pwc-archive/papers-with-abstracts huggingface dataset:
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
Authors: Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, Victor Lempitsky
Date: 2016-03-10 00:00:00
Tasks: Style Transfer

Gatys et al. recently demonstrated that deep networks can generate beautiful
textures and stylized images from a single texture example. However, their
methods requires a slow and memory-consuming optimization process. We propose
here an alternative approach that moves the computational burden to a learning
stage. Given a single example of a texture, our approach trains compact
feed-forward convolutional networks to generate multiple samples of the same
texture of arbitrary size and to transfer artistic style from a given image to
any other image. The resulting networks are remarkably light-weight and can
generate textures of quality comparable to Gatys~et~al., but hundreds of times
faster. More generally, our approach highlights the power and flexibility of
generative feed-forward models trained with complex and expressive loss
functions.
Lexical bundles in computational linguistics academic literature
Authors: Adel Rahimi
Date: 2016-03-09 00:00:00

In this study we analyzed a corpus of 8 million words academic literature
from Computational lingustics' academic literature. the lexical bundles from
this corpus are categorized based on structures and functions.
Efficient Bayesian experimentation using an expected information gain lower bound
Authors: Panagiotis Tsilifis, Roger G. Ghanem, Paris Hajali
Date: 2015-05-30 00:00:00
Tasks: Experimental Design

Experimental design is crucial for inference where limitations in the data
collection procedure are present due to cost or other restrictions. Optimal
experimental designs determine parameters that in some appropriate sense make
the data the most informative possible. In a Bayesian setting this is
translated to updating to the best possible posterior. Information theoretic
arguments have led to the formation of the expected information gain as a
design criterion. This can be evaluated mainly by Monte Carlo sampling and
maximized by using stochastic approximation methods, both known for being
computationally expensive tasks. We propose a framework where a lower bound of
the expected information gain is used as an alternative design criterion. In
addition to alleviating the computational burden, this also addresses issues
concerning estimation bias. The problem of permeability inference in a large
contaminated area is used to demonstrate the validity of our approach where we
employ the massively parallel version of the multiphase multicomponent
simulator TOUGH2 to simulate contaminant transport and a Polynomial Chaos
approximation of the forward model that further accelerates the objective
function evaluations. The proposed methodology is demonstrated to a setting
where field measurements are available.
Spectral Ranking using Seriation
Authors: Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic
Date: 2014-06-20 00:00:00

We describe a seriation algorithm for ranking a set of items given pairwise
comparisons between these items. Intuitively, the algorithm assigns similar
rankings to items that compare similarly with all others. It does so by
constructing a similarity matrix from pairwise comparisons, using seriation
methods to reorder this matrix and construct a ranking. We first show that this
spectral seriation algorithm recovers the true ranking when all pairwise
comparisons are observed and consistent with a total order. We then show that
ranking reconstruction is still exact when some pairwise comparisons are
corrupted or missing, and that seriation based spectral ranking is more robust
to noise than classical scoring methods. Finally, we bound the ranking error
when only a random subset of the comparions are observed. An additional benefit
of the seriation formulation is that it allows us to solve semi-supervised
ranking problems. Experiments on both synthetic and real datasets demonstrate
that seriation based spectral ranking achieves competitive and in some cases
superior performance compared to classical ranking methods.
Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies
Authors: Francois W. Belletti, Evan R. Sparks, Michael J. Franklin, Alexandre M. Bayen, Joseph E. Gonzalez
Date: 2016-03-10 00:00:00
Tasks: Causal Inference, Time Series, Time Series Analysis

