pwc-archive/papers-with-abstracts huggingface dataset:
A Survey on Social Media Anomaly Detection
Authors: Rose Yu, Huida Qiu, Zhen Wen, Ching-Yung Lin, Yan Liu
Date: 2016-01-06 00:00:00
Tasks: Anomaly Detection, Survey

Social media anomaly detection is of critical importance to prevent malicious
activities such as bullying, terrorist attack planning, and fraud information
dissemination. With the recent popularity of social media, new types of
anomalous behaviors arise, causing concerns from various parties. While a large
amount of work have been dedicated to traditional anomaly detection problems,
we observe a surge of research interests in the new realm of social media
anomaly detection. In this paper, we present a survey on existing approaches to
address this problem. We focus on the new type of anomalous phenomena in the
social media and review the recent developed techniques to detect those special
types of anomalies. We provide a general overview of the problem domain, common
formulations, existing methodologies and potential directions. With this work,
we hope to call out the attention from the research community on this
challenging problem and open up new directions that we can contribute in the
future.
Multi-layer Representation Learning for Medical Concepts
Authors: Edward Choi, Mohammad Taha Bahadori, Elizabeth Searles, Catherine Coffey, Jimeng Sun
Date: 2016-02-17 00:00:00
Tasks: Document Classification, Machine Translation, Medical Diagnosis, Representation Learning, Translation

Learning efficient representations for concepts has been proven to be an
important basis for many applications such as machine translation or document
classification. Proper representations of medical concepts such as diagnosis,
medication, procedure codes and visits will have broad applications in
healthcare analytics. However, in Electronic Health Records (EHR) the visit
sequences of patients include multiple concepts (diagnosis, procedure, and
medication codes) per visit. This structure provides two types of relational
information, namely sequential order of visits and co-occurrence of the codes
within each visit. In this work, we propose Med2Vec, which not only learns
distributed representations for both medical codes and visits from a large EHR
dataset with over 3 million visits, but also allows us to interpret the learned
representations confirmed positively by clinical experts. In the experiments,
Med2Vec displays significant improvement in key medical applications compared
to popular baselines such as Skip-gram, GloVe and stacked autoencoder, while
providing clinically meaningful interpretation.
Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods
Authors: Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang
Date: 2016-02-17 00:00:00

To the best of our knowledge, there are no general well-founded robust
methods for statistical unsupervised learning. Most of the unsupervised methods
explicitly or implicitly depend on the kernel covariance operator (kernel CO)
or kernel cross-covariance operator (kernel CCO). They are sensitive to
contaminated data, even when using bounded positive definite kernels. First, we
propose robust kernel covariance operator (robust kernel CO) and robust kernel
crosscovariance operator (robust kernel CCO) based on a generalized loss
function instead of the quadratic loss function. Second, we propose influence
function of classical kernel canonical correlation analysis (classical kernel
CCA). Third, using this influence function, we propose a visualization method
to detect influential observations from two sets of data. Finally, we propose a
method based on robust kernel CO and robust kernel CCO, called robust kernel
CCA, which is designed for contaminated data and less sensitive to noise than
classical kernel CCA. The principles we describe also apply to many kernel
methods which must deal with the issue of kernel CO or kernel CCO. Experiments
on synthesized and imaging genetics analysis demonstrate that the proposed
visualization and robust kernel CCA can be applied effectively to both ideal
data and contaminated data. The robust methods show the superior performance
over the state-of-the-art methods.
A Comprehensive Comparative Study of Word and Sentence Similarity Measures
Authors: Issa Atoum, Ahmed Otoom, Narayanan Kulathuramaiyer
Date: 2016-02-17 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval, Sentence, Sentence Similarity, Text Summarization

