pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Boundary Matching Algorithm for Video Temporal Error Concealment
Authors: Seyed Mojtaba Marvasti-Zadeh, Hossein Ghanei-Yakhdan, Shohreh Kasaei
Date: 2016-10-25 00:00:00
Tasks: Decoder

With the fast growth of communication networks, the video data transmission
from these networks is extremely vulnerable. Error concealment is a technique
to estimate the damaged data by employing the correctly received data at the
decoder. In this paper, an efficient boundary matching algorithm for estimating
damaged motion vectors (MVs) is proposed. The proposed algorithm performs error
concealment for each damaged macro block (MB) according to the list of
identified priority of each frame. It then uses a classic boundary matching
criterion or the proposed boundary matching criterion adaptively to identify
matching distortion in each boundary of candidate MB. Finally, the candidate MV
with minimum distortion is selected as an MV of damaged MB and the list of
priorities is updated. Experimental results show that the proposed algorithm
improves both objective and subjective qualities of reconstructed frames
without any significant increase in computational cost. The PSNR for test
sequences in some frames is increased about 4.7, 4.5, and 4.4 dB compared to
the classic boundary matching, directional boundary matching, and directional
temporal boundary matching algorithm, respectively.
Big Models for Big Data using Multi objective averaged one dependence estimators
Authors: Mrutyunjaya Panda
Date: 2016-10-25 00:00:00
Tasks: feature selection, General Classification, Multi-class Classification

Even though, many researchers tried to explore the various possibilities on
multi objective feature selection, still it is yet to be explored with best of
its capabilities in data mining applications rather than going for developing
new ones. In this paper, multi-objective evolutionary algorithm ENORA is used
to select the features in a multi-class classification problem. The fusion of
AnDE (averaged n-dependence estimators) with n=1, a variant of naive Bayes with
efficient feature selection by ENORA is performed in order to obtain a fast
hybrid classifier which can effectively learn from big data. This method aims
at solving the problem of finding optimal feature subset from full data which
at present still remains to be a difficult problem. The efficacy of the
obtained classifier is extensively evaluated with a range of most popular 21
real world dataset, ranging from small to big. The results obtained are
encouraging in terms of time, Root mean square error, zero-one loss and
classification accuracy.
Quadripolar Relational Model: a framework for the description of borderline and narcissistic personality disorders
Authors: Alessandro Fontana
Date: 2015-12-18 00:00:00

Borderline personality disorder and narcissistic personality disorder are
important nosographic entities and have been subject of intensive
investigations. The currently prevailing psychodynamic theory for mental
disorders is based on the repertoire of defense mechanisms employed. Another
line of research is concerned with the study of psychological traumas and
dissociation as a defensive response. Both theories can be used to shed light
on some aspects of pathological mental functioning, and have many points of
contact. This work merges these two psychological theories, and builds a model
of mental function in a relational context called Quadripolar Relational Model.
The model, which is enriched with ideas borrowed from the field of computer
science, leads to a new therapeutic proposal for psychological traumas and
personality disorders.
Approximate cross-validation formula for Bayesian linear regression
Authors: Yoshiyuki Kabashima, Tomoyuki Obuchi, Makoto Uemura
Date: 2016-10-25 00:00:00
Tasks: regression

Cross-validation (CV) is a technique for evaluating the ability of
statistical models/learning systems based on a given data set. Despite its wide
applicability, the rather heavy computational cost can prevent its use as the
system size grows. To resolve this difficulty in the case of Bayesian linear
regression, we develop a formula for evaluating the leave-one-out CV error
approximately without actually performing CV. The usefulness of the developed
formula is tested by statistical mechanical analysis for a synthetic model.
This is confirmed by application to a real-world supernova data set as well.
Camera Fingerprint: A New Perspective for Identifying User's Identity
Authors: Xiang Jiang, Shikui Wei, Ruizhen Zhao, Yao Zhao, Xindong Wu
Date: 2016-10-25 00:00:00
Tasks: Product Recommendation, User Identification

