pwc-archive/papers-with-abstracts huggingface dataset:
Real World Applications of Machine Learning Techniques over Large Mobile Subscriber Datasets
Authors: Jobin Wilson, Chitharanj Kachappilly, Rakesh Mohan, Prateek Kapadia, Arun Soman, Santanu Chaudhury
Date: 2015-02-08 00:00:00
Tasks: BIG-bench Machine Learning, Management

Communication Service Providers (CSPs) are in a unique position to utilize
their vast transactional data assets generated from interactions of subscribers
with network elements as well as with other subscribers. CSPs could leverage
its data assets for a gamut of applications such as service personalization,
predictive offer management, loyalty management, revenue forecasting, network
capacity planning, product bundle optimization and churn management to gain
significant competitive advantage. However, due to the sheer data volume,
variety, velocity and veracity of mobile subscriber datasets, sophisticated
data analytics techniques and frameworks are necessary to derive actionable
insights in a useable timeframe. In this paper, we describe our journey from a
relational database management system (RDBMS) based campaign management
solution which allowed data scientists and marketers to use hand-written rules
for service personalization and targeted promotions to a distributed Big Data
Analytics platform, capable of performing large scale machine learning and data
mining to deliver real time service personalization, predictive modelling and
product optimization. Our work involves a careful blend of technology,
processes and best practices, which facilitate man-machine collaboration and
continuous experimentation to derive measurable economic value from data. Our
platform has a reach of more than 500 million mobile subscribers worldwide,
delivering over 1 billion personalized recommendations annually, processing a
total data volume of 64 Petabytes, corresponding to 8.5 trillion events.
Learning Rank Functionals: An Empirical Study
Authors: Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2014-07-23 00:00:00
Tasks: Information Retrieval, Learning-To-Rank, Question Answering, Recommendation Systems, Retrieval

Ranking is a key aspect of many applications, such as information retrieval,
question answering, ad placement and recommender systems. Learning to rank has
the goal of estimating a ranking model automatically from training data. In
practical settings, the task often reduces to estimating a rank functional of
an object with respect to a query. In this paper, we investigate key issues in
designing an effective learning to rank algorithm. These include data
representation, the choice of rank functionals, the design of the loss function
so that it is correlated with the rank metrics used in evaluation. For the loss
function, we study three techniques: approximating the rank metric by a smooth
function, decomposition of the loss into a weighted sum of element-wise losses
and into a weighted sum of pairwise losses. We then present derivations of
piecewise losses using the theory of high-order Markov chains and Markov random
fields. In experiments, we evaluate these design aspects on two tasks: answer
ranking in a Social Question Answering site, and Web Information Retrieval.
The Silver Lining Around Fearful Living
Authors: Liane Gabora
Date: 2015-02-07 00:00:00

This paper discusses in layperson's terms human and computational studies of
the impact of threat and fear on exploration and creativity. A first study
showed that both killifish from a lake with predators and from a lake without
predators explore a new environment to the same degree and plotting number of
new spaces covered over time generates a hump-shaped curve. However, for the
fish from the lake with predators the curve is shifted to the right; they take
longer. This pattern was replicated by a computer model of exploratory behavior
varying only one parameter, the fear parameter. A second study showed that
stories inspired by threatening photographs were rated as more creative than
stories inspired by non-threatening photographs. Various explanations for the
findings are discussed.
An investigation into language complexity of World-of-Warcraft game-external texts
Authors: Simon Šuster
Date: 2015-02-07 00:00:00
Tasks: Diversity

We present a language complexity analysis of World of Warcraft (WoW)
community texts, which we compare to texts from a general corpus of web
English. Results from several complexity types are presented, including lexical
diversity, density, readability and syntactic complexity. The language of WoW
texts is found to be comparable to the general corpus on some complexity
measures, yet more specialized on other measures. Our findings can be used by
educators willing to include game-related activities into school curricula.
Person Re-identification Meets Image Search
Authors: Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jiahao Bu, Qi Tian
Date: 2015-02-07 00:00:00
Tasks: Image Retrieval, Person Re-Identification

For long time, person re-identification and image search are two separately
studied tasks. However, for person re-identification, the effectiveness of
local features and the "query-search" mode make it well posed for image search
techniques.
  In the light of recent advances in image search, this paper proposes to treat
person re-identification as an image search problem. Specifically, this paper
claims two major contributions. 1) By designing an unsupervised Bag-of-Words
representation, we are devoted to bridging the gap between the two tasks by
integrating techniques from image search in person re-identification. We show
that our system sets up an effective yet efficient baseline that is amenable to
further supervised/unsupervised improvements. 2) We contribute a new high
quality dataset which uses DPM detector and includes a number of distractor
images. Our dataset reaches closer to realistic settings, and new perspectives
are provided.
  Compared with approaches that rely on feature-feature match, our method is
faster by over two orders of magnitude. Moreover, on three datasets, we report
competitive results compared with the state-of-the-art methods.
A Survey on Hough Transform, Theory, Techniques and Applications
Authors: Allam Shehata Hassanein, Sherien Mohammad, Mohamed Sameer, Mohammad Ehab Ragab
Date: 2015-02-07 00:00:00
Tasks: Survey