Sentence similarity is considered the basis of many natural language tasks
such as information retrieval, question answering and text summarization. The
semantic meaning between compared text fragments is based on the words semantic
features and their relationships. This article reviews a set of word and
sentence similarity measures and compares them on benchmark datasets. On the
studied datasets, results showed that hybrid semantic measures perform better
than both knowledge and corpus based measures.
High-Performance and Tunable Stereo Reconstruction
Authors: Sudeep Pillai, Srikumar Ramalingam, John J. Leonard
Date: 2015-11-03 00:00:00
Tasks: CPU, Disparity Estimation, Stereo Disparity Estimation, Vocal Bursts Intensity Prediction

Traditional stereo algorithms have focused their efforts on reconstruction
quality and have largely avoided prioritizing for run time performance. Robots,
on the other hand, require quick maneuverability and effective computation to
observe its immediate environment and perform tasks within it. In this work, we
propose a high-performance and tunable stereo disparity estimation method, with
a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can
potentially enable robots to quickly reconstruct their immediate surroundings
and maneuver at high-speeds. Our key contribution is a disparity estimation
algorithm that iteratively approximates the scene depth via a piece-wise planar
mesh from stereo imagery, with a fast depth validation step for semi-dense
reconstruction. The mesh is initially seeded with sparsely matched keypoints,
and is recursively tessellated and refined as needed (via a resampling stage),
to provide the desired stereo disparity accuracy. The inherent simplicity and
speed of our approach, with the ability to tune it to a desired reconstruction
quality and runtime performance makes it a compelling solution for applications
in high-speed vehicles.
Lass-0: sparse non-convex regression by local search
Authors: William Herlands, Maria De-Arteaga, Daniel Neill, Artur Dubrawski
Date: 2015-11-13 00:00:00
Tasks: regression

We compute approximate solutions to L0 regularized linear regression using L1
regularization, also known as the Lasso, as an initialization step. Our
algorithm, the Lass-0 ("Lass-zero"), uses a computationally efficient stepwise
search to determine a locally optimal L0 solution given any L1 regularization
solution. We present theoretical results of consistency under orthogonality and
appropriate handling of redundant features. Empirically, we use synthetic data
to demonstrate that Lass-0 solutions are closer to the true sparse support than
L1 regularization models. Additionally, in real-world data Lass-0 finds more
parsimonious solutions than L1 regularization while maintaining similar
predictive accuracy.
Cell segmentation with random ferns and graph-cuts
Authors: Arnaud Browet, Christophe De Vleeschouwer, Laurent Jacques, Navrita Mathiah, Bechara Saykali, Isabelle Migeotte
Date: 2016-02-17 00:00:00
Tasks: Cell Segmentation, Image Segmentation, Semantic Segmentation

The progress in imaging techniques have allowed the study of various aspect
of cellular mechanisms. To isolate individual cells in live imaging data, we
introduce an elegant image segmentation framework that effectively extracts
cell boundaries, even in the presence of poor edge details. Our approach works
in two stages. First, we estimate pixel interior/border/exterior class
probabilities using random ferns. Then, we use an energy minimization framework
to compute boundaries whose localization is compliant with the pixel class
probabilities. We validate our approach on a manually annotated dataset.
Low-Rank Factorization of Determinantal Point Processes for Recommendation
Authors: Mike Gartrell, Ulrich Paquet, Noam Koenigstein
Date: 2016-02-17 00:00:00
Tasks: Diversity, Point Processes, Product Recommendation

Determinantal point processes (DPPs) have garnered attention as an elegant
probabilistic model of set diversity. They are useful for a number of subset
selection tasks, including product recommendation. DPPs are parametrized by a
positive semi-definite kernel matrix. In this work we present a new method for
learning the DPP kernel from observed data using a low-rank factorization of
this kernel. We show that this low-rank factorization enables a learning
algorithm that is nearly an order of magnitude faster than previous approaches,
while also providing for a method for computing product recommendation
predictions that is far faster (up to 20x faster or more for large item
catalogs) than previous techniques that involve a full-rank DPP kernel.
Furthermore, we show that our method provides equivalent or sometimes better
predictive performance than prior full-rank DPP approaches, and better
performance than several other competing recommendation methods in many cases.
We conduct an extensive experimental evaluation using several real-world
datasets in the domain of product recommendation to demonstrate the utility of
our method, along with its limitations.
11 x 11 Domineering is Solved: The first player wins
Authors: Jos W. H. M. Uiterwijk
Date: 2016-02-17 00:00:00
Tasks: Recommendation Systems

