pwc-archive/papers-with-abstracts huggingface dataset:
Sequential Relevance Maximization with Binary Feedback
Authors: Vijay Kamble, Nadia Fawaz, Fernando Silveira
Date: 2015-03-06 00:00:00
Tasks: Collaborative Filtering

Motivated by online settings where users can provide explicit feedback about
the relevance of products that are sequentially presented to them, we look at
the recommendation process as a problem of dynamically optimizing this
relevance feedback. Such an algorithm optimizes the fine tradeoff between
presenting the products that are most likely to be relevant, and learning the
preferences of the user so that more relevant recommendations can be made in
the future.
  We assume a standard predictive model inspired by collaborative filtering, in
which a user is sampled from a distribution over a set of possible types. For
every product category, each type has an associated relevance feedback that is
assumed to be binary: the category is either relevant or irrelevant. Assuming
that the user stays for each additional recommendation opportunity with
probability $\beta$ independent of the past, the problem is to find a policy
that maximizes the expected number of recommendations that are deemed relevant
in a session.
  We analyze this problem and prove key structural properties of the optimal
policy. Based on these properties, we first present an algorithm that strikes a
balance between recursion and dynamic programming to compute this policy. We
further propose and analyze two heuristic policies: a `farsighted' greedy
policy that attains at least $1-\beta$ factor of the optimal payoff, and a
naive greedy policy that attains at least $\frac{1-\beta}{1+\beta}$ factor of
the optimal payoff in the worst case. Extensive simulations show that these
heuristics are very close to optimal in practice.
Partial light field tomographic reconstruction from a fixed-camera focal stack
Authors: A. Mousnier, E. Vural, C. Guillemot
Date: 2015-03-06 00:00:00
Tasks: Position

This paper describes a novel approach to partially reconstruct
high-resolution 4D light fields from a stack of differently focused photographs
taken with a fixed camera. First, a focus map is calculated from this stack
using a simple approach combining gradient detection and region expansion with
graph-cut. Then, this focus map is converted into a depth map thanks to the
calibration of the camera. We proceed after this with the tomographic
reconstruction of the epipolar images by back-projecting the focused regions of
the scene only. We call it masked back-projection. The angles of
back-projection are calculated from the depth map. Thanks to the high angular
resolution we achieve by suitably exploiting the image content captured over a
large interval of focus distances, we are able to render puzzling perspective
shifts although the original photographs were taken from a single fixed camera
at a fixed position.
A Bayesian Model of node interaction in networks
Authors: Ingmar Schuster
Date: 2014-02-18 00:00:00
Tasks: BIG-bench Machine Learning, model

We are concerned with modeling the strength of links in networks by taking
into account how often those links are used. Link usage is a strong indicator
of how closely two nodes are related, but existing network models in Bayesian
Statistics and Machine Learning are able to predict only wether a link exists
at all. As priors for latent attributes of network nodes we explore the Chinese
Restaurant Process (CRP) and a multivariate Gaussian with fixed dimensionality.
The model is applied to a social network dataset and a word coocurrence
dataset.
Ranking and significance of variable-length similarity-based time series motifs
Authors: Joan Serrà, Isabel Serra, Álvaro Corral, Josep Lluis Arcos
Date: 2015-03-06 00:00:00
Tasks: Time Series, Time Series Analysis

The detection of very similar patterns in a time series, commonly called
motifs, has received continuous and increasing attention from diverse
scientific communities. In particular, recent approaches for discovering
similar motifs of different lengths have been proposed. In this work, we show
that such variable-length similarity-based motifs cannot be directly compared,
and hence ranked, by their normalized dissimilarities. Specifically, we find
that length-normalized motif dissimilarities still have intrinsic dependencies
on the motif length, and that lowest dissimilarities are particularly affected
by this dependency. Moreover, we find that such dependencies are generally
non-linear and change with the considered data set and dissimilarity measure.
Based on these findings, we propose a solution to rank those motifs and measure
their significance. This solution relies on a compact but accurate model of the
dissimilarity space, using a beta distribution with three parameters that
depend on the motif length in a non-linear way. We believe the incomparability
of variable-length dissimilarities could go beyond the field of time series,
and that similar modeling strategies as the one used here could be of help in a
more broad context.
Estimation of the parameters of an infectious disease model using neural networks
Authors: V. Sree Hari Rao, M. Naresh Kumar
Date: 2015-03-06 00:00:00

