pwc-archive/papers-with-abstracts huggingface dataset:
Joint Event Detection and Entity Resolution: a Virtuous Cycle
Authors: Matthias Galle, Jean-Michel Renders, Guillaume Jacquet
Date: 2016-07-18 00:00:00
Tasks: Articles, Clustering, Diversity, Entity Resolution, Event Detection

Clustering web documents has numerous applications, such as aggregating news
articles into meaningful events, detecting trends and hot topics on the Web,
preserving diversity in search results, etc. At the same time, the importance
of named entities and, in particular, the ability to recognize them and to
solve the associated co-reference resolution problem are widely recognized as
key enabling factors when mining, aggregating and comparing content on the Web.
  Instead of considering these two problems separately, we propose in this
paper a method that tackles jointly the problem of clustering news articles
into events and cross-document co-reference resolution of named entities. The
co-occurrence of named entities in the same clusters is used as an additional
signal to decide whether two referents should be merged into one entity. These
refined entities can in turn be used as enhanced features to re-cluster the
documents and then be refined again, entering into a virtuous cycle that
improves simultaneously the performances of both tasks. We implemented a
prototype system and report results using the TDT5 collection of news articles,
demonstrating the potential of our approach.
Learning to Hash with Binary Deep Neural Network
Authors: Thanh-Toan Do, Anh-Dzung Doan, Ngai-Man Cheung
Date: 2016-07-18 00:00:00
Tasks: Binarization

This work proposes deep network models and learning algorithms for
unsupervised and supervised binary hashing. Our novel network design constrains
one hidden layer to directly output the binary codes. This addresses a
challenging issue in some previous works: optimizing non-smooth objective
functions due to binarization. Moreover, we incorporate independence and
balance properties in the direct and strict forms in the learning. Furthermore,
we include similarity preserving property in our objective function. Our
resulting optimization with these binary, independence, and balance constraints
is difficult to solve. We propose to attack it with alternating optimization
and careful relaxation. Experimental results on three benchmark datasets show
that our proposed methods compare favorably with the state of the art.
A Semiparametric Model for Bayesian Reader Identification
Authors: Ahmed Abdelwahab, Reinhold Kliegl, Niels Landwehr
Date: 2016-07-18 00:00:00
Proceeding: EMNLP 2016 11
Tasks: model

We study the problem of identifying individuals based on their characteristic
gaze patterns during reading of arbitrary text. The motivation for this problem
is an unobtrusive biometric setting in which a user is observed during access
to a document, but no specific challenge protocol requiring the user's time and
attention is carried out. Existing models of individual differences in gaze
control during reading are either based on simple aggregate features of eye
movements, or rely on parametric density models to describe, for instance,
saccade amplitudes or word fixation durations. We develop flexible
semiparametric models of eye movements during reading in which densities are
inferred under a Gaussian process prior centered at a parametric distribution
family that is expected to approximate the true distribution well. An empirical
study on reading data from 251 individuals shows significant improvements over
the state of the art.
Neural Machine Translation with Recurrent Attention Modeling
Authors: Zichao Yang, Zhiting Hu, Yuntian Deng, Chris Dyer, Alex Smola
Date: 2016-07-18 00:00:00
Proceeding: EACL 2017 4
Tasks: Machine Translation, Translation

Knowing which words have been attended to in previous time steps while
generating a translation is a rich source of information for predicting what
words will be attended to in the future. We improve upon the attention model of
Bahdanau et al. (2014) by explicitly modeling the relationship between previous
and subsequent attention levels for each word using one recurrent network per
input word. This architecture easily captures informative features, such as
fertility and regularities in relative distortion. In experiments, we show our
parameterization of attention improves translation quality.
Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay
Authors: Ionel-Alexandru Hosu, Traian Rebedea
Date: 2016-07-18 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Montezuma's Revenge, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a novel method for learning how to play the most
difficult Atari 2600 games from the Arcade Learning Environment using deep
reinforcement learning. The proposed method, human checkpoint replay, consists
in using checkpoints sampled from human gameplay as starting points for the
learning process. This is meant to compensate for the difficulties of current
exploration strategies, such as epsilon-greedy, to find successful control
policies in games with sparse rewards. Like other deep reinforcement learning
architectures, our model uses a convolutional neural network that receives only
raw pixel inputs to estimate the state value function. We tested our method on
Montezuma's Revenge and Private Eye, two of the most challenging games from the
Atari platform. The results we obtained show a substantial improvement compared
to previous learning approaches, as well as over a random player. We also
propose a method for training deep reinforcement learning agents using human
gameplay experience, which we call human experience replay.
Deep Active Contours
Authors: Christian Rupprecht, Elizabeth Huaroc, Maximilian Baust, Nassir Navab
Date: 2016-07-18 00:00:00
Tasks: Interactive Segmentation