Identifying user's identity is a key problem in many data mining
applications, such as product recommendation, customized content delivery and
criminal identification. Given a set of accounts from the same or different
social network platforms, user identification attempts to identify all accounts
belonging to the same person. A commonly used solution is to build the
relationship among different accounts by exploring their collective patterns,
e.g., user profile, writing style, similar comments. However, this kind of
method doesn't work well in many practical scenarios, since the information
posted explicitly by users may be false due to various reasons. In this paper,
we re-inspect the user identification problem from a novel perspective, i.e.,
identifying user's identity by matching his/her cameras. The underlying
assumption is that multiple accounts belonging to the same person contain the
same or similar camera fingerprint information. The proposed framework, called
User Camera Identification (UCI), is based on camera fingerprints, which takes
fully into account the problems of multiple cameras and reposting behaviors.
Sparse Hierarchical Tucker Factorization and its Application to Healthcare
Authors: Ioakeim Perros, Robert Chen, Richard Vuduc, Jimeng Sun
Date: 2016-10-25 00:00:00

We propose a new tensor factorization method, called the Sparse
Hierarchical-Tucker (Sparse H-Tucker), for sparse and high-order data tensors.
Sparse H-Tucker is inspired by its namesake, the classical Hierarchical Tucker
method, which aims to compute a tree-structured factorization of an input data
set that may be readily interpreted by a domain expert. However, Sparse
H-Tucker uses a nested sampling technique to overcome a key scalability problem
in Hierarchical Tucker, which is the creation of an unwieldy intermediate dense
core tensor; the result of our approach is a faster, more space-efficient, and
more accurate method. We extensively test our method on a real healthcare
dataset, which is collected from 30K patients and results in an 18th order
sparse data tensor. Unlike competing methods, Sparse H-Tucker can analyze the
full data set on a single multi-threaded machine. It can also do so more
accurately and in less time than the state-of-the-art: on a 12th order subset
of the input data, Sparse H-Tucker is 18x more accurate and 7.5x faster than a
previously state-of-the-art method. Even for analyzing low order tensors (e.g.,
4-order), our method requires close to an order of magnitude less time and over
two orders of magnitude less memory, as compared to traditional tensor
factorization methods such as CP and Tucker. Moreover, we observe that Sparse
H-Tucker scales nearly linearly in the number of non-zero tensor elements. The
resulting model also provides an interpretable disease hierarchy, which is
confirmed by a clinical expert.
FPNN: Field Probing Neural Networks for 3D Data
Authors: Yangyan Li, Soeren Pirk, Hao Su, Charles R. Qi, Leonidas J. Guibas
Date: 2016-05-20 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: 3D geometry, 3D Object Recognition, Object Recognition

Building discriminative representations for 3D data has been an important
task in computer graphics and computer vision research. Convolutional Neural
Networks (CNNs) have shown to operate on 2D images with great success for a
variety of tasks. Lifting convolution operators to 3D (3DCNNs) seems like a
plausible and promising next step. Unfortunately, the computational complexity
of 3D CNNs grows cubically with respect to voxel resolution. Moreover, since
most 3D geometry representations are boundary based, occupied regions do not
increase proportionately with the size of the discretization, resulting in
wasted computation. In this work, we represent 3D spaces as volumetric fields,
and propose a novel design that employs field probing filters to efficiently
extract features from them. Each field probing filter is a set of probing
points --- sensors that perceive the space. Our learning algorithm optimizes
not only the weights associated with the probing points, but also their
locations, which deforms the shape of the probing filters and adaptively
distributes them in 3D space. The optimized probing points sense the 3D space
"intelligently", rather than operating blindly over the entire domain. We show
that field probing is significantly more efficient than 3DCNNs, while providing
state-of-the-art performance, on classification tasks for 3D object recognition
benchmark datasets.
Characteristic Kernels and Infinitely Divisible Distributions
Authors: Yu Nishiyama, Kenji Fukumizu
Date: 2014-03-28 00:00:00