In this paper, we propose a realistic mathematical model taking into account
the mutual interference among the interacting populations. This model attempts
to describe the control (vaccination) function as a function of the number of
infective individuals, which is an improvement over the existing susceptible
infective epidemic models. Regarding the growth of the epidemic as a nonlinear
phenomenon we have developed a neural network architecture to estimate the
vital parameters associated with this model. This architecture is based on a
recently developed new class of neural networks known as co-operative and
supportive neural networks. The application of this architecture to the present
study involves preprocessing of the input data, and this renders an efficient
estimation of the rate of spread of the epidemic. It is observed that the
proposed new neural network outperforms a simple feed-forward neural network
and polynomial regression.
An Incidence Geometry approach to Dictionary Learning
Authors: Meera Sitharam, Mohamad Tarifi, Menghan Wang
Date: 2014-02-28 00:00:00
Tasks: Dictionary Learning

We study the Dictionary Learning (aka Sparse Coding) problem of obtaining a
sparse representation of data points, by learning \emph{dictionary vectors}
upon which the data points can be written as sparse linear combinations. We
view this problem from a geometry perspective as the spanning set of a subspace
arrangement, and focus on understanding the case when the underlying hypergraph
of the subspace arrangement is specified. For this Fitted Dictionary Learning
problem, we completely characterize the combinatorics of the associated
subspace arrangements (i.e.\ their underlying hypergraphs). Specifically, a
combinatorial rigidity-type theorem is proven for a type of geometric incidence
system. The theorem characterizes the hypergraphs of subspace arrangements that
generically yield (a) at least one dictionary (b) a locally unique dictionary
(i.e.\ at most a finite number of isolated dictionaries) of the specified size.
We are unaware of prior application of combinatorial rigidity techniques in the
setting of Dictionary Learning, or even in machine learning. We also provide a
systematic classification of problems related to Dictionary Learning together
with various algorithms, their assumptions and performance.
Deep Clustered Convolutional Kernels
Authors: Minyoung Kim, Luca Rigazio
Date: 2015-03-06 00:00:00
Tasks: Clustering, parameter estimation

Deep neural networks have recently achieved state of the art performance
thanks to new training algorithms for rapid parameter estimation and new
regularization methods to reduce overfitting. However, in practice the network
architecture has to be manually set by domain experts, generally by a costly
trial and error procedure, which often accounts for a large portion of the
final system performance. We view this as a limitation and propose a novel
training algorithm that automatically optimizes network architecture, by
progressively increasing model complexity and then eliminating model redundancy
by selectively removing parameters at training time. For convolutional neural
networks, our method relies on iterative split/merge clustering of
convolutional kernels interleaved by stochastic gradient descent. We present a
training algorithm and experimental results on three different vision tasks,
showing improved performance compared to similarly sized hand-crafted
architectures.
Latent Hierarchical Model for Activity Recognition
Authors: Ninghang Hu, Gwenn Englebienne, Zhongyu Lou, Ben Kröse
Date: 2015-03-06 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, model

