pwc-archive/papers-with-abstracts huggingface dataset:
Privacy Prediction of Images Shared on Social Media Sites Using Deep Features
Authors: Ashwini Tonge, Cornelia Caragea
Date: 2015-10-29 00:00:00

Online image sharing in social media sites such as Facebook, Flickr, and
Instagram can lead to unwanted disclosure and privacy violations, when privacy
settings are used inappropriately. With the exponential increase in the number
of images that are shared online every day, the development of effective and
efficient prediction methods for image privacy settings are highly needed. The
performance of models critically depends on the choice of the feature
representation. In this paper, we present an approach to image privacy
prediction that uses deep features and deep image tags as feature
representations. Specifically, we explore deep features at various neural
network layers and use the top layer (probability) as an auto-annotation
mechanism. The results of our experiments show that models trained on the
proposed deep features and deep image tags substantially outperform baselines
such as those based on SIFT and GIST as well as those that use "bag of tags" as
features.
Sparse approximation by greedy algorithms
Authors: Vladimir Temlyakov
Date: 2015-11-05 00:00:00
Tasks: compressed sensing

It is a survey on recent results in constructive sparse approximation. Three
directions are discussed here: (1) Lebesgue-type inequalities for greedy
algorithms with respect to a special class of dictionaries, (2) constructive
sparse approximation with respect to the trigonometric system, (3) sparse
approximation with respect to dictionaries with tensor product structure. In
all three cases constructive ways are provided for sparse approximation. The
technique used is based on fundamental results from the theory of greedy
approximation. In particular, results in the direction (1) are based on deep
methods developed recently in compressed sensing. We present some of these
results with detailed proofs.
Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier
Authors: Jacob Abernethy, Elad Hazan
Date: 2015-07-09 00:00:00

This paper explores a surprising equivalence between two seemingly-distinct
convex optimization methods. We show that simulated annealing, a well-studied
random walk algorithms, is directly equivalent, in a certain sense, to the
central path interior point algorithm for the the entropic universal barrier
function. This connection exhibits several benefits. First, we are able improve
the state of the art time complexity for convex optimization under the
membership oracle model. We improve the analysis of the randomized algorithm of
Kalai and Vempala by utilizing tools developed by Nesterov and Nemirovskii that
underly the central path following interior point algorithm. We are able to
tighten the temperature schedule for simulated annealing which gives an
improved running time, reducing by square root of the dimension in certain
instances. Second, we get an efficient randomized interior point method with an
efficiently computable universal barrier for any convex set described by a
membership oracle. Previously, efficiently computable barriers were known only
for particular convex sets.
Computational Intractability of Dictionary Learning for Sparse Representation
Authors: Meisam Razaviyayn, Hung-Wei Tseng, Zhi-Quan Luo
Date: 2015-11-05 00:00:00
Tasks: Denoising, Dictionary Learning, Image Denoising

In this paper we consider the dictionary learning problem for sparse
representation. We first show that this problem is NP-hard by polynomial time
reduction of the densest cut problem. Then, using successive convex
approximation strategies, we propose efficient dictionary learning schemes to
solve several practical formulations of this problem to stationary points.
Unlike many existing algorithms in the literature, such as K-SVD, our proposed
dictionary learning scheme is theoretically guaranteed to converge to the set
of stationary points under certain mild assumptions. For the image denoising
application, the performance and the efficiency of the proposed dictionary
learning scheme are comparable to that of K-SVD algorithm in simulation.
Canonical Polyadic Decomposition with Auxiliary Information for Brain Computer Interface
Authors: Junhua Li, Chao Li, Andrzej Cichocki
Date: 2014-10-23 00:00:00
Tasks: Brain Computer Interface, Classification, EEG, Electroencephalogram (EEG), General Classification