We connect shift-invariant characteristic kernels to infinitely divisible
distributions on $\mathbb{R}^{d}$. Characteristic kernels play an important
role in machine learning applications with their kernel means to distinguish
any two probability measures. The contribution of this paper is two-fold.
First, we show, using the L\'evy-Khintchine formula, that any shift-invariant
kernel given by a bounded, continuous and symmetric probability density
function (pdf) of an infinitely divisible distribution on $\mathbb{R}^d$ is
characteristic. We also present some closure property of such characteristic
kernels under addition, pointwise product, and convolution. Second, in
developing various kernel mean algorithms, it is fundamental to compute the
following values: (i) kernel mean values $m_P(x)$, $x \in \mathcal{X}$, and
(ii) kernel mean RKHS inner products ${\left\langle m_P, m_Q
\right\rangle_{\mathcal{H}}}$, for probability measures $P, Q$. If $P, Q$, and
kernel $k$ are Gaussians, then computation (i) and (ii) results in Gaussian
pdfs that is tractable. We generalize this Gaussian combination to more general
cases in the class of infinitely divisible distributions. We then introduce a
{\it conjugate} kernel and {\it convolution trick}, so that the above (i) and
(ii) have the same pdf form, expecting tractable computation at least in some
cases. As specific instances, we explore $\alpha$-stable distributions and a
rich class of generalized hyperbolic distributions, where the Laplace, Cauchy
and Student-t distributions are included.
EmojiNet: Building a Machine Readable Sense Inventory for Emoji
Authors: Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran
Date: 2016-10-25 00:00:00
Tasks: Word Sense Disambiguation

Emoji are a contemporary and extremely popular way to enhance electronic
communication. Without rigid semantics attached to them, emoji symbols take on
different meanings based on the context of a message. Thus, like the word sense
disambiguation task in natural language processing, machines also need to
disambiguate the meaning or sense of an emoji. In a first step toward achieving
this goal, this paper presents EmojiNet, the first machine readable sense
inventory for emoji. EmojiNet is a resource enabling systems to link emoji with
their context-specific meaning. It is automatically constructed by integrating
multiple emoji resources with BabelNet, which is the most comprehensive
multilingual sense inventory available to date. The paper discusses its
construction, evaluates the automatic resource creation process, and presents a
use case where EmojiNet disambiguates emoji usage in tweets. EmojiNet is
available online for use at http://emojinet.knoesis.org.
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Authors: Amit Sheth, Sujan Perera, Sanjaya Wijeratne
Date: 2016-10-25 00:00:00

Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to unsupervised learning from a massive
amount of data, albeit much of it relates to one modality/type of data at a
time. In spite of early assertions of the unreasonable effectiveness of data,
there is increasing recognition of utilizing knowledge whenever it is available
or can be created purposefully. In this paper, we focus on discussing the
indispensable role of knowledge for deeper understanding of complex text and
multimodal data in situations where (i) large amounts of training data
(labeled/unlabeled) are not available or labor intensive to create, (ii) the
objects (particularly text) to be recognized are complex (i.e., beyond simple
entity-person/location/organization names), such as implicit entities and
highly subjective content, and (iii) applications need to use complementary or
related data in multiple modalities/media. What brings us to the cusp of rapid
progress is our ability to (a) create knowledge, varying from comprehensive or
cross domain to domain or application specific, and (b) carefully exploit the
knowledge to further empower or extend the applications of ML/NLP techniques.
Using the early results in several diverse situations - both in data types and
applications - we seek to foretell unprecedented progress in our ability for
deeper understanding and exploitation of multimodal data.
Predicting the Relative Difficulty of Single Sentences With and Without Surrounding Context
Authors: Elliot Schumacher, Maxine Eskenazi, Gwen Frishkoff, Kevyn Collins-Thompson
Date: 2016-06-27 00:00:00
Proceeding: EMNLP 2016 11

