adapt to new environments of unknown
dynamics within a few observations. We demonstrate robots achieving complex
manipulation tasks using the learned simulator, such as manipulating fluids and
deformable foam, with experiments both in simulation and in the real world. Our
study helps lay the foundation for robot learning of dynamic scenes with
particle-based representations.
Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region
Authors: Faustine John, Irwandi Hipiny, Hamimah Ujir, Mohd Shahrizal Sunar
Date: 2018-10-03 00:00:00

It is recommended for a novice to engage a trained and experience person,
i.e., a coach before starting an unfamiliar aerobic or weight routine. The
coach's task is to provide real-time feedbacks to ensure that the routine is
performed in a correct manner. This greatly reduces the risk of injury and
maximise physical gains. We present a simple image similarity measure based on
intersected image region to assess a subject's performance of an aerobic
routine. The method is implemented inside an Augmented Reality (AR) desktop app
that employs a single RGB camera to capture still images of the subject as he
or she progresses through the routine. The background-subtracted body pose
image is compared against the exemplar body pose image (i.e., AR template) at
specific intervals. Based on a limited dataset, our pose matching function is
reported to have an accuracy of 93.67%.
Performance Evaluation of SIFT Descriptor against Common Image Deformations on Iban Plaited Mat Motifs
Authors: Silvia Joseph, Irwandi Hipiny, Hamimah Ujir
Date: 2018-10-03 00:00:00

Borneo indigenous communities are blessed with rich craft heritage. One such
examples is the Iban's plaited mat craft. There have been many efforts by
UNESCO and the Sarawak Government to preserve and promote the craft. One such
method is by developing a mobile app capable of recognising the different mat
motifs. As a first step towards this aim, we presents a novel image dataset
consisting of seven mat motif classes. Each class possesses a unique variation
of chevrons, diagonal shapes, symmetrical, repetitive, geometric and non
geometric patterns. In this study, the performance of the Scale invariant
feature transform (SIFT) descriptor is evaluated against five common image
deformations, i.e., zoom and rotation, viewpoint, image blur, JPEG compression
and illumination. Using our dataset, SIFT performed favourably with test
sequences belonging to Illumination changes, Viewpoint changes, JPEG
compression and Zoom and Rotation. However, it did not performed well with
Image blur test sequences with an average of 1.61 percents retained pairwise
matching after blurring with a Gaussian kernel of 8.0 radius.
A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation
Authors: Clayton Scott
Date: 2018-10-03 00:00:00
Tasks: Binary Classification, Domain Adaptation, General Classification

In the problem of domain adaptation for binary classification, the learner is
presented with labeled examples from a source domain, and must correctly
classify unlabeled examples from a target domain, which may differ from the
source. Previous work on this problem has assumed that the performance measure
of interest is the expected value of some loss function. We introduce a new
Neyman-Pearson-like criterion and argue that, for this optimality criterion,
stronger domain adaptation results are possible than what has previously been
established. In particular, we study a class of domain adaptation problems that
generalizes both the covariate shift assumption and a model for
feature-dependent label noise, and establish optimal classification on the
target domain despite not having access to labelled data from this domain.
Image as Data: Automated Visual Content Analysis for Political Science
Authors: Jungseock Joo, Zachary C. Steinert-Threlkeld
Date: 2018-10-03 00:00:00

Image data provide unique information about political events, actors, and
their interactions which are difficult to measure from or not available in text
data. This article introduces a new class of automated methods based on
computer vision and deep learning which can automatically analyze visual
content data. Scholars have already recognized the importance of visual data
and a variety of large visual datasets have become available. The lack of
scalable analytic methods, however, has prevented from incorporating large
scale image data in political analysis. This article aims to offer an in-depth
overview of automated methods for visual content analysis and explains their
usages and implementations. We further elaborate on how these methods and
results can be validated and interpreted. We then discuss how these methods can
contribute to the study of political communication, identity and politics,
development, and conflict, by enabling a new set of research questions at
scale.
Co-Arg: Cogent Argumentation with Crowd Elicitation
Authors: Mihai Boicu, Dorin Marcu, Gheorghe Tecuci, Lou Kaiser, Chirag Uttamsingh, Navya Kalale
Date: 2018-10-02 00:00:00

This paper presents Co-Arg, a new type of cognitive assistant to an
intelligence analyst that enables the synergistic integration of analyst
imagination and expertise, computer knowledge and critical reasoning, and crowd
wisdom, to draw defensible and persuasive conclusions from masses of evidence
of all types, in a world that is changing all the time. Co-Arg's goal is to
improve the quality of the analytic results and enhance their understandability
for both experts and novices. The performed analysis is based on a sound and
transparent argumentation that links evidence to conclusions in a way that
shows very clearly how the conclusions have been reached, what evidence was
used and how, what is not known, and what assumptions have been made. The
analytic results are presented in a report describes the analytic conclusion
and its probability, the main favoring and disfavoring arguments, the
justification of the key judgments and assumptions, and the missing information
that might increase the accuracy of the solution.
Automated learning with a probabilistic programming language: Birch
Authors: Lawrence M. Murray, Thomas B. Schön
Date: 2018-10-02 00:00:00
Tasks: Form, Multiple Object Tracking, Object Tracking, Probabilistic Programming