Physiological signals are often organized in the form of multiple dimensions
(e.g., channel, time, task, and 3D voxel), so it is better to preserve original
organization structure when processing. Unlike vector-based methods that
destroy data structure, Canonical Polyadic Decomposition (CPD) aims to process
physiological signals in the form of multi-way array, which considers
relationships between dimensions and preserves structure information contained
by the physiological signal. Nowadays, CPD is utilized as an unsupervised
method for feature extraction in a classification problem. After that, a
classifier, such as support vector machine, is required to classify those
features. In this manner, classification task is achieved in two isolated
steps. We proposed supervised Canonical Polyadic Decomposition by directly
incorporating auxiliary label information during decomposition, by which a
classification task can be achieved without an extra step of classifier
training. The proposed method merges the decomposition and classifier learning
together, so it reduces procedure of classification task compared with that of
respective decomposition and classification. In order to evaluate the
performance of the proposed method, three different kinds of signals, synthetic
signal, EEG signal, and MEG signal, were used. The results based on evaluations
of synthetic and real signals demonstrated that the proposed method is
effective and efficient.
Discrete Rényi Classifiers
Authors: Meisam Razaviyayn, Farzan Farnia, David Tse
Date: 2015-11-05 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Binary Classification, feature selection, General Classification

Consider the binary classification problem of predicting a target variable
$Y$ from a discrete feature vector $X = (X_1,...,X_d)$. When the probability
distribution $\mathbb{P}(X,Y)$ is known, the optimal classifier, leading to the
minimum misclassification rate, is given by the Maximum A-posteriori
Probability decision rule. However, estimating the complete joint distribution
$\mathbb{P}(X,Y)$ is computationally and statistically impossible for large
values of $d$. An alternative approach is to first estimate some low order
marginals of $\mathbb{P}(X,Y)$ and then design the classifier based on the
estimated low order marginals. This approach is also helpful when the complete
training data instances are not available due to privacy concerns. In this
work, we consider the problem of finding the optimum classifier based on some
estimated low order marginals of $(X,Y)$. We prove that for a given set of
marginals, the minimum Hirschfeld-Gebelein-Renyi (HGR) correlation principle
introduced in [1] leads to a randomized classification rule which is shown to
have a misclassification rate no larger than twice the misclassification rate
of the optimal classifier. Then, under a separability condition, we show that
the proposed algorithm is equivalent to a randomized linear regression
approach. In addition, this method naturally results in a robust feature
selection method selecting a subset of features having the maximum worst case
HGR correlation with the target variable. Our theoretical upper-bound is
similar to the recent Discrete Chebyshev Classifier (DCC) approach [2], while
the proposed algorithm has significant computational advantages since it only
requires solving a least square optimization problem. Finally, we numerically
compare our proposed algorithm with the DCC classifier and show that the
proposed algorithm results in better misclassification rate over various
datasets.
Multi-Target Tracking and Occlusion Handling with Learned Variational Bayesian Clusters and a Social Force Model
Authors: Ata-ur-Rehman, Syed Mohsen Naqvi, Lyudmila Mihaylova, Jonathon Chambers
Date: 2015-11-05 00:00:00
Tasks: Clustering, Occlusion Handling

This paper considers the problem of multiple human target tracking in a
sequence of video data. A solution is proposed which is able to deal with the
challenges of a varying number of targets, interactions and when every target
gives rise to multiple measurements. The developed novel algorithm comprises
variational Bayesian clustering combined with a social force model, integrated
within a particle filter with an enhanced prediction step. It performs
measurement-to-target association by automatically detecting the measurement
relevance. The performance of the developed algorithm is evaluated over several
sequences from publicly available data sets: AV16.3, CAVIAR and PETS2006, which
demonstrates that the proposed algorithm successfully initializes and tracks a
variable number of targets in the presence of complex occlusions. A comparison
with state-of-the-art techniques due to Khan et al., Laet et al. and Czyz et
al. shows improved tracking performance.
Adaptive information-theoretic bounded rational decision-making with parametric priors
Authors: Jordi Grau-Moya, Daniel A. Braun
Date: 2015-11-05 00:00:00
Tasks: Decision Making