The problem of accurately predicting relative reading difficulty across a set
of sentences arises in a number of important natural language applications,
such as finding and curating effective usage examples for intelligent language
tutoring systems. Yet while significant research has explored document- and
passage-level reading difficulty, the special challenges involved in assessing
aspects of readability for single sentences have received much less attention,
particularly when considering the role of surrounding passages. We introduce
and evaluate a novel approach for estimating the relative reading difficulty of
a set of sentences, with and without surrounding context. Using different sets
of lexical and grammatical features, we explore models for predicting pairwise
relative difficulty using logistic regression, and examine rankings generated
by aggregating pairwise difficulty labels using a Bayesian rating system to
form a final ranking. We also compare rankings derived for sentences assessed
with and without context, and find that contextual features can help predict
differences in relative difficulty judgments across these two conditions.
Co-Occuring Directions Sketching for Approximate Matrix Multiply
Authors: Youssef Mroueh, Etienne Marcheret, Vaibhava Goel
Date: 2016-10-25 00:00:00

We introduce co-occurring directions sketching, a deterministic algorithm for
approximate matrix product (AMM), in the streaming model. We show that
co-occuring directions achieves a better error bound for AMM than other
randomized and deterministic approaches for AMM. Co-occurring directions gives
a $1 + \epsilon$ -approximation of the optimal low rank approximation of a
matrix product. Empirically our algorithm outperforms competing methods for
AMM, for a small sketch size. We validate empirically our theoretical findings
and algorithms
Recurrent Instance Segmentation
Authors: Bernardino Romera-Paredes, Philip H. S. Torr
Date: 2015-11-25 00:00:00
Tasks: Instance Segmentation, Occlusion Handling, Plant Phenotyping, Segmentation, Semantic Segmentation

Instance segmentation is the problem of detecting and delineating each
distinct object of interest appearing in an image. Current instance
segmentation approaches consist of ensembles of modules that are trained
independently of each other, thus missing opportunities for joint learning.
Here we propose a new instance segmentation paradigm consisting in an
end-to-end method that learns how to segment instances sequentially. The model
is based on a recurrent neural network that sequentially finds objects and
their segmentations one at a time. This net is provided with a spatial memory
that keeps track of what pixels have been explained and allows occlusion
handling. In order to train the model we designed a principled loss function
that accurately represents the properties of the instance segmentation problem.
In the experiments carried out, we found that our method outperforms recent
approaches on multiple person segmentation, and all state of the art approaches
on the Plant Phenotyping dataset for leaf counting.
A Bayesian Ensemble for Unsupervised Anomaly Detection
Authors: Edward Yu, Parth Parekh
Date: 2016-10-24 00:00:00
Tasks: Anomaly Detection, Clustering, Ensemble Learning, General Classification, Outlier Detection, Time Series, Time Series Analysis, Unsupervised Anomaly Detection

Methods for unsupervised anomaly detection suffer from the fact that the data
is unlabeled, making it difficult to assess the optimality of detection
algorithms. Ensemble learning has shown exceptional results in classification
and clustering problems, but has not seen as much research in the context of
outlier detection. Existing methods focus on combining output scores of
individual detectors, but this leads to outputs that are not easily
interpretable. In this paper, we introduce a theoretical foundation for
combining individual detectors with Bayesian classifier combination. Not only
are posterior distributions easily interpreted as the probability distribution
of anomalies, but bias, variance, and individual error rates of detectors are
all easily obtained. Performance on real-world datasets shows high accuracy
across varied types of time series data.
A global optimization algorithm for sparse mixed membership matrix factorization
Authors: Fan Zhang, Chuangqi Wang, Andrew Trapp, Patrick Flaherty
Date: 2016-10-19 00:00:00
Tasks: global-optimization

Mixed membership factorization is a popular approach for analyzing data sets
that have within-sample heterogeneity. In recent years, several algorithms have
been developed for mixed membership matrix factorization, but they only
guarantee estimates from a local optimum. Here, we derive a global optimization
(GOP) algorithm that provides a guaranteed $\epsilon$-global optimum for a
sparse mixed membership matrix factorization problem. We test the algorithm on
simulated data and find the algorithm always bounds the global optimum across
random initializations and explores multiple modes efficiently.
UTD-CRSS Systems for 2016 NIST Speaker Recognition Evaluation
Authors: Chunlei Zhang, Fahimeh Bahmaninezhad, Shivesh Ranjan, Chengzhu Yu, Navid Shokouhi, John H. L. Hansen
Date: 2016-10-24 00:00:00
Tasks: Clustering, Dimensionality Reduction, Speaker Recognition