We present a novel hierarchical model for human activity recognition. In
contrast to approaches that successively recognize actions and activities, our
approach jointly models actions and activities in a unified framework, and
their labels are simultaneously predicted. The model is embedded with a latent
layer that is able to capture a richer class of contextual information in both
state-state and observation-state pairs. Although loops are present in the
model, the model has an overall linear-chain structure, where the exact
inference is tractable. Therefore, the model is very efficient in both
inference and learning. The parameters of the graphical model are learned with
a Structured Support Vector Machine (Structured-SVM). A data-driven approach is
used to initialize the latent variables; therefore, no manual labeling for the
latent states is required. The experimental results from using two benchmark
datasets show that our model outperforms the state-of-the-art approach, and our
model is computationally more efficient.
A General Hybrid Clustering Technique
Authors: Saeid Amiri, Bertrand Clarke, Jennifer Clarke, Hoyt A. Koepke
Date: 2015-03-04 00:00:00
Tasks: Clustering

Here, we propose a clustering technique for general clustering problems
including those that have non-convex clusters. For a given desired number of
clusters $K$, we use three stages to find a clustering. The first stage uses a
hybrid clustering technique to produce a series of clusterings of various sizes
(randomly selected). They key steps are to find a $K$-means clustering using
$K_\ell$ clusters where $K_\ell \gg K$ and then joins these small clusters by
using single linkage clustering. The second stage stabilizes the result of
stage one by reclustering via the `membership matrix' under Hamming distance to
generate a dendrogram. The third stage is to cut the dendrogram to get $K^*$
clusters where $K^* \geq K$ and then prune back to $K$ to give a final
clustering. A variant on our technique also gives a reasonable estimate for
$K_T$, the true number of clusters.
  We provide a series of arguments to justify the steps in the stages of our
methods and we provide numerous examples involving real and simulated data to
compare our technique with other related techniques.
Frequency Domain TOF: Encoding Object Depth in Modulation Frequency
Authors: Achuta Kadambi, Vage Taamazyan, Suren Jayasuriya, Ramesh Raskar
Date: 2015-03-05 00:00:00
Tasks: Object

Time of flight cameras may emerge as the 3-D sensor of choice. Today, time of
flight sensors use phase-based sampling, where the phase delay between emitted
and received, high-frequency signals encodes distance. In this paper, we
present a new time of flight architecture that relies only on frequency---we
refer to this technique as frequency-domain time of flight (FD-TOF). Inspired
by optical coherence tomography (OCT), FD-TOF excels when frequency bandwidth
is high. With the increasing frequency of TOF sensors, new challenges to time
of flight sensing continue to emerge. At high frequencies, FD-TOF offers
several potential benefits over phase-based time of flight methods.
Efficient Estimation of Mutual Information for Strongly Dependent Variables
Authors: Shuyang Gao, Greg Ver Steeg, Aram Galstyan
Date: 2014-11-07 00:00:00

We demonstrate that a popular class of nonparametric mutual information (MI)
estimators based on k-nearest-neighbor graphs requires number of samples that
scales exponentially with the true MI. Consequently, accurate estimation of MI
between two strongly dependent variables is possible only for prohibitively
large sample size. This important yet overlooked shortcoming of the existing
estimators is due to their implicit reliance on local uniformity of the
underlying joint distribution. We introduce a new estimator that is robust to
local non-uniformity, works well with limited data, and is able to capture
relationship strengths over many orders of magnitude. We demonstrate the
superior performance of the proposed estimator on both synthetic and real-world
data.
Learning Stochastic Recurrent Networks
Authors: Justin Bayer, Christian Osendorfer
Date: 2014-11-27 00:00:00
Tasks: Variational Inference