For more than half a century, the Hough transform is ever-expanding for new
frontiers. Thousands of research papers and numerous applications have evolved
over the decades. Carrying out an all-inclusive survey is hardly possible and
enormously space-demanding. What we care about here is emphasizing some of the
most crucial milestones of the transform. We describe its variations
elaborating on the basic ones such as the line and circle Hough transforms. The
high demand for storage and computation time is clarified with different
solution approaches. Since most uses of the transform take place on binary
images, we have been concerned with the work done directly on gray or color
images. The myriad applications of the standard transform and its variations
have been classified highlighting the up-to-date and the unconventional ones.
Due to its merits such as noise-immunity and expandability, the transform has
an excellent history, and a bright future as well.
Learning Parametric-Output HMMs with Two Aliased States
Authors: Roi Weiss, Boaz Nadler
Date: 2015-02-07 00:00:00
Tasks: Vocal Bursts Valence Prediction

In various applications involving hidden Markov models (HMMs), some of the
hidden states are aliased, having identical output distributions. The
minimality, identifiability and learnability of such aliased HMMs have been
long standing problems, with only partial solutions provided thus far. In this
paper we focus on parametric-output HMMs, whose output distributions come from
a parametric family, and that have exactly two aliased states. For this class,
we present a complete characterization of their minimality and identifiability.
Furthermore, for a large family of parametric output distributions, we derive
computationally efficient and statistically consistent algorithms to detect the
presence of aliasing and learn the aliased HMM transition and emission
parameters. We illustrate our theoretical analysis by several simulations.
Distributed Robust Learning
Authors: Jiashi Feng, Huan Xu, Shie Mannor
Date: 2014-09-21 00:00:00

We propose a framework for distributed robust statistical learning on {\em
big contaminated data}. The Distributed Robust Learning (DRL) framework can
reduce the computational time of traditional robust learning methods by several
orders of magnitude. We analyze the robustness property of DRL, showing that
DRL not only preserves the robustness of the base robust learning method, but
also tolerates contaminations on a constant fraction of results from computing
nodes (node failures). More precisely, even in presence of the most adversarial
outlier distribution over computing nodes, DRL still achieves a breakdown point
of at least $ \lambda^*/2 $, where $ \lambda^* $ is the break down point of
corresponding centralized algorithm. This is in stark contrast with naive
division-and-averaging implementation, which may reduce the breakdown point by
a factor of $ k $ when $ k $ computing nodes are used. We then specialize the
DRL framework for two concrete cases: distributed robust principal component
analysis and distributed robust regression. We demonstrate the efficiency and
the robustness advantages of DRL through comprehensive simulations and
predicting image tags on a large-scale image set.
RELEAF: An Algorithm for Learning and Exploiting Relevance
Authors: Cem Tekin, Mihaela van der Schaar
Date: 2015-02-05 00:00:00
Tasks: Decision Making, Medical Diagnosis, Recommendation Systems, Relation

Recommender systems, medical diagnosis, network security, etc., require
on-going learning and decision-making in real time. These -- and many others --
represent perfect examples of the opportunities and difficulties presented by
Big Data: the available information often arrives from a variety of sources and
has diverse features so that learning from all the sources may be valuable but
integrating what is learned is subject to the curse of dimensionality. This
paper develops and analyzes algorithms that allow efficient learning and
decision-making while avoiding the curse of dimensionality. We formalize the
information available to the learner/decision-maker at a particular time as a
context vector which the learner should consider when taking actions. In
general the context vector is very high dimensional, but in many settings, the
most relevant information is embedded into only a few relevant dimensions. If
these relevant dimensions were known in advance, the problem would be simple --
but they are not. Moreover, the relevant dimensions may be different for
different actions. Our algorithm learns the relevant dimensions for each
action, and makes decisions based in what it has learned. Formally, we build on
the structure of a contextual multi-armed bandit by adding and exploiting a
relevance relation. We prove a general regret bound for our algorithm whose
time order depends only on the maximum number of relevant dimensions among all
the actions, which in the special case where the relevance relation is
single-valued (a function), reduces to $\tilde{O}(T^{2(\sqrt{2}-1)})$; in the
absence of a relevance relation, the best known contextual bandit algorithms
achieve regret $\tilde{O}(T^{(D+1)/(D+2)})$, where $D$ is the full dimension of
the context vector.
Reflectance Hashing for Material Recognition
Authors: Hang Zhang, Kristin Dana, Ko Nishino
Date: 2015-02-07 00:00:00
Proceeding: CVPR 2015 6
Tasks: Dictionary Learning, Material Recognition