Deviations from rational decision-making due to limited computational
resources have been studied in the field of bounded rationality, originally
proposed by Herbert Simon. There have been a number of different approaches to
model bounded rationality ranging from optimality principles to heuristics.
Here we take an information-theoretic approach to bounded rationality, where
information-processing costs are measured by the relative entropy between a
posterior decision strategy and a given fixed prior strategy. In the case of
multiple environments, it can be shown that there is an optimal prior rendering
the bounded rationality problem equivalent to the rate distortion problem for
lossy compression in information theory. Accordingly, the optimal prior and
posterior strategies can be computed by the well-known Blahut-Arimoto algorithm
which requires the computation of partition sums over all possible outcomes and
cannot be applied straightforwardly to continuous problems. Here we derive a
sampling-based alternative update rule for the adaptation of prior behaviors of
decision-makers and we show convergence to the optimal prior predicted by rate
distortion theory. Importantly, the update rule avoids typical infeasible
operations such as the computation of partition sums. We show in simulations a
proof of concept for discrete action and environment domains. This approach is
not only interesting as a generic computational method, but might also provide
a more realistic model of human decision-making processes occurring on a fast
and a slow time scale.
Image classification based on support vector machine and the fusion of complementary features
Authors: Huilin Gao, Wenjie Chen, Lihua Dou
Date: 2015-11-05 00:00:00
Tasks: Classification, Clustering, General Classification, Image Categorization, image-classification, Image Classification

Image Classification based on BOW (Bag-of-words) has broad application
prospect in pattern recognition field but the shortcomings are existed because
of single feature and low classification accuracy. To this end we combine three
ingredients: (i) Three features with functions of mutual complementation are
adopted to describe the images, including PHOW (Pyramid Histogram of Words),
PHOC (Pyramid Histogram of Color) and PHOG (Pyramid Histogram of Orientated
Gradients). (ii) The improvement of traditional BOW model is presented by using
dense sample and an improved K-means clustering method for constructing the
visual dictionary. (iii) An adaptive feature-weight adjusted image
categorization algorithm based on the SVM and the fusion of multiple features
is adopted. Experiments carried out on Caltech 101 database confirm the
validity of the proposed approach. From the experimental results can be seen
that the classification accuracy rate of the proposed method is improved by
7%-17% higher than that of the traditional BOW methods. This algorithm makes
full use of global, local and spatial information and has significant
improvements to the classification accuracy.
Comparing Writing Styles using Word Embedding and Dynamic Time Warping
Authors: Abhinav Tushar, Abhinav Dahiya
Date: 2015-11-05 00:00:00
Tasks: Dynamic Time Warping

The development of plot or story in novels is reflected in the content and
the words used. The flow of sentiments, which is one aspect of writing style,
can be quantified by analyzing the flow of words. This study explores literary
works as signals in word embedding space and tries to compare writing styles of
popular classic novels using dynamic time warping.
An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding
Authors: Yiou Lin, Hang Lei, Jia Wu, Xiaoyu Li
Date: 2015-11-05 00:00:00
Proceeding: PACLIC 2015 10
Tasks: BIG-bench Machine Learning, General Classification, Sentiment Analysis, Sentiment Classification, Word Embeddings

In this article, how word embeddings can be used as features in Chinese
sentiment classification is presented. Firstly, a Chinese opinion corpus is
built with a million comments from hotel review websites. Then the word
embeddings which represent each comment are used as input in different machine
learning methods for sentiment classification, including SVM, Logistic
Regression, Convolutional Neural Network (CNN) and ensemble methods. These
methods get better performance compared with N-gram models using Naive Bayes
(NB) and Maximum Entropy (ME). Finally, a combination of machine learning
methods is proposed which presents an outstanding performance in precision,
recall and F1 score. After selecting the most useful methods to construct the
combinational model and testing over the corpus, the final F1 score is 0.920.
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
Authors: Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan
Date: 2015-11-05 00:00:00