Linear causal analysis is central to a wide range of important application
spanning finance, the physical sciences, and engineering. Much of the existing
literature in linear causal analysis operates in the time domain.
Unfortunately, the direct application of time domain linear causal analysis to
many real-world time series presents three critical challenges: irregular
temporal sampling, long range dependencies, and scale. Moreover, real-world
data is often collected at irregular time intervals across vast arrays of
decentralized sensors and with long range dependencies which make naive time
domain correlation estimators spurious. In this paper we present a frequency
domain based estimation framework which naturally handles irregularly sampled
data and long range dependencies while enabled memory and communication
efficient distributed processing of time series data. By operating in the
frequency domain we eliminate the need to interpolate and help mitigate the
effects of long range dependencies. We implement and evaluate our new work-flow
in the distributed setting using Apache Spark and demonstrate on both Monte
Carlo simulations and high-frequency financial trading that we can accurately
recover causal structure at scale.
Some like it hot - visual guidance for preference prediction
Authors: Rasmus Rothe, Radu Timofte, Luc van Gool
Date: 2015-10-27 00:00:00
Proceeding: CVPR 2016 6
Tasks: Collaborative Filtering, Prediction, regression

For people first impressions of someone are of determining importance. They
are hard to alter through further information. This begs the question if a
computer can reach the same judgement. Earlier research has already pointed out
that age, gender, and average attractiveness can be estimated with reasonable
precision. We improve the state-of-the-art, but also predict - based on
someone's known preferences - how much that particular person is attracted to a
novel face. Our computational pipeline comprises a face detector, convolutional
neural networks for the extraction of deep features, standard support vector
regression for gender, age and facial beauty, and - as the main novelties -
visual regularized collaborative filtering to infer inter-person preferences as
well as a novel regression technique for handling visual queries without rating
history. We validate the method using a very large dataset from a dating site
as well as images from celebrities. Our experiments yield convincing results,
i.e. we predict 76% of the ratings correctly solely based on an image, and
reveal some sociologically relevant conclusions. We also validate our
collaborative filtering solution on the standard MovieLens rating dataset,
augmented with movie posters, to predict an individual's movie rating. We
demonstrate our algorithms on howhot.io which went viral around the Internet
with more than 50 million pictures evaluated in the first month.
Recent advances on inconsistency indices for pairwise comparisons - a commentary
Authors: Matteo Brunelli
Date: 2015-03-28 00:00:00

This paper recalls the definition of consistency for pairwise comparison
matrices and briefly presents the concept of inconsistency index in connection
to other aspects of the theory of pairwise comparisons. By commenting on a
recent contribution by Koczkodaj and Szwarc, it will be shown that the
discussion on inconsistency indices is far from being over, and the ground is
still fertile for debates.
Sequential Monte Carlo Methods for System Identification
Authors: Thomas B. Schön, Fredrik Lindsten, Johan Dahlin, Johan Wågberg, Christian A. Naesseth, Andreas Svensson, Liang Dai
Date: 2015-03-20 00:00:00
Tasks: State Estimation, State Space Models

One of the key challenges in identifying nonlinear and possibly non-Gaussian
state space models (SSMs) is the intractability of estimating the system state.
Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced
more than two decades ago), provide numerical solutions to the nonlinear state
estimation problems arising in SSMs. When combined with additional
identification techniques, these algorithms provide solid solutions to the
nonlinear system identification problem. We describe two general strategies for
creating such combinations and discuss why SMC is a natural tool for
implementing these strategies.
An Innovative Imputation and Classification Approach for Accurate Disease Prediction
Authors: Yelipe UshaRani, P. Sammulal
Date: 2016-03-10 00:00:00
Tasks: Attribute, Classification, Clustering, Dimensionality Reduction, Disease Prediction, General Classification, Imputation, Missing Values

