pwc-archive/papers-with-abstracts huggingface dataset:
A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation
Authors: Kun Wang, WaiChing Sun, Qiang Du
Date: 2019-03-08 00:00:00
Tasks: Deep Reinforcement Learning, Knowledge Graphs, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a multi-agent meta-modeling game to generate data, knowledge,
and models that make predictions on constitutive responses of elasto-plastic
materials. We introduce a new concept from graph theory where a modeler agent
is tasked with evaluating all the modeling options recast as a directed
multigraph and find the optimal path that links the source of the directed
graph (e.g. strain history) to the target (e.g. stress) measured by an
objective function. Meanwhile, the data agent, which is tasked with generating
data from real or virtual experiments (e.g. molecular dynamics, discrete
element simulations), interacts with the modeling agent sequentially and uses
reinforcement learning to design new experiments to optimize the prediction
capacity. Consequently, this treatment enables us to emulate an idealized
scientific collaboration as selections of the optimal choices in a decision
tree search done automatically via deep reinforcement learning.
Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics
Authors: Hongmei Wang, Zhenzhen Wu, Shuai Ma, Songtao Lu, Han Zhang, Guoru Ding, Shiyin Li
Date: 2019-03-08 00:00:00

In this paper, we investigate deep learning (DL)-enabled signal demodulation
methods and establish the first open dataset of real modulated signals for
wireless communication systems. Specifically, we propose a flexible
communication prototype platform for measuring real modulation dataset. Then,
based on the measured dataset, two DL-based demodulators, called deep belief
network (DBN)-support vector machine (SVM) demodulator and adaptive boosting
(AdaBoost) based demodulator, are proposed. The proposed DBN-SVM based
demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of
DBN as a feature extractor and SVM as a feature classifier. In DBN-SVM based
demodulator, the received signals are normalized before being fed to the DBN
network. Furthermore, an AdaBoost based demodulator is developed, which employs
the $k$-Nearest Neighbor (KNN) as a weak classifier to form a strong combined
classifier. Finally, experimental results indicate that the proposed DBN-SVM
based demodulator and AdaBoost based demodulator are superior to the single
classification method using DBN, SVM, and maximum likelihood (MLD) based
demodulator.
Based on Graph-VAE Model to Predict Student's Score
Authors: Yang Zhang, Mingming Lu
Date: 2019-03-08 00:00:00
Tasks: Clustering, Deep Learning, Graph Neural Network

The OECD pointed out that the best way to keep students up to school is to
intervene as early as possible [1]. Using education big data and deep learning
to predict student's score provides new resources and perspectives for early
intervention. Previous forecasting schemes often requires manual filter of
features , a large amount of prior knowledge and expert knowledge. Deep
learning can automatically extract features without manual intervention to
achieve better predictive performance. In this paper, the graph neural network
matrix filling model (Graph-VAE) based on deep learning can automatically
extract features without a large amount of prior knowledge. The experiment
proves that our model is better than the traditional solution in the student's
score dataset, and it better describes the correlation and difference between
the students and the curriculum, and dimensionality reducing the vector of
coding result is visualized, the clustering effect is consistent with the real
data distribution clustering. In addition, we use gradient-based attribution
methods to analyze the key factors that influence performance prediction.
Auto-Vectorizing TensorFlow Graphs: Jacobians, Auto-Batching And Beyond
Authors: Ashish Agarwal, Igor Ganichev
Date: 2019-03-08 00:00:00

We propose a static loop vectorization optimization on top of high level
dataflow IR used by frameworks like TensorFlow. A new statically vectorized
parallel-for abstraction is provided on top of TensorFlow, and used for
applications ranging from auto-batching and per-example gradients, to jacobian
computation, optimized map functions and input pipeline optimization. We report
huge speedups compared to both loop based implementations, as well as run-time
batching adopted by the DyNet framework.
Automatic Ontology Learning from Domain-Specific Short Unstructured Text Data
Authors: Yiming Xu, Dnyanesh Rajpathak, Ian Gibbs, Diego Klabjan
Date: 2019-03-07 00:00:00
Tasks: General Classification, Information Retrieval, Retrieval