This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Turing-complete programming languages. We consider a typical workflow and how probabilistic programming languages can help to automate this workflow, especially in the matching of models with inference methods. We focus on two properties of a model that are critical in this matching: its structure---the conditional dependencies between random variables---and its form---the precise mathematical definition of those dependencies. While the structure and form of a probabilistic model are often fixed a priori, it is a curiosity of probabilistic programming that they need not be, and may instead vary according to random choices made during program execution. We introduce a formal description of models expressed as programs, and discuss some of the ways in which probabilistic programming languages can reveal the structure and form of these, in order to tailor inference methods. We demonstrate the ideas with a new probabilistic programming language called Birch, with a multiple object tracking example.
Band Assignment in Dual Band Systems: A Learning-based Approach
Authors: Daoud Burghal, Rui Wang, Andreas F. Molisch
Date: 2018-10-02 00:00:00

We consider the band assignment problem in dual band systems, where the
base-station (BS) chooses one of the two available frequency bands
(centimeter-wave and millimeter-wave bands) to communicate data to the mobile
station (MS). While the millimeter-wave band offers higher data rate when it is
available, there is a significant probability of outage during which the
communication should be carried on the centimeter-wave band.
  In this work, we use a machine learning framework to provide an efficient and
practical solution to the band assignment problem. In particular, the BS trains
a Neural Network (NN) to predict the right band assignment decision using
observed channel information. We study the performance of the NN in two
environments: (i) A stochastic channel model with correlated bands, and (ii)
microcellular outdoor channels obtained by simulations with a commercial
ray-tracer. For the former case, for sake of comparison we also develop a
threshold based band assignment that relies on the optimal mean square error
estimator of the best band. In addition, we study the performance of the
NN-based solution with different NN structures and different observed
parameters (position, field strength, etc.). We compare the achieved
performance to linear and logistic regression based solutions as well as the
threshold based solution. Under practical constraints, the learning based band
assignment shows competitive or superior performance in both environments.
An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation
Authors: Hamed Zamani, Markus Schedl, Paul Lamere, Ching-Wei Chen
Date: 2018-10-02 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

The ACM Recommender Systems Challenge 2018 focused on the task of automatic music playlist continuation, which is a form of the more general task of sequential recommendation. Given a playlist of arbitrary length with some additional meta-data, the task was to recommend up to 500 tracks that fit the target characteristics of the original playlist. For the RecSys Challenge, Spotify released a dataset of one million user-generated playlists. Participants could compete in two tracks, i.e., main and creative tracks. Participants in the main track were only allowed to use the provided training set, however, in the creative track, the use of external public sources was permitted. In total, 113 teams submitted 1,228 runs to the main track; 33 teams submitted 239 runs to the creative track. The highest performing team in the main track achieved an R-precision of 0.2241, an NDCG of 0.3946, and an average number of recommended songs clicks of 1.784. In the creative track, an R-precision of 0.2233, an NDCG of 0.3939, and a click rate of 1.785 was obtained by the best team. This article provides an overview of the challenge, including motivation, task definition, dataset description, and evaluation. We further report and analyze the results obtained by the top performing teams in each track and explore the approaches taken by the winners. We finally summarize our key findings, discuss generalizability of approaches and results to domains other than music, and list the open avenues and possible future directions in the area of automatic playlist continuation.
Hierarchical community detection by recursive partitioning
Authors: Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Koen Van den Berge, Purnamrita Sarkar, Peter J. Bickel, Elizaveta Levina
Date: 2018-10-02 00:00:00
Tasks: Clustering, Community Detection, Stochastic Block Model

The problem of community detection in networks is usually formulated as finding a single partition of the network into some "correct" number of communities. We argue that it is more interpretable and in some regimes more accurate to construct a hierarchical tree of communities instead. This can be done with a simple top-down recursive partitioning algorithm, starting with a single community and separating the nodes into two communities by spectral clustering repeatedly, until a stopping rule suggests there are no further communities. This class of algorithms is model-free, computationally efficient, and requires no tuning other than selecting a stopping rule. We show that there are regimes where this approach outperforms K-way spectral clustering, and propose a natural framework for analyzing the algorithm's theoretical performance, the binary tree stochastic block model. Under this model, we prove that the algorithm correctly recovers the entire community tree under relatively mild assumptions. We apply the algorithm to a gene network based on gene co-occurrence in 1580 research papers on anemia, and identify six clusters of genes in a meaningful hierarchy. We also illustrate the algorithm on a dataset of statistics papers.
DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks
Authors: João Caldeira, W. L. Kimmy Wu, Brian Nord, Camille Avestruz, Shubhendu Trivedi, Kyle T. Story
Date: 2018-10-02 00:00:00
Tasks: parameter estimation

Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires high signal-to-noise extraction of the projected gravitational potential from the CMB maps. Standard methods for reconstructing the lensing potential employ the quadratic estimator (QE). However, the QE performs suboptimally at the low noise levels expected in upcoming experiments. Other methods, like maximum likelihood estimators (MLE), are under active development. In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet. The network is trained and tested on simulated data, and otherwise has no physical parametrization related to the physical processes of the CMB and gravitational lensing. We show that, over a wide range of angular scales, ResUNets recover the input gravitational potential with a higher signal-to-noise ratio than the QE method, reaching levels comparable to analytic approximations of MLE methods. We demonstrate that the network outputs quantifiably different lensing maps when given input CMB maps generated with different cosmologies. We also show we can use the reconstructed lensing map for cosmological parameter estimation. This application of CNN provides a few innovations at the intersection of cosmology and machine learning. First, while training and regressing on images, we predict a continuous-variable field rather than discrete classes. Second, we are able to establish uncertainty measures for the network output that are analogous to standard methods. We expect this approach to excel in capturing hard-to-model non-Gaussian astrophysical foreground and noise contributions.
Learning to Segment Inputs for NMT Favors Character-Level Processing
Authors: Julia Kreutzer, Artem Sokolov
Date: 2018-10-02 00:00:00
Proceeding: IWSLT (EMNLP) 2018 10
Tasks: Machine Translation, Navigate, NMT, Segmentation, Translation

Most modern neural machine translation (NMT) systems rely on presegmented
inputs. Segmentation granularity importantly determines the input and output
sequence lengths, hence the modeling depth, and source and target vocabularies,
which in turn determine model size, computational costs of softmax
normalization, and handling of out-of-vocabulary words. However, the current
practice is to use static, heuristic-based segmentations that are fixed before
NMT training. This begs the question whether the chosen segmentation is optimal
for the translation task. To overcome suboptimal segmentation choices, we
present an algorithm for dynamic segmentation based on the Adaptative
Computation Time algorithm (Graves 2016), that is trainable end-to-end and
driven by the NMT objective. In an evaluation on four translation tasks we
found that, given the freedom to navigate between different segmentation
levels, the model prefers to operate on (almost) character level, providing
support for purely character-level NMT models from a novel angle.
Adaptive, Personalized Diversity for Visual Discovery
Authors: Choon Hui Teo, Houssam Nassif, Daniel Hill, Sriram Srinavasan, Mitchell Goodman, Vijai Mohan, SVN Vishwanathan
Date: 2018-10-02 00:00:00
Tasks: Diversity

Search queries are appropriate when users have explicit intent, but they
perform poorly when the intent is difficult to express or if the user is simply
looking to be inspired. Visual browsing systems allow e-commerce platforms to
address these scenarios while offering the user an engaging shopping
experience. Here we explore extensions in the direction of adaptive
personalization and item diversification within Stream, a new form of visual
browsing and discovery by Amazon. Our system presents the user with a diverse
set of interesting items while adapting to user interactions. Our solution
consists of three components (1) a Bayesian regression model for scoring the
relevance of items while leveraging uncertainty, (2) a submodular
diversification framework that re-ranks the top scoring items based on
category, and (3) personalized category preferences learned from the user's
behavior. When tested on live traffic, our algorithms show a strong lift in
click-through-rate and session duration.
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding
Authors: Gaurav Singh, James Thomas, Iain J. Marshall, John Shawe-Taylor, Byron C. Wallace
Date: 2018-10-02 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder

We propose a model for tagging unstructured texts with an arbitrary number of
terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat
this as a special case of sequence-to-sequence learning in which the decoder
begins at the root node of an ontological tree and recursively elects to expand
child nodes as a function of the input text, the current node, and the latent
decoder state. In our experiments the proposed method outperforms
state-of-the-art approaches on the important task of automatically assigning
MeSH terms to biomedical abstracts.
A Contextual-Bandit Approach to Personalized News Article Recommendation
Authors: Lihong Li, Wei Chu, John Langford, Robert E. Schapire
Date: 2010-02-28 00:00:00
Tasks: Articles, Collaborative Filtering, Learning Theory

Personalized web services strive to adapt their services (advertisements,
news articles, etc) to individual users by making use of both content and user
information. Despite a few recent advances, this problem remains challenging
for at least two reasons. First, web service is featured with dynamically
changing pools of content, rendering traditional collaborative filtering
methods inapplicable. Second, the scale of most web services of practical
interest calls for solutions that are both fast in learning and computation.
  In this work, we model personalized recommendation of news articles as a
contextual bandit problem, a principled approach in which a learning algorithm
sequentially selects articles to serve users based on contextual information
about the users and articles, while simultaneously adapting its
article-selection strategy based on user-click feedback to maximize total user
clicks.
  The contributions of this work are three-fold. First, we propose a new,
general contextual bandit algorithm that is computationally efficient and well
motivated from learning theory. Second, we argue that any bandit algorithm can
be reliably evaluated offline using previously recorded random traffic.
Finally, using this offline evaluation method, we successfully applied our new
algorithm to a Yahoo! Front Page Today Module dataset containing over 33
million events. Results showed a 12.5% click lift compared to a standard
context-free bandit algorithm, and the advantage becomes even greater when data
gets more scarce.