We propose a method for interactive boundary extraction which combines a
deep, patch-based representation with an active contour framework. We train a
class-specific convolutional neural network which predicts a vector pointing
from the respective point on the evolving contour towards the closest point on
the boundary of the object of interest. These predictions form a vector field
which is then used for evolving the contour by the Sobolev active contour
framework proposed by Sundaramoorthi et al. The resulting interactive
segmentation method is very efficient in terms of required computational
resources and can even be trained on comparatively small graphics cards. We
evaluate the potential of the proposed method on both medical and non-medical
challenge data sets, such as the STACOM data set and the PASCAL VOC 2012 data
set.
Recycle deep features for better object detection
Authors: Wei Li, Matthias Breier, Dorit Merhof
Date: 2016-07-18 00:00:00
Tasks: Object, object-detection, Object Detection, regression

Aiming at improving the performance of existing detection algorithms
developed for different applications, we propose a region regression-based
multi-stage class-agnostic detection pipeline, whereby the existing algorithms
are employed for providing the initial detection proposals. Better detection is
obtained by exploiting the power of deep learning in the region regress scheme
while avoiding the requirement on a huge amount of reference data for training
deep neural networks. Additionally, a novel network architecture with recycled
deep features is proposed, which provides superior regression results compared
to the commonly used architectures. As demonstrated on a data set with ~1200
samples of different classes, it is feasible to successfully train a deep
neural network in our proposed architecture and use it to obtain the desired
detection performance. Since only slight modifications are required to common
network architectures and since the deep neural network is trained using the
standard hyperparameters, the proposed detection is well accessible and can be
easily adopted to a broad variety of detection tasks.
A Batch, Off-Policy, Actor-Critic Algorithm for Optimizing the Average Reward
Authors: S. A. Murphy, Y. Deng, E. B. Laber, H. R. Maei, R. S. Sutton, K. Witkiewitz
Date: 2016-07-18 00:00:00

We develop an off-policy actor-critic algorithm for learning an optimal
policy from a training set composed of data from multiple individuals. This
algorithm is developed with a view towards its use in mobile health.
Deep Cascaded Bi-Network for Face Hallucination
Authors: Shizhan Zhu, Sifei Liu, Chen Change Loy, Xiaoou Tang
Date: 2016-07-18 00:00:00
Tasks: Face Hallucination, Hallucination

We present a novel framework for hallucinating faces of unconstrained poses
and with very low resolution (face size as small as 5pxIOD). In contrast to
existing studies that mostly ignore or assume pre-aligned face spatial
configuration (e.g. facial landmarks localization or dense correspondence
field), we alternatingly optimize two complementary tasks, namely face
hallucination and dense correspondence field estimation, in a unified
framework. In addition, we propose a new gated deep bi-network that contains
two functionality-specialized branches to recover different levels of texture
details. Extensive experiments demonstrate that such formulation allows
exceptional hallucination quality on in-the-wild low-res faces with significant
pose and illumination variations.
Intelligent Biohybrid Neurotechnologies: Are They Really What They Claim?
Authors: Gabriella Panuccio, Marianna Semprini, Lorenzo Natale, Michela Chiappalone
Date: 2016-07-18 00:00:00

In the era of intelligent biohybrid neurotechnologies for brain repair, new
fanciful terms are appearing in the scientific dictionary to define what has so
far been unimaginable. As the emerging neurotechnologies are becoming
increasingly polyhedral and sophisticated, should we talk about evolution and
rank the intelligence of these devices?
Geometric Mean Metric Learning
Authors: Pourya Habib Zadeh, Reshad Hosseini, Suvrit Sra
Date: 2016-07-18 00:00:00
Tasks: Form, General Classification, Metric Learning, Riemannian optimization