Ontology learning is a critical task in industry, dealing with identifying
and extracting concepts captured in text data such that these concepts can be
used in different tasks, e.g. information retrieval. Ontology learning is
non-trivial due to several reasons with limited amount of prior research work
that automatically learns a domain specific ontology from data. In our work, we
propose a two-stage classification system to automatically learn an ontology
from unstructured text data. We first collect candidate concepts, which are
classified into concepts and irrelevant collocates by our first classifier. The
concepts from the first classifier are further classified by the second
classifier into different concept types. The proposed system is deployed as a
prototype at a company and its performance is validated by using complaint and
repair verbatim data collected in automotive industry from different data
sources.
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization
Authors: Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
Date: 2019-03-07 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: compressed sensing, Multi-Armed Bandits, Reinforcement Learning

We present a new algorithm ASEBO for optimizing high-dimensional blackbox functions. ASEBO adapts to the geometry of the function and learns optimal sets of sensing directions, which are used to probe it, on-the-fly. It addresses the exploration-exploitation trade-off of blackbox optimization with expensive blackbox queries by continuously learning the bias of the lower-dimensional model used to approximate gradients of smoothings of the function via compressed sensing and contextual bandits methods. To obtain this model, it leverages techniques from the emerging theory of active subspaces in the novel ES blackbox optimization context. As a result, ASEBO learns the dynamically changing intrinsic dimensionality of the gradient space and adapts to the hardness of different stages of the optimization without external supervision. Consequently, it leads to more sample-efficient blackbox optimization than state-of-the-art algorithms. We provide theoretical results and test ASEBO advantages over other methods empirically by evaluating it on the set of reinforcement learning policy optimization tasks as well as functions from the recently open-sourced Nevergrad library.
Deep Learning for Channel Coding via Neural Mutual Information Estimation
Authors: Rick Fritschek, Rafael F. Schaefer, Gerhard Wunder
Date: 2019-03-07 00:00:00
Tasks: Decoder, Deep Learning, Mutual Information Estimation, Reinforcement Learning

End-to-end deep learning for communication systems, i.e., systems whose
encoder and decoder are learned, has attracted significant interest recently,
due to its performance which comes close to well-developed classical
encoder-decoder designs. However, one of the drawbacks of current learning
approaches is that a differentiable channel model is needed for the training of
the underlying neural networks. In real-world scenarios, such a channel model
is hardly available and often the channel density is not even known at all.
Some works, therefore, focus on a generative approach, i.e., generating the
channel from samples, or rely on reinforcement learning to circumvent this
problem. We present a novel approach which utilizes a recently proposed neural
estimator of mutual information. We use this estimator to optimize the encoder
for a maximized mutual information, only relying on channel samples. Moreover,
we show that our approach achieves the same performance as state-of-the-art
end-to-end learning with perfect channel model knowledge.
A Clustering-Based Combinatorial Approach to Unsupervised Matching of Product Titles
Authors: Leonidas Akritidis, Athanasios Fevgas, Panayiotis Bozanis, Christos Makris
Date: 2019-03-07 00:00:00
Tasks: Clustering, Retrieval

The constant growth of the e-commerce industry has rendered the problem of
product retrieval particularly important. As more enterprises move their
activities on the Web, the volume and the diversity of the product-related
information increase quickly. These factors make it difficult for the users to
identify and compare the features of their desired products. Recent studies
proved that the standard similarity metrics cannot effectively identify
identical products, since similar titles often refer to different products and
vice-versa. Other studies employed external data sources (search engines) to
enrich the titles; these solutions are rather impractical mainly because the
external data fetching is slow. In this paper we introduce UPM, an unsupervised
algorithm for matching products by their titles. UPM is independent of any
external sources, since it analyzes the titles and extracts combinations of
words out of them. These combinations are evaluated according to several
criteria, and the most appropriate of them constitutes the cluster where a
product is classified into. UPM is also parameter-free, it avoids product
pairwise comparisons, and includes a post-processing verification stage which
corrects the erroneous matches. The experimental evaluation of UPM demonstrated
its superiority against the state-of-the-art approaches in terms of both
efficiency and effectiveness.
Distributed Online Convex Optimization with Time-Varying Coupled Inequality Constraints
Authors: Xinlei Yi, Xiuxian Li, Lihua Xie, Karl H. Johansson
Date: 2019-03-06 00:00:00