Imputation of missing attribute values in medical datasets for extracting
hidden knowledge from medical datasets is an interesting research topic of
interest which is very challenging. One cannot eliminate missing values in
medical records. The reason may be because some tests may not been conducted as
they are cost effective, values missed when conducting clinical trials, values
may not have been recorded to name some of the reasons. Data mining researchers
have been proposing various approaches to find and impute missing values to
increase classification accuracies so that disease may be predicted accurately.
In this paper, we propose a novel imputation approach for imputation of missing
values and performing classification after fixing missing values. The approach
is based on clustering concept and aims at dimensionality reduction of the
records. The case study discussed shows that missing values can be fixed and
imputed efficiently by achieving dimensionality reduction. The importance of
proposed approach for classification is visible in the case study which assigns
single class label in contrary to multi-label assignment if dimensionality
reduction is not performed.
Hierarchical Linearly-Solvable Markov Decision Problems
Authors: Anders Jonsson, Vicenç Gómez
Date: 2016-03-10 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a hierarchical reinforcement learning framework that formulates
each task in the hierarchy as a special type of Markov decision process for
which the Bellman equation is linear and has analytical solution. Problems of
this type, called linearly-solvable MDPs (LMDPs) have interesting properties
that can be exploited in a hierarchical setting, such as efficient learning of
the optimal value function or task compositionality. The proposed hierarchical
approach can also be seen as a novel alternative to solving LMDPs with large
state spaces. We derive a hierarchical version of the so-called Z-learning
algorithm that learns different tasks simultaneously and show empirically that
it significantly outperforms the state-of-the-art learning methods in two
classical hierarchical reinforcement learning domains: the taxi domain and an
autonomous guided vehicle task.
A Markovian-based Approach for Daily Living Activities Recognition
Authors: Zaineb Liouane, Tayeb Lemlouma, Philippe Roose, Frédéric Weis, Messaoud Hassani
Date: 2016-03-10 00:00:00

Recognizing the activities of daily living plays an important role in
healthcare. It is necessary to use an adapted model to simulate the human
behavior in a domestic space to monitor the patient harmonically and to
intervene in the necessary time. In this paper, we tackle this problem using
the hierarchical hidden Markov model for representing and recognizing complex
indoor activities. We propose a new grammar, called "Home By Room Activities
Language", to facilitate the complexity of human scenarios and consider the
abnormal activities.
Binarized Neural Networks
Authors: Itay Hubara, Daniel Soudry, Ran El Yaniv
Date: 2016-02-08 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: GPU

We introduce a method to train Binarized Neural Networks (BNNs) - neural
networks with binary weights and activations at run-time and when computing the
parameters' gradient at train-time. We conduct two sets of experiments, each
based on a different framework, namely Torch7 and Theano, where we train BNNs
on MNIST, CIFAR-10 and SVHN, and achieve nearly state-of-the-art results.
During the forward pass, BNNs drastically reduce memory size and accesses, and
replace most arithmetic operations with bit-wise operations, which might lead
to a great increase in power-efficiency. Last but not least, we wrote a binary
matrix multiplication GPU kernel with which it is possible to run our MNIST BNN
7 times faster than with an unoptimized GPU kernel, without suffering any loss
in classification accuracy. The code for training and running our BNNs is
available.
Instance-Aware Hashing for Multi-Label Image Retrieval
Authors: Hanjiang Lai, Pan Yan, Xiangbo Shu, Yunchao Wei, Shuicheng Yan
Date: 2016-03-10 00:00:00
Tasks: Image Retrieval, Multi-Label Image Retrieval, Retrieval

Similarity-preserving hashing is a commonly used method for nearest neighbour
search in large-scale image retrieval. For image retrieval, deep-networks-based
hashing methods are appealing since they can simultaneously learn effective
image representations and compact hash codes. This paper focuses on
deep-networks-based hashing for multi-label images, each of which may contain
objects of multiple categories. In most existing hashing methods, each image is
represented by one piece of hash code, which is referred to as semantic
hashing. This setting may be suboptimal for multi-label image retrieval. To
solve this problem, we propose a deep architecture that learns
\textbf{instance-aware} image representations for multi-label image data, which
are organized in multiple groups, with each group containing the features for
one category. The instance-aware representations not only bring advantages to
semantic hashing, but also can be used in category-aware hashing, in which an
image is represented by multiple pieces of hash codes and each piece of code
corresponds to a category. Extensive evaluations conducted on several benchmark
datasets demonstrate that, for both semantic hashing and category-aware
hashing, the proposed method shows substantial improvement over the
state-of-the-art supervised and unsupervised hashing methods.
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
Authors: Mansour Ahmadi, Dmitry Ulyanov, Stanislav Semenov, Mikhail Trofimov, Giorgio Giacinto
Date: 2015-11-13 00:00:00
Tasks: Computer Security, General Classification, Malware Classification