This document briefly describes the systems submitted by the Center for
Robust Speech Systems (CRSS) from The University of Texas at Dallas (UTD) to
the 2016 National Institute of Standards and Technology (NIST) Speaker
Recognition Evaluation (SRE). We developed several UBM and DNN i-Vector based
speaker recognition systems with different data sets and feature
representations. Given that the emphasis of the NIST SRE 2016 is on language
mismatch between training and enrollment/test data, so-called domain mismatch,
in our system development we focused on: (1) using unlabeled in-domain data for
centralizing data to alleviate the domain mismatch problem, (2) finding the
best data set for training LDA/PLDA, (3) using newly proposed dimension
reduction technique incorporating unlabeled in-domain data before PLDA
training, (4) unsupervised speaker clustering of unlabeled data and using them
alone or with previous SREs for PLDA training, (5) score calibration using only
unlabeled data and combination of unlabeled and development (Dev) data as
separate experiments.
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data
Authors: Yining Wang, Yu-Xiang Wang, Aarti Singh
Date: 2016-10-24 00:00:00
Tasks: Clustering, Dimensionality Reduction

Subspace clustering is the problem of partitioning unlabeled data points into
a number of clusters so that data points within one cluster lie approximately
on a low-dimensional linear subspace. In many practical scenarios, the
dimensionality of data points to be clustered are compressed due to constraints
of measurement, computation or privacy. In this paper, we study the theoretical
properties of a popular subspace clustering algorithm named sparse subspace
clustering (SSC) and establish formal success conditions of SSC on
dimensionality-reduced data. Our analysis applies to the most general fully
deterministic model where both underlying subspaces and data points within each
subspace are deterministically positioned, and also a wide range of
dimensionality reduction techniques (e.g., Gaussian random projection, uniform
subsampling, sketching) that fall into a subspace embedding framework (Meng &
Mahoney, 2013; Avron et al., 2014). Finally, we apply our analysis to a
differentially private SSC algorithm and established both privacy and utility
guarantees of the proposed method.
A Light Touch for Heavily Constrained SGD
Authors: Andrew Cotter, Maya Gupta, Jan Pfeifer
Date: 2015-12-15 00:00:00

Minimizing empirical risk subject to a set of constraints can be a useful
strategy for learning restricted classes of functions, such as monotonic
functions, submodular functions, classifiers that guarantee a certain class
label for some subset of examples, etc. However, these restrictions may result
in a very large number of constraints. Projected stochastic gradient descent
(SGD) is often the default choice for large-scale optimization in machine
learning, but requires a projection after each update. For heavily-constrained
objectives, we propose an efficient extension of SGD that stays close to the
feasible region while only applying constraints probabilistically at each
iteration. Theoretical analysis shows a compelling trade-off between
per-iteration work and the number of iterations needed on problems with a large
number of constraints.
Dialog-based Language Learning
Authors: Jason Weston
Date: 2016-04-20 00:00:00
Proceeding: NeurIPS 2016
Tasks: Machine Translation, Natural Language Understanding, Question Answering, Sentence, Translation

A long-term goal of machine learning research is to build an intelligent
dialog agent. Most research in natural language understanding has focused on
learning from fixed training sets of labeled data, with supervision either at
the word level (tagging, parsing tasks) or sentence level (question answering,
machine translation). This kind of supervision is not realistic of how humans
learn, where language is both learned by, and used for, communication. In this
work, we study dialog-based language learning, where supervision is given
naturally and implicitly in the response of the dialog partner during the
conversation. We study this setup in two domains: the bAbI dataset of (Weston
et al., 2015) and large-scale question answering from (Dodge et al., 2015). We
evaluate a set of baseline learning strategies on these tasks, and show that a
novel model incorporating predictive lookahead is a promising approach for
learning from a teacher's response. In particular, a surprising result is that
it can learn to answer questions correctly without any reward-based supervision
at all.