We have developed a program called MUDoS (Maastricht University Domineering
Solver) that solves Domineering positions in a very efficient way. This enables
the solution of known positions so far (up to the 10 x 10 board) much quicker
(measured in number of investigated nodes).
  More importantly, it enables the solution of the 11 x 11 Domineering board, a
board up till now far out of reach of previous Domineering solvers. The
solution needed the investigation of 259,689,994,008 nodes, using almost half a
year of computation time on a single simple desktop computer. The results show
that under optimal play the first player wins the 11 x 11 Domineering game,
irrespective if Vertical or Horizontal starts the game.
  In addition, several other boards hitherto unsolved were solved. Using the
convention that Vertical starts, the 8 x 15, 11 x 9, 12 x 8, 12 x 15, 14 x 8,
and 17 x 6 boards are all won by Vertical, whereas the 6 x 17, 8 x 12, 9 x 11,
and 11 x 10 boards are all won by Horizontal.
Symmetry Breaking Predicates for SAT-based DFA Identification
Authors: Vladimir Ulyantsev, Ilya Zakirzyanov, Anatoly Shalyto
Date: 2016-02-16 00:00:00

It was shown before that the NP-hard problem of deterministic finite automata
(DFA) identification can be effectively translated to Boolean satisfiability
(SAT). Modern SAT-solvers can tackle hard DFA identification instances
efficiently. We present a technique to reduce the problem search space by
enforcing an enumeration of DFA states in depth-first search (DFS) or
breadth-first search (BFS) order. We propose symmetry breaking predicates,
which can be added to Boolean formulae representing various DFA identification
problems. We show how to apply this technique to DFA identification from both
noiseless and noisy data. Also we propose a method to identify all automata of
the desired size. The proposed approach outperforms the current
state-of-the-art DFASAT method for DFA identification from noiseless data. A
big advantage of the proposed approach is that it allows to determine exactly
the existence or non-existence of a solution of the noisy DFA identification
problem unlike metaheuristic approaches such as genetic algorithms.
Image Restoration: A General Wavelet Frame Based Model and Its Asymptotic Analysis
Authors: Bin Dong, Zuowei Shen, Peichu Xie
Date: 2016-02-17 00:00:00
Tasks: Image Restoration

Image restoration is one of the most important areas in imaging science.
Mathematical tools have been widely used in image restoration, where wavelet
frame based approach is one of the successful examples. In this paper, we
introduce a generic wavelet frame based image restoration model, called the
"general model", which includes most of the existing wavelet frame based models
as special cases. Moreover, the general model also includes examples that are
new to the literature. Motivated by our earlier studies [1-3], We provide an
asymptotic analysis of the general model as image resolution goes to infinity,
which establishes a connection between the general model in discrete setting
and a new variatonal model in continuum setting. The variational model also
includes some of the existing variational models as special cases, such as the
total generalized variational model proposed by [4]. In the end, we introduce
an algorithm solving the general model and present one numerical simulation as
an example.
Deep Linear Discriminant Analysis
Authors: Matthias Dorfer, Rainer Kelz, Gerhard Widmer
Date: 2015-11-15 00:00:00
Tasks: Dimensionality Reduction