This paper considers distributed online optimization with time-varying coupled inequality constraints. The global objective function is composed of local convex cost and regularization functions and the coupled constraint function is the sum of local convex functions. A distributed online primal-dual dynamic mirror descent algorithm is proposed to solve this problem, where the local cost, regularization, and constraint functions are held privately and revealed only after each time slot. Without assuming Slater's condition, we first derive regret and constraint violation bounds for the algorithm and show how they depend on the stepsize sequences, the accumulated dynamic variation of the comparator sequence, the number of agents, and the network connectivity. As a result, under some natural decreasing stepsize sequences, we prove that the algorithm achieves sublinear dynamic regret and constraint violation if the accumulated dynamic variation of the optimal sequence also grows sublinearly. We also prove that the algorithm achieves sublinear static regret and constraint violation under mild conditions. Assuming Slater's condition, we show that the algorithm achieves smaller bounds on the constraint violation. In addition, smaller bounds on the static regret are achieved when the objective function is strongly convex. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.
Coping with Large Traffic Volumes in Schedule-Driven Traffic Signal Control
Authors: Hsu-Chieh Hu, Stephen F. Smith
Date: 2019-03-06 00:00:00
Tasks: Management, Scheduling, Traffic Signal Control

Recent work in decentralized, schedule-driven traffic control has
demonstrated the ability to significantly improve traffic flow efficiency in
complex urban road networks. However, in situations where vehicle volumes
increase to the point that the physical capacity of a road network reaches or
exceeds saturation, it has been observed that the effectiveness of a
schedule-driven approach begins to degrade, leading to progressively higher
network congestion. In essence, the traffic control problem becomes less of a
scheduling problem and more of a queue management problem in this circumstance.
In this paper we propose a composite approach to real-time traffic control that
uses sensed information on queue lengths to influence scheduling decisions and
gracefully shift the signal control strategy to queue management in high
volume/high congestion settings. Specifically, queue-length information is used
to establish weights for the sensed vehicle clusters that must be scheduled
through a given intersection at any point, and hence bias the wait time
minimization calculation. To compute these weights, we develop a model in which
successive movement phases are viewed as different states of an Ising model,
and parameters quantify strength of interactions. To ensure scalability, queue
information is only exchanged between direct neighbors and the asynchronous
nature of local intersection scheduling is preserved. We demonstrate the
potential of the approach through microscopic traffic simulation of a
real-world road network, showing a 60% reduction in average wait times over the
baseline schedule-driven approach in heavy traffic scenarios. We also report
initial field test results, which show the ability to reduce queues during
heavy traffic periods.
Large Scale Product Categorization using Structured and Unstructured Attributes
Authors: Abhinandan Krishnan, Abilash Amarthaluri
Date: 2019-03-01 00:00:00
Tasks: Attribute, General Classification, Multi Class Text Classification, Product Categorization, text-classification, Text Classification

Product categorization using text data for eCommerce is a very challenging
extreme classification problem with several thousands of classes and several
millions of products to classify. Even though multi-class text classification
is a well studied problem both in academia and industry, most approaches either
deal with treating product content as a single pile of text, or only consider a
few product attributes for modelling purposes. Given the variety of products
sold on popular eCommerce platforms, it is hard to consider all available
product attributes as part of the modeling exercise, considering that products
possess their own unique set of attributes based on category. In this paper, we
compare hierarchical models to flat models and show that in specific cases,
flat models perform better. We explore two Deep Learning based models that
extract features from individual pieces of unstructured data from each product
and then combine them to create a product signature. We also propose a novel
idea of using structured attributes and their values together in an
unstructured fashion along with convolutional filters such that the ordering of
the attributes and the differing attributes by product categories no longer
becomes a modelling challenge. This approach is also more robust to the
presence of faulty product attribute names and values and can elegantly
generalize to use both closed list and open list attributes.
On Application of Learning to Rank for E-Commerce Search
Authors: Shubhra Kanti Karmaker Santu, Parikshit Sondhi, ChengXiang Zhai
Date: 2019-03-01 00:00:00
Tasks: Attribute, Information Retrieval, Learning-To-Rank, Retrieval