Leveraging advances in variational inference, we propose to enhance recurrent
neural networks with latent variables, resulting in Stochastic Recurrent
Networks (STORNs). The model i) can be trained with stochastic gradient
methods, ii) allows structured and multi-modal conditionals at each time step,
iii) features a reliable estimator of the marginal likelihood and iv) is a
generalisation of deterministic recurrent neural networks. We evaluate the
method on four polyphonic musical data sets and motion capture data.
Correct-by-synthesis reinforcement learning with temporal logic constraints
Authors: Min Wen, Ruediger Ehlers, Ufuk Topcu
Date: 2015-03-05 00:00:00
Tasks: Motion Planning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider a problem on the synthesis of reactive controllers that optimize
some a priori unknown performance criterion while interacting with an
uncontrolled environment such that the system satisfies a given temporal logic
specification. We decouple the problem into two subproblems. First, we extract
a (maximally) permissive strategy for the system, which encodes multiple
(possibly all) ways in which the system can react to the adversarial
environment and satisfy the specifications. Then, we quantify the a priori
unknown performance criterion as a (still unknown) reward function and compute
an optimal strategy for the system within the operating envelope allowed by the
permissive strategy by using the so-called maximin-Q learning algorithm. We
establish both correctness (with respect to the temporal logic specifications)
and optimality (with respect to the a priori unknown performance criterion) of
this two-step technique for a fragment of temporal logic specifications. For
specifications beyond this fragment, correctness can still be preserved, but
the learned strategy may be sub-optimal. We present an algorithm to the overall
problem, and demonstrate its use and computational requirements on a set of
robot motion planning examples.
Min-Max Kernels
Authors: Ping Li
Date: 2015-03-05 00:00:00
Tasks: General Classification

The min-max kernel is a generalization of the popular resemblance kernel
(which is designed for binary data). In this paper, we demonstrate, through an
extensive classification study using kernel machines, that the min-max kernel
often provides an effective measure of similarity for nonnegative data. As the
min-max kernel is nonlinear and might be difficult to be used for industrial
applications with massive data, we show that the min-max kernel can be
linearized via hashing techniques. This allows practitioners to apply min-max
kernel to large-scale applications using well matured linear algorithms such as
linear SVM or logistic regression.
  The previous remarkable work on consistent weighted sampling (CWS) produces
samples in the form of ($i^*, t^*$) where the $i^*$ records the location (and
in fact also the weights) information analogous to the samples produced by
classical minwise hashing on binary data. Because the $t^*$ is theoretically
unbounded, it was not immediately clear how to effectively implement CWS for
building large-scale linear classifiers. In this paper, we provide a simple
solution by discarding $t^*$ (which we refer to as the "0-bit" scheme). Via an
extensive empirical study, we show that this 0-bit scheme does not lose
essential information. We then apply the "0-bit" CWS for building linear
classifiers to approximate min-max kernel classifiers, as extensively validated
on a wide range of publicly available classification datasets. We expect this
work will generate interests among data mining practitioners who would like to
efficiently utilize the nonlinear information of non-binary and nonnegative
data.
Error-Correcting Factorization
Authors: Miguel Angel Bautista, Oriol Pujol, Fernando de la Torre, Sergio Escalera
Date: 2015-02-27 00:00:00
Tasks: Multi-class Classification

Error Correcting Output Codes (ECOC) is a successful technique in multi-class
classification, which is a core problem in Pattern Recognition and Machine
Learning. A major advantage of ECOC over other methods is that the multi- class
problem is decoupled into a set of binary problems that are solved
independently. However, literature defines a general error-correcting
capability for ECOCs without analyzing how it distributes among classes,
hindering a deeper analysis of pair-wise error-correction. To address these
limitations this paper proposes an Error-Correcting Factorization (ECF) method,
our contribution is three fold: (I) We propose a novel representation of the
error-correction capability, called the design matrix, that enables us to build
an ECOC on the basis of allocating correction to pairs of classes. (II) We
derive the optimal code length of an ECOC using rank properties of the design
matrix. (III) ECF is formulated as a discrete optimization problem, and a
relaxed solution is found using an efficient constrained block coordinate
descent approach. (IV) Enabled by the flexibility introduced with the design
matrix we propose to allocate the error-correction on classes that are prone to
confusion. Experimental results in several databases show that when allocating
the error-correction to confusable classes ECF outperforms state-of-the-art
approaches.
Color Image Classification via Quaternion Principal Component Analysis Network
Authors: Rui Zeng, Jiasong Wu, Zhuhong Shao, Yang Chen, Lotfi Senhadji, Huazhong Shu
Date: 2015-03-05 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