We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns
linearly separable latent representations in an end-to-end fashion. Classic LDA
extracts features which preserve class separability and is used for
dimensionality reduction for many classification problems. The central idea of
this paper is to put LDA on top of a deep neural network. This can be seen as a
non-linear extension of classic LDA. Instead of maximizing the likelihood of
target labels for individual samples, we propose an objective function that
pushes the network to produce feature distributions which: (a) have low
variance within the same class and (b) high variance between different classes.
Our objective is derived from the general LDA eigenvalue problem and still
allows to train with stochastic gradient descent and back-propagation. For
evaluation we test our approach on three different benchmark datasets (MNIST,
CIFAR-10 and STL-10). DeepLDA produces competitive results on MNIST and
CIFAR-10 and outperforms a network trained with categorical cross entropy (same
architecture) on a supervised setting of STL-10.
PlaNet - Photo Geolocation with Convolutional Neural Networks
Authors: Tobias Weyand, Ilya Kostrikov, James Philbin
Date: 2016-02-17 00:00:00
Tasks: Image Retrieval, Photo geolocation estimation, Retrieval

Is it possible to build a system to determine the location where a photo was
taken using just its pixels? In general, the problem seems exceptionally
difficult: it is trivial to construct situations where no location can be
inferred. Yet images often contain informative cues such as landmarks, weather
patterns, vegetation, road markings, and architectural details, which in
combination may allow one to determine an approximate location and occasionally
an exact location. Websites such as GeoGuessr and View from your Window suggest
that humans are relatively good at integrating these cues to geolocate images,
especially en-masse. In computer vision, the photo geolocation problem is
usually approached using image retrieval methods. In contrast, we pose the
problem as one of classification by subdividing the surface of the earth into
thousands of multi-scale geographic cells, and train a deep network using
millions of geotagged images. While previous approaches only recognize
landmarks or perform approximate matching using global image descriptors, our
model is able to use and integrate multiple visible cues. We show that the
resulting model, called PlaNet, outperforms previous approaches and even
attains superhuman levels of accuracy in some cases. Moreover, we extend our
model to photo albums by combining it with a long short-term memory (LSTM)
architecture. By learning to exploit temporal coherence to geolocate uncertain
photos, we demonstrate that this model achieves a 50% performance improvement
over the single-image model.
Density-based Denoising of Point Cloud
Authors: Faisal Zaman, Ya Ping Wong, Boon Yian Ng
Date: 2016-02-17 00:00:00
Tasks: Clustering, Denoising, Density Estimation, Surface Reconstruction

Point cloud source data for surface reconstruction is usually contaminated
with noise and outliers. To overcome this deficiency, a density-based point
cloud denoising method is presented to remove outliers and noisy points. First,
particle-swam optimization technique is employed for automatically
approximating optimal bandwidth of multivariate kernel density estimation to
ensure the robust performance of density estimation. Then, mean-shift based
clustering technique is used to remove outliers through a thresholding scheme.
After removing outliers from the point cloud, bilateral mesh filtering is
applied to smooth the remaining points. The experimental results show that this
approach, comparably, is robust and efficient.
Large Scale Kernel Learning using Block Coordinate Descent
Authors: Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Benjamin Recht
Date: 2016-02-17 00:00:00
Tasks: Classification, General Classification, regression

We demonstrate that distributed block coordinate descent can quickly solve
kernel regression and classification problems with millions of data points.
Armed with this capability, we conduct a thorough comparison between the full
kernel, the Nystr\"om method, and random features on three large classification
tasks from various domains. Our results suggest that the Nystr\"om method
generally achieves better statistical accuracy than random features, but can
require significantly more iterations of optimization. Lastly, we derive new
rates for block coordinate descent which support our experimental findings when
specialized to kernel methods.
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
Authors: Xiang Ren, Wenqi He, Meng Qu, Clare R. Voss, Heng Ji, Jiawei Han
Date: 2016-02-17 00:00:00
Tasks: Entity Typing, Semantic Similarity, Semantic Textual Similarity, Vocal Bursts Type Prediction