E-Commerce (E-Com) search is an emerging important new application of
information retrieval. Learning to Rank (LETOR) is a general effective strategy
for optimizing search engines, and is thus also a key technology for E-Com
search. While the use of LETOR for web search has been well studied, its use
for E-Com search has not yet been well explored. In this paper, we discuss the
practical challenges in applying learning to rank methods to E-Com search,
including the challenges in feature representation, obtaining reliable
relevance judgments, and optimally exploiting multiple user feedback signals
such as click rates, add-to-cart ratios, order rates, and revenue. We study
these new challenges using experiments on industry data sets and report several
interesting findings that can provide guidance on how to optimally apply LETOR
to E-Com search: First, popularity-based features defined solely on product
items are very useful and LETOR methods were able to effectively optimize their
combination with relevance-based features. Second, query attribute sparsity
raises challenges for LETOR, and selecting features to reduce/avoid sparsity is
beneficial. Third, while crowdsourcing is often useful for obtaining relevance
judgments for Web search, it does not work as well for E-Com search due to
difficulty in eliciting sufficiently fine grained relevance judgments. Finally,
among the multiple feedback signals, the order rate is found to be the most
robust training objective, followed by click rate, while add-to-cart ratio
seems least robust, suggesting that an effective practical strategy may be to
initially use click rates for training and gradually shift to using order rates
as they become available.
Lenses and Learners
Authors: Brendan Fong, Michael Johnson
Date: 2019-03-05 00:00:00

Lenses are a well-established structure for modelling bidirectional
transformations, such as the interactions between a database and a view of it.
Lenses may be symmetric or asymmetric, and may be composed, forming the
morphisms of a monoidal category. More recently, the notion of a learner has
been proposed: these provide a compositional way of modelling supervised
learning algorithms, and again form the morphisms of a monoidal category. In
this paper, we show that the two concepts are tightly linked. We show both that
there is a faithful, identity-on-objects symmetric monoidal functor embedding a
category of asymmetric lenses into the category of learners, and furthermore
there is such a functor embedding the category of learners into a category of
symmetric lenses.
A seismic scaling relation for stellar age
Authors: Earl Patrick Bellinger
Date: 2019-03-11 00:00:00
Proceeding: Monthly Notices of the Royal Astronomical Society 2019 3
Tasks: Relation

A simple solar scaling relation for estimating the ages of main-sequence stars from asteroseismic and spectroscopic data is developed. New seismic scaling relations for estimating mass and radius are presented as well, including a purely seismic radius scaling relation (i.e., no dependence on temperature). The relations show substantial improvement over the classical scaling relations and perform similarly well to grid-based modeling.
Label Consistent Fisher Vectors for Supervised Feature Aggregation
Authors: Quan Wang, Xin Shen, Meng Wang, Kim L. Boyer
Date: 2014-08-24 00:00:00
Proceeding: 2014 22nd International Conference on Pattern Recognition 2014 8
Tasks: Classification, General Classification, image-classification, Image Classification, Retrieval

In this paper, we present a simple and efficient way to add supervised information into Fisher vectors, which has become a popular image representation method for image classification and retrieval purposes in recent years. The basic idea of our approach is to improve the Fisher kernel in the training process by adding a discriminative label comparison matrix to it. The resulting new representations, which we call Label Consistent Fisher Vectors (LCFV), can be solved for both over determined and underdetermined cases. We show that LCFV has better classification performance than traditional Fisher vectors on three public datasets.
A survey of statistical network models
Authors: Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg, Edoardo M. Airoldi
Date: 2009-12-29 00:00:00
Tasks: Sociology, Survey

Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics.
A Bayesian Approach to Network Modularity
Authors: Jake M. Hofman, Chris H. Wiggins
Date: 2007-09-21 00:00:00
Tasks: Model Selection

We present an efficient, principled, and interpretable technique for
inferring module assignments and for identifying the optimal number of modules
in a given network. We show how several existing methods for finding modules
can be described as variant, special, or limiting cases of our work, and how
the method overcomes the resolution limit problem, accurately recovering the
true number of modules. Our approach is based on Bayesian methods for model
selection which have been used with success for almost a century, implemented
using a variational technique developed only in the past decade. We apply the
technique to synthetic and real networks and outline how the method naturally
allows selection among competing models.
Universal Properties of Mythological Networks
Authors: Pádraig Mac Carron, Ralph Kenna
Date: 2012-05-19 00:00:00

As in statistical physics, the concept of universality plays an important,
albeit qualitative, role in the field of comparative mythology. Here we apply
statistical mechanical tools to analyse the networks underlying three iconic
mythological narratives with a view to identifying common and distinguishing
quantitative features. Of the three narratives, an Anglo-Saxon and a Greek text
are mostly believed by antiquarians to be partly historically based while the
third, an Irish epic, is often considered to be fictional. Here we show that
network analysis is able to discriminate real from imaginary social networks
and place mythological narratives on the spectrum between them. Moreover, the
perceived artificiality of the Irish narrative can be traced back to anomalous
features associated with six characters. Considering these as amalgams of
several entities or proxies, renders the plausibility of the Irish text
comparable to the others from a network-theoretic point of view.