Modern malware is designed with mutation characteristics, namely polymorphism
and metamorphism, which causes an enormous growth in the number of variants of
malware samples. Categorization of malware samples on the basis of their
behaviors is essential for the computer security community, because they
receive huge number of malware everyday, and the signature extraction process
is usually based on malicious parts characterizing malware families. Microsoft
released a malware classification challenge in 2015 with a huge dataset of near
0.5 terabytes of data, containing more than 20K malware samples. The analysis
of this dataset inspired the development of a novel paradigm that is effective
in categorizing malware variants into their actual family groups. This paradigm
is presented and discussed in the present paper, where emphasis has been given
to the phases related to the extraction, and selection of a set of novel
features for the effective representation of malware samples. Features can be
grouped according to different characteristics of malware behavior, and their
fusion is performed according to a per-class weighting paradigm. The proposed
method achieved a very high accuracy ($\approx$ 0.998) on the Microsoft Malware
Challenge dataset.
Estimating Renyi Entropy of Discrete Distributions
Authors: Jayadev Acharya, Alon Orlitsky, Ananda Theertha Suresh, Himanshu Tyagi
Date: 2014-08-02 00:00:00

It was recently shown that estimating the Shannon entropy $H({\rm p})$ of a
discrete $k$-symbol distribution ${\rm p}$ requires $\Theta(k/\log k)$ samples,
a number that grows near-linearly in the support size. In many applications
$H({\rm p})$ can be replaced by the more general R\'enyi entropy of order
$\alpha$, $H_\alpha({\rm p})$. We determine the number of samples needed to
estimate $H_\alpha({\rm p})$ for all $\alpha$, showing that $\alpha < 1$
requires a super-linear, roughly $k^{1/\alpha}$ samples, noninteger $\alpha>1$
requires a near-linear $k$ samples, but, perhaps surprisingly, integer
$\alpha>1$ requires only $\Theta(k^{1-1/\alpha})$ samples. Furthermore,
developing on a recently established connection between polynomial
approximation and estimation of additive functions of the form $\sum_{x} f({\rm
p}_x)$, we reduce the sample complexity for noninteger values of $\alpha$ by a
factor of $\log k$ compared to the empirical estimator. The estimators
achieving these bounds are simple and run in time linear in the number of
samples. Our lower bounds provide explicit constructions of distributions with
different R\'enyi entropies that are hard to distinguish.
Inferring Fine-grained Details on User Activities and Home Location from Social Media: Detecting Drinking-While-Tweeting Patterns in Communities
Authors: Nabil Hossain, Tianran Hu, Roghayeh Feizi, Ann Marie White, Jiebo Luo, Henry Kautz
Date: 2016-03-10 00:00:00

Nearly all previous work on geo-locating latent states and activities from
social media confounds general discussions about activities, self-reports of
users participating in those activities at times in the past or future, and
self-reports made at the immediate time and place the activity occurs.
Activities, such as alcohol consumption, may occur at different places and
types of places, and it is important not only to detect the local regions where
these activities occur, but also to analyze the degree of participation in them
by local residents. In this paper, we develop new machine learning based
methods for fine-grained localization of activities and home locations from
Twitter data. We apply these methods to discover and compare alcohol
consumption patterns in a large urban area, New York City, and a more suburban
and rural area, Monroe County. We find positive correlations between the rate
of alcohol consumption reported among a community's Twitter users and the
density of alcohol outlets, demonstrating that the degree of correlation varies
significantly between urban and suburban areas. While our experiments are
focused on alcohol use, our methods for locating homes and distinguishing
temporally-specific self-reports are applicable to a broad range of behaviors
and latent states.
Scenario Submodular Cover
Authors: Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik, Patrick Lin
Date: 2016-03-10 00:00:00