Current systems of fine-grained entity typing use distant supervision in
conjunction with existing knowledge bases to assign categories (type labels) to
entity mentions. However, the type labels so obtained from knowledge bases are
often noisy (i.e., incorrect for the entity mention's local context). We define
a new task, Label Noise Reduction in Entity Typing (LNR), to be the automatic
identification of correct type labels (type-paths) for training examples, given
the set of candidate type labels obtained by distant supervision with a given
type hierarchy. The unknown type labels for individual entity mentions and the
semantic similarity between entity types pose unique challenges for solving the
LNR task. We propose a general framework, called PLE, to jointly embed entity
mentions, text features and entity types into the same low-dimensional space
where, in that space, objects whose types are semantically close have similar
representations. Then we estimate the type-path for each training example in a
top-down manner using the learned embeddings. We formulate a global objective
for learning the embeddings from text corpora and knowledge bases, which adopts
a novel margin-based loss that is robust to noisy labels and faithfully models
type correlation derived from knowledge bases. Our experiments on three public
typing datasets demonstrate the effectiveness and robustness of PLE, with an
average of 25% improvement in accuracy compared to next best method.
Authorship Attribution Using a Neural Network Language Model
Authors: Zhenhao Ge, Yufang Sun, Mark J. T. Smith
Date: 2016-02-17 00:00:00
Tasks: Authorship Attribution, Language Modeling, Language Modelling, model

In practice, training language models for individual authors is often
expensive because of limited data resources. In such cases, Neural Network
Language Models (NNLMs), generally outperform the traditional non-parametric
N-gram models. Here we investigate the performance of a feed-forward NNLM on an
authorship attribution problem, with moderate author set size and relatively
limited data. We also consider how the text topics impact performance. Compared
with a well-constructed N-gram baseline method with Kneser-Ney smoothing, the
proposed method achieves nearly 2:5% reduction in perplexity and increases
author classification accuracy by 3:43% on average, given as few as 5 test
sentences. The performance is very competitive with the state of the art in
terms of accuracy and demand on test data. The source code, preprocessed
datasets, a detailed description of the methodology and results are available
at https://github.com/zge/authorship-attribution.
Choice by Elimination via Deep Neural Networks
Authors: Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2016-02-17 00:00:00
Tasks: Learning-To-Rank

We introduce Neural Choice by Elimination, a new framework that integrates
deep neural networks into probabilistic sequential choice models for learning
to rank. Given a set of items to chose from, the elimination strategy starts
with the whole item set and iteratively eliminates the least worthy item in the
remaining subset. We prove that the choice by elimination is equivalent to
marginalizing out the random Gompertz latent utilities. Coupled with the choice
model is the recently introduced Neural Highway Networks for approximating
arbitrarily complex rank functions. We evaluate the proposed framework on a
large-scale public dataset with over 425K items, drawn from the Yahoo! learning
to rank challenge. It is demonstrated that the proposed method is competitive
against state-of-the-art learning to rank methods.
High-dimensional Time Series Prediction with Missing Values
Authors: Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon
Date: 2015-09-28 00:00:00
Tasks: Demand Forecasting, Matrix Completion, Missing Values, Prediction, Time Series, Time Series Analysis, Time Series Prediction, Vocal Bursts Intensity Prediction

High-dimensional time series prediction is needed in applications as diverse
as demand forecasting and climatology. Often, such applications require methods
that are both highly scalable, and deal with noisy data in terms of corruptions
or missing values. Classical time series methods usually fall short of handling
both these issues. In this paper, we propose to adapt matrix matrix completion
approaches that have previously been successfully applied to large scale noisy
data, but which fail to adequately model high-dimensional time series due to
temporal dependencies. We present a novel temporal regularized matrix
factorization (TRMF) framework which supports data-driven temporal dependency
learning and enables forecasting ability to our new matrix factorization
approach. TRMF is highly general, and subsumes many existing matrix
factorization approaches for time series data. We make interesting connections
to graph regularized matrix factorization methods in the context of learning
the dependencies. Experiments on both real and synthetic data show that TRMF
outperforms several existing approaches for common time series tasks.