We aim to produce predictive models that are not only accurate, but are also
interpretable to human experts. Our models are decision lists, which consist of
a series of if...then... statements (e.g., if high blood pressure, then stroke)
that discretize a high-dimensional, multivariate feature space into a series of
simple, readily interpretable decision statements. We introduce a generative
model called Bayesian Rule Lists that yields a posterior distribution over
possible decision lists. It employs a novel prior structure to encourage
sparsity. Our experiments show that Bayesian Rule Lists has predictive accuracy
on par with the current top algorithms for prediction in machine learning. Our
method is motivated by recent developments in personalized medicine, and can be
used to produce highly accurate and interpretable medical scoring systems. We
demonstrate this by producing an alternative to the CHADS$_2$ score, actively
used in clinical practice for estimating the risk of stroke in patients that
have atrial fibrillation. Our model is as interpretable as CHADS$_2$, but more
accurate.
Computing sets of graded attribute implications with witnessed non-redundancy
Authors: Vilem Vychodil
Date: 2015-11-05 00:00:00
Tasks: Attribute

In this paper we extend our previous results on sets of graded attribute
implications with witnessed non-redundancy. We assume finite residuated
lattices as structures of truth degrees and use arbitrary idempotent
truth-stressing linguistic hedges as parameters which influence the semantics
of graded attribute implications. In this setting, we introduce algorithm which
transforms any set of graded attribute implications into an equivalent
non-redundant set of graded attribute implications with saturated consequents
whose non-redundancy is witnessed by antecedents of the formulas. As a
consequence, we solve the open problem regarding the existence of general
systems of pseudo-intents which appear in formal concept analysis of
object-attribute data with graded attributes and linguistic hedges.
Furthermore, we show a polynomial-time procedure for determining bases given by
general systems of pseudo-intents from sets of graded attribute implications
which are complete in data.
Point Localization and Density Estimation from Ordinal kNN graphs using Synchronization
Authors: Mihai Cucuringu, Joseph Woodworth
Date: 2015-04-03 00:00:00
Tasks: Density Estimation

We consider the problem of embedding unweighted, directed k-nearest neighbor
graphs in low-dimensional Euclidean space. The k-nearest neighbors of each
vertex provides ordinal information on the distances between points, but not
the distances themselves. We use this ordinal information along with the
low-dimensionality to recover the coordinates of the points up to arbitrary
similarity transformations (rigid transformations and scaling). Furthermore, we
also illustrate the possibility of robustly recovering the underlying density
via the Total Variation Maximum Penalized Likelihood Estimation (TV-MPLE)
method. We make existing approaches scalable by using an instance of a
local-to-global algorithm based on group synchronization, recently proposed in
the literature in the context of sensor network localization and structural
biology, which we augment with a scaling synchronization step. We demonstrate
the scalability of our approach on large graphs, and show how it compares to
the Local Ordinal Embedding (LOE) algorithm, which was recently proposed for
recovering the configuration of a cloud of points from pairwise ordinal
comparisons between a sparse set of distances.
Attention with Intention for a Neural Network Conversation Model
Authors: Kaisheng Yao, Geoffrey Zweig, Baolin Peng
Date: 2015-10-29 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling

In a conversation or a dialogue process, attention and intention play
intrinsic roles. This paper proposes a neural network based approach that
models the attention and intention processes. It essentially consists of three
recurrent networks. The encoder network is a word-level model representing
source side sentences. The intention network is a recurrent network that models
the dynamics of the intention process. The decoder network is a recurrent
network produces responses to the input from the source side. It is a language
model that is dependent on the intention and has an attention mechanism to
attend to particular source side words, when predicting a symbol in the
response. The model is trained end-to-end without labeling data. Experiments
show that this model generates natural responses to user inputs.
Background Modeling Using Adaptive Pixelwise Kernel Variances in a Hybrid Feature Space
Authors: Manjunath Narayana, Allen Hanson, Erik Learned-Miller
Date: 2015-11-05 00:00:00