We introduce a novel method for using reflectance to identify materials.
Reflectance offers a unique signature of the material but is challenging to
measure and use for recognizing materials due to its high-dimensionality. In
this work, one-shot reflectance is captured using a unique optical camera
measuring {\it reflectance disks} where the pixel coordinates correspond to
surface viewing angles. The reflectance has class-specific stucture and angular
gradients computed in this reflectance space reveal the material class.
  These reflectance disks encode discriminative information for efficient and
accurate material recognition. We introduce a framework called reflectance
hashing that models the reflectance disks with dictionary learning and binary
hashing. We demonstrate the effectiveness of reflectance hashing for material
recognition with a number of real-world materials.
Discriminative training for Convolved Multiple-Output Gaussian processes
Authors: Sebastián Gómez-González, Mauricio A. Álvarez, Hernán Felipe García
Date: 2015-02-07 00:00:00
Tasks: Activity Recognition, Emotion Recognition, Face Recognition, Gaussian Processes, General Classification, Multi-class Classification

Multi-output Gaussian processes (MOGP) are probability distributions over
vector-valued functions, and have been previously used for multi-output
regression and for multi-class classification. A less explored facet of the
multi-output Gaussian process is that it can be used as a generative model for
vector-valued random fields in the context of pattern recognition. As a
generative model, the multi-output GP is able to handle vector-valued functions
with continuous inputs, as opposed, for example, to hidden Markov models. It
also offers the ability to model multivariate random functions with high
dimensional inputs. In this report, we use a discriminative training criteria
known as Minimum Classification Error to fit the parameters of a multi-output
Gaussian process. We compare the performance of generative training and
discriminative training of MOGP in emotion recognition, activity recognition,
and face recognition. We also compare the proposed methodology against hidden
Markov models trained in a generative and in a discriminative way.
Massively Multitask Networks for Drug Discovery
Authors: Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
Date: 2015-02-06 00:00:00
Tasks: Drug Discovery

Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process.
Learning from Multiple Sources for Video Summarisation
Authors: Xiatian Zhu, Chen Change Loy, Shaogang Gong
Date: 2015-01-13 00:00:00
Tasks: Clustering, Video Understanding

Many visual surveillance tasks, e.g.video summarisation, is conventionally
accomplished through analysing imagerybased features. Relying solely on visual
cues for public surveillance video understanding is unreliable, since visual
observations obtained from public space CCTV video data are often not
sufficiently trustworthy and events of interest can be subtle. On the other
hand, non-visual data sources such as weather reports and traffic sensory
signals are readily accessible but are not explored jointly to complement
visual data for video content analysis and summarisation. In this paper, we
present a novel unsupervised framework to learn jointly from both visual and
independently-drawn non-visual data sources for discovering meaningful latent
structure of surveillance video data. In particular, we investigate ways to
cope with discrepant dimension and representation whist associating these
heterogeneous data sources, and derive effective mechanism to tolerate with
missing and incomplete data from different sources. We show that the proposed
multi-source learning framework not only achieves better video content
clustering than state-of-the-art methods, but also is capable of accurately
inferring missing non-visual semantics from previously unseen videos. In
addition, a comprehensive user study is conducted to validate the quality of
video summarisation generated using the proposed multi-source model.
Temporally Coherent Bayesian Models for Entity Discovery in Videos by Tracklet Clustering
Authors: Adway Mitra, Soma Biswas, Chiranjib Bhattacharyya
Date: 2014-09-22 00:00:00
Tasks: Clustering, Video Summarization

A video can be represented as a sequence of tracklets, each spanning 10-20
frames, and associated with one entity (eg. a person). The task of \emph{Entity
Discovery} in videos can be naturally posed as tracklet clustering. We approach
this task by leveraging \emph{Temporal Coherence}(TC): the fundamental property
of videos that each tracklet is likely to be associated with the same entity as
its temporal neighbors. Our major contributions are the first Bayesian
nonparametric models for TC at tracklet-level. We extend Chinese Restaurant
Process (CRP) to propose TC-CRP, and further to Temporally Coherent Chinese
Restaurant Franchise (TC-CRF) to jointly model short temporal segments. On the
task of discovering persons in TV serial videos without meta-data like scripts,
these methods show considerable improvement in cluster purity and person
coverage compared to state-of-the-art approaches to tracklet clustering. We
represent entities with mixture components, and tracklets with vectors of very
generic features, which can work for any type of entity (not necessarily
person). The proposed methods can perform online tracklet clustering on
streaming videos with little performance deterioration unlike existing
approaches, and can automatically reject tracklets resulting from false
detections. Finally we discuss entity-driven video summarization- where some
temporal segments of the video are selected automatically based on the
discovered entities.
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds
Authors: Yuchen Zhang, Martin J. Wainwright, Michael. I. Jordan
Date: 2015-02-05 00:00:00