Many problems in Machine Learning can be modeled as submodular optimization
problems. Recent work has focused on stochastic or adaptive versions of these
problems. We consider the Scenario Submodular Cover problem, which is a
counterpart to the Stochastic Submodular Cover problem studied by Golovin and
Krause. In Scenario Submodular Cover, the goal is to produce a cover with
minimum expected cost, where the expectation is with respect to an empirical
joint distribution, given as input by a weighted sample of realizations. In
contrast, in Stochastic Submodular Cover, the variables of the input
distribution are assumed to be independent, and the distribution of each
variable is given as input. Building on algorithms developed by Cicalese et al.
and Golovin and Krause for related problems, we give two approximation
algorithms for Scenario Submodular Cover over discrete distributions. The first
achieves an approximation factor of O(log Qm), where m is the size of the
sample and Q is the goal utility. The second, simpler algorithm achieves an
approximation bound of O(log QW), where Q is the goal utility and W is the sum
of the integer weights. (Both bounds assume an integer-valued utility
function.) Our results yield approximation bounds for other problems involving
non-independent distributions that are explicitly specified by their support.
Real time error detection in metal arc welding process using Artificial Neural Netwroks
Authors: Prashant Sharma, Shaju K. Albert, S. Rajeswari
Date: 2016-03-10 00:00:00
Tasks: ARC, Clustering, General Classification, Specificity, Time Series, Time Series Analysis

Quality assurance in production line demands reliable weld joints. Human made
errors is a major cause of faulty production. Promptly Identifying errors in
the weld while welding is in progress will decrease the post inspection cost
spent on the welding process. Electrical parameters generated during welding,
could able to characterize the process efficiently. Parameter values are
collected using high speed data acquisition system. Time series analysis tasks
such as filtering, pattern recognition etc. are performed over the collected
data. Filtering removes the unwanted noisy signal components and pattern
recognition task segregate error patterns in the time series based upon
similarity, which is performed by Self Organized mapping clustering algorithm.
Welder quality is thus compared by detecting and counting number of error
patterns appeared in his parametric time series. Moreover, Self Organized
mapping algorithm provides the database in which patterns are segregated into
two classes either desirable or undesirable. Database thus generated is used to
train the classification algorithms, and thereby automating the real time error
detection task. Multi Layer Perceptron and Radial basis function are the two
classification algorithms used, and their performance has been compared based
on metrics such as specificity, sensitivity, accuracy and time required in
training.
Penta and Hexa Valued Representation of Neutrosophic Information
Authors: Vasile Patrascu
Date: 2016-03-10 00:00:00
Tasks: Negation

Starting from the primary representation of neutrosophic information, namely
the degree of truth, degree of indeterminacy and degree of falsity, we define a
nuanced representation in a penta valued fuzzy space, described by the index of
truth, index of falsity, index of ignorance, index of contradiction and index
of hesitation. Also, it was constructed an associated penta valued logic and
then using this logic, it was defined for the proposed penta valued structure
the following operators: union, intersection, negation, complement and dual.
Then, the penta valued representation is extended to a hexa valued one, adding
the sixth component, namely the index of ambiguity.
Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection
Authors: Pin-Yu Chen, Sutanay Choudhury, Alfred O. Hero
Date: 2015-12-23 00:00:00
Tasks: Dictionary Learning, Intrusion Detection

Many modern datasets can be represented as graphs and hence spectral
decompositions such as graph principal component analysis (PCA) can be useful.
Distinct from previous graph decomposition approaches based on subspace
projection of a single topological feature, e.g., the Fiedler vector of
centered graph adjacency matrix (graph Laplacian), we propose spectral
decomposition approaches to graph PCA and graph dictionary learning that
integrate multiple features, including graph walk statistics, centrality
measures and graph distances to reference nodes. In this paper we propose a new
PCA method for single graph analysis, called multi-centrality graph PCA
(MC-GPCA), and a new dictionary learning method for ensembles of graphs, called
multi-centrality graph dictionary learning (MC-GDL), both based on spectral
decomposition of multi-centrality matrices. As an application to cyber
intrusion detection, MC-GPCA can be an effective indicator of anomalous
connectivity pattern and MC-GDL can provide discriminative basis for attack
classification.