The Principal Component Analysis Network (PCANet), which is one of the
recently proposed deep learning architectures, achieves the state-of-the-art
classification accuracy in various databases. However, the performance of
PCANet may be degraded when dealing with color images. In this paper, a
Quaternion Principal Component Analysis Network (QPCANet), which is an
extension of PCANet, is proposed for color images classification. Compared to
PCANet, the proposed QPCANet takes into account the spatial distribution
information of color images and ensures larger amount of intra-class invariance
of color images. Experiments conducted on different color image datasets such
as Caltech-101, UC Merced Land Use, Georgia Tech face and CURet have revealed
that the proposed QPCANet achieves higher classification accuracy than PCANet.
Video-Based Facial Expression Recognition Using Local Directional Binary Pattern
Authors: Sahar Hooshmand, Ali Jamali Avilaq, Amir Hossein Rezaie
Date: 2015-03-05 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Automatic facial expression analysis is a challenging issue and influenced so
many areas such as human computer interaction. Due to the uncertainties of the
light intensity and light direction, the face gray shades are uneven and the
expression recognition rate under simple Local Binary Pattern is not ideal and
promising. In this paper we propose two state-of-the-art descriptors for
person-independent facial expression recognition. First the face regions of the
whole images in a video sequence are modeled with Volume Local Directional
Binary pattern (VLDBP), which is an extended version of the LDBP operator,
incorporating movement and appearance together. To make the survey
computationally simple and easy to expand, only the co-occurrences of the Local
Directional Binary Pattern on three orthogonal planes (LDBP-TOP) are debated.
After extracting the feature vectors the K-Nearest Neighbor classifier was used
to recognize the expressions. The proposed methods are applied to the videos of
the Extended Cohn-Kanade database (CK+) and the experimental outcomes
demonstrate that the offered techniques achieve more accuracy in comparison
with the classic and traditional algorithms.
Towards an intelligent VNS heuristic for the k-labelled spanning forest problem
Authors: Sergio Consoli, Josè Andrès Moreno Pèrez, Nenad Mladenovic
Date: 2015-03-05 00:00:00

In a currently ongoing project, we investigate a new possibility for solving
the k-labelled spanning forest (kLSF) problem by an intelligent Variable
Neighbourhood Search (Int-VNS) metaheuristic. In the kLSF problem we are given
an undirected input graph G and an integer positive value k, and the aim is to
find a spanning forest of G having the minimum number of connected components
and the upper bound k on the number of labels to use. The problem is related to
the minimum labelling spanning tree (MLST) problem, whose goal is to get the
spanning tree of the input graph with the minimum number of labels, and has
several applications in the real world, where one aims to ensure connectivity
by means of homogeneous connections. The Int-VNS metaheuristic that we propose
for the kLSF problem is derived from the promising intelligent VNS strategy
recently proposed for the MLST problem, and integrates the basic VNS for the
kLSF problem with other complementary approaches from machine learning,
statistics and experimental algorithmics, in order to produce high-quality
performance and to completely automate the resulting strategy.
External Validity: From Do-Calculus to Transportability Across Populations
Authors: Judea Pearl, Elias Bareinboim
Date: 2015-03-05 00:00:00

The generalizability of empirical findings to new environments, settings or
populations, often called "external validity," is essential in most scientific
explorations. This paper treats a particular problem of generalizability,
called "transportability," defined as a license to transfer causal effects
learned in experimental studies to a new population, in which only
observational studies can be conducted. We introduce a formal representation
called "selection diagrams" for expressing knowledge about differences and
commonalities between populations of interest and, using this representation,
we reduce questions of transportability to symbolic derivations in the
do-calculus. This reduction yields graph-based procedures for deciding, prior
to observing any data, whether causal effects in the target population can be
inferred from experimental findings in the study population. When the answer is
affirmative, the procedures identify what experimental and observational
findings need be obtained from the two populations, and how they can be
combined to ensure bias-free transport.