We study the following generalized matrix rank estimation problem: given an
$n \times n$ matrix and a constant $c \geq 0$, estimate the number of
eigenvalues that are greater than $c$. In the distributed setting, the matrix
of interest is the sum of $m$ matrices held by separate machines. We show that
any deterministic algorithm solving this problem must communicate $\Omega(n^2)$
bits, which is order-equivalent to transmitting the whole matrix. In contrast,
we propose a randomized algorithm that communicates only $\widetilde O(n)$
bits. The upper bound is matched by an $\Omega(n)$ lower bound on the
randomized communication complexity. We demonstrate the practical effectiveness
of the proposed algorithm with some numerical experiments.
A Multistage Stochastic Programming Approach to the Dynamic and Stochastic VRPTW - Extended version
Authors: Michael Saint-Guillain, Yves Deville, Christine Solnon
Date: 2015-02-06 00:00:00

We consider a dynamic vehicle routing problem with time windows and
stochastic customers (DS-VRPTW), such that customers may request for services
as vehicles have already started their tours. To solve this problem, the goal
is to provide a decision rule for choosing, at each time step, the next action
to perform in light of known requests and probabilistic knowledge on requests
likelihood. We introduce a new decision rule, called Global Stochastic
Assessment (GSA) rule for the DS-VRPTW, and we compare it with existing
decision rules, such as MSA. In particular, we show that GSA fully integrates
nonanticipativity constraints so that it leads to better decisions in our
stochastic context. We describe a new heuristic approach for efficiently
approximating our GSA rule. We introduce a new waiting strategy. Experiments on
dynamic and stochastic benchmarks, which include instances of different degrees
of dynamism, show that not only our approach is competitive with
state-of-the-art methods, but also enables to compute meaningful offline
solutions to fully dynamic problems where absolutely no a priori customer
request is provided.
A Quantum Production Model
Authors: Luís Tarrataca, Andreas Wichert
Date: 2015-02-06 00:00:00
Tasks: model

The production system is a theoretical model of computation relevant to the
artificial intelligence field allowing for problem solving procedures such as
hierarchical tree search. In this work we explore some of the connections
between artificial intelligence and quantum computation by presenting a model
for a quantum production system. Our approach focuses on initially developing a
model for a reversible production system which is a simple mapping of Bennett's
reversible Turing machine. We then expand on this result in order to
accommodate for the requirements of quantum computation. We present the details
of how our proposition can be used alongside Grover's algorithm in order to
yield a speedup comparatively to its classical counterpart. We discuss the
requirements associated with such a speedup and how it compares against a
similar quantum hierarchical search approach.
Active Function Cross-Entropy Clustering
Authors: P. Spurek, J. Tabor, P. Markowicz
Date: 2015-02-06 00:00:00
Tasks: Clustering, Density Estimation

Gaussian Mixture Models (GMM) have found many applications in density
estimation and data clustering. However, the model does not adapt well to
curved and strongly nonlinear data. Recently there appeared an improvement
called AcaGMM (Active curve axis Gaussian Mixture Model), which fits Gaussians
along curves using an EM-like (Expectation Maximization) approach.
  Using the ideas standing behind AcaGMM, we build an alternative active
function model of clustering, which has some advantages over AcaGMM. In
particular it is naturally defined in arbitrary dimensions and enables an easy
adaptation to clustering of complicated datasets along the predefined family of
functions. Moreover, it does not need external methods to determine the number
of clusters as it automatically reduces the number of groups on-line.
Data Fusion by Matrix Factorization
Authors: Marinka Žitnik, Blaž Zupan
Date: 2013-07-02 00:00:00
Tasks: Data Integration

For most problems in science and engineering we can obtain data sets that
describe the observed system from various perspectives and record the behavior
of its individual components. Heterogeneous data sets can be collectively mined
by data fusion. Fusion can focus on a specific target relation and exploit
directly associated data together with contextual data and data about system's
constraints. In the paper we describe a data fusion approach with penalized
matrix tri-factorization (DFMF) that simultaneously factorizes data matrices to
reveal hidden associations. The approach can directly consider any data that
can be expressed in a matrix, including those from feature-based
representations, ontologies, associations and networks. We demonstrate the
utility of DFMF for gene function prediction task with eleven different data
sources and for prediction of pharmacologic actions by fusing six data sources.
Our data fusion algorithm compares favorably to alternative data integration
approaches and achieves higher accuracy than can be obtained from any single
data source alone.