Recent work on background subtraction has shown developments on two major
fronts. In one, there has been increasing sophistication of probabilistic
models, from mixtures of Gaussians at each pixel [7], to kernel density
estimates at each pixel [1], and more recently to joint domainrange density
estimates that incorporate spatial information [6]. Another line of work has
shown the benefits of increasingly complex feature representations, including
the use of texture information, local binary patterns, and recently
scale-invariant local ternary patterns [4]. In this work, we use joint
domain-range based estimates for background and foreground scores and show that
dynamically choosing kernel variances in our kernel estimates at each
individual pixel can significantly improve results. We give a heuristic method
for selectively applying the adaptive kernel calculations which is nearly as
accurate as the full procedure but runs much faster. We combine these modeling
improvements with recently developed complex features [4] and show significant
improvements on a standard backgrounding benchmark.
Background subtraction - separating the modeling and the inference
Authors: Manjunath Narayana, Allen Hanson, Erik Learned-Miller
Date: 2015-11-05 00:00:00

In its early implementations, background modeling was a process of building a
model for the background of a video with a stationary camera, and identifying
pixels that did not conform well to this model. The pixels that were not
well-described by the background model were assumed to be moving objects. Many
systems today maintain models for the foreground as well as the background, and
these models compete to explain the pixels in a video. In this paper, we argue
that the logical endpoint of this evolution is to simply use Bayes' rule to
classify pixels. In particular, it is essential to have a background
likelihood, a foreground likelihood, and a prior at each pixel. A simple
application of Bayes' rule then gives a posterior probability over the label.
The only remaining question is the quality of the component models: the
background likelihood, the foreground likelihood, and the prior. We describe a
model for the likelihoods that is built by using not only the past observations
at a given pixel location, but by also including observations in a spatial
neighborhood around the location. This enables us to model the influence
between neighboring pixels and is an improvement over earlier pixelwise models
that do not allow for such influence. Although similar in spirit to the joint
domain-range model, we show that our model overcomes certain deficiencies in
that model. We use a spatially dependent prior for the background and
foreground. The background and foreground labels from the previous frame, after
spatial smoothing to account for movement of objects,are used to build the
prior for the current frame.
Douglas-Rachford splitting for nonconvex optimization with application to nonconvex feasibility problems
Authors: Guoyin Li, Ting Kei Pong
Date: 2014-09-30 00:00:00

We adapt the Douglas-Rachford (DR) splitting method to solve nonconvex
feasibility problems by studying this method for a class of nonconvex
optimization problem. While the convergence properties of the method for convex
problems have been well studied, far less is known in the nonconvex setting. In
this paper, for the direct adaptation of the method to minimize the sum of a
proper closed function $g$ and a smooth function $f$ with a Lipschitz
continuous gradient, we show that if the step-size parameter is smaller than a
computable threshold and the sequence generated has a cluster point, then it
gives a stationary point of the optimization problem. Convergence of the whole
sequence and a local convergence rate are also established under the additional
assumption that $f$ and $g$ are semi-algebraic. We also give simple sufficient
conditions guaranteeing the boundedness of the sequence generated. We then
apply our nonconvex DR splitting method to finding a point in the intersection
of a closed convex set $C$ and a general closed set $D$ by minimizing the
squared distance to $C$ subject to $D$. We show that if either set is bounded
and the step-size parameter is smaller than a computable threshold, then the
sequence generated from the DR splitting method is actually bounded.
Consequently, the sequence generated will have cluster points that are
stationary for an optimization problem, and the whole sequence is convergent
under an additional assumption that $C$ and $D$ are semi-algebraic. We achieve
these results based on a new merit function constructed particularly for the DR
splitting method. Our preliminary numerical results indicate that our DR
splitting method usually outperforms the alternating projection method in
finding a sparse solution of a linear system, in terms of both the solution
quality and the number of iterations taken.