We revisit the task of learning a Euclidean metric from data. We approach
this problem from first principles and formulate it as a surprisingly simple
optimization problem. Indeed, our formulation even admits a closed form
solution. This solution possesses several very attractive properties: (i) an
innate geometric appeal through the Riemannian geometry of positive definite
matrices; (ii) ease of interpretability; and (iii) computational speed several
orders of magnitude faster than the widely used LMNN and ITML methods.
Furthermore, on standard benchmark datasets, our closed-form solution
consistently attains higher classification accuracy.
Neural Semantic Role Labeling with Dependency Path Embeddings
Authors: Michael Roth, Mirella Lapata
Date: 2016-05-24 00:00:00
Proceeding: ACL 2016 8
Tasks: Semantic Role Labeling

This paper introduces a novel model for semantic role labeling that makes use
of neural sequence modeling techniques. Our approach is motivated by the
observation that complex syntactic structures and related phenomena, such as
nested subordinations and nominal predicates, are not handled well by existing
models. Our model treats such instances as sub-sequences of lexicalized
dependency paths and learns suitable embedding representations. We
experimentally demonstrate that such embeddings can improve results over
previous state-of-the-art semantic role labelers, and showcase qualitative
improvements obtained by our method.
Performance Based Evaluation of Various Machine Learning Classification Techniques for Chronic Kidney Disease Diagnosis
Authors: Sahil Sharma, Vinod Sharma, Atul Sharma
Date: 2016-06-28 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Medical Diagnosis, Prognosis, Sensitivity, Specificity

Areas where Artificial Intelligence (AI) & related fields are finding their
applications are increasing day by day, moving from core areas of computer
science they are finding their applications in various other domains.In recent
times Machine Learning i.e. a sub-domain of AI has been widely used in order to
assist medical experts and doctors in the prediction, diagnosis and prognosis
of various diseases and other medical disorders. In this manuscript the authors
applied various machine learning algorithms to a problem in the domain of
medical diagnosis and analyzed their efficiency in predicting the results. The
problem selected for the study is the diagnosis of the Chronic Kidney
Disease.The dataset used for the study consists of 400 instances and 24
attributes. The authors evaluated 12 classification techniques by applying them
to the Chronic Kidney Disease data. In order to calculate efficiency, results
of the prediction by candidate methods were compared with the actual medical
results of the subject.The various metrics used for performance evaluation are
predictive accuracy, precision, sensitivity and specificity. The results
indicate that decision-tree performed best with nearly the accuracy of 98.6%,
sensitivity of 0.9720, precision of 1 and specificity of 1.
Distributed Coding of Multiview Sparse Sources with Joint Recovery
Authors: Huynh Van Luong, Nikos Deligiannis, Søren Forchhammer, André Kaup
Date: 2016-07-18 00:00:00
Tasks: compressed sensing, Object Recognition

In support of applications involving multiview sources in distributed object
recognition using lightweight cameras, we propose a new method for the
distributed coding of sparse sources as visual descriptor histograms extracted
from multiview images. The problem is challenging due to the computational and
energy constraints at each camera as well as the limitations regarding
inter-camera communication. Our approach addresses these challenges by
exploiting the sparsity of the visual descriptor histograms as well as their
intra- and inter-camera correlations. Our method couples distributed source
coding of the sparse sources with a new joint recovery algorithm that
incorporates multiple side information signals, where prior knowledge (low
quality) of all the sparse sources is initially sent to exploit their
correlations. Experimental evaluation using the histograms of shift-invariant
feature transform (SIFT) descriptors extracted from multiview images shows that
our method leads to bit-rate saving of up to 43% compared to the
state-of-the-art distributed compressed sensing method with independent
encoding of the sources.
Graphical Model Sketch
Authors: Branislav Kveton, Hung Bui, Mohammad Ghavamzadeh, Georgios Theocharous, S. Muthukrishnan, Siqi Sun
Date: 2016-02-09 00:00:00
Tasks: model

Structured high-cardinality data arises in many domains, and poses a major
challenge for both modeling and inference. Graphical models are a popular
approach to modeling structured data but they are unsuitable for
high-cardinality variables. The count-min (CM) sketch is a popular approach to
estimating probabilities in high-cardinality data but it does not scale well
beyond a few variables. In this work, we bring together the ideas of graphical
models and count sketches; and propose and analyze several approaches to
estimating probabilities in structured high-cardinality streams of data. The
key idea of our approximations is to use the structure of a graphical model and
approximately estimate its factors by "sketches", which hash high-cardinality
variables using random projections. Our approximations are computationally
efficient and their space complexity is independent of the cardinality of
variables. Our error bounds are multiplicative and significantly improve upon
those of the CM sketch, a state-of-the-art approach to estimating probabilities
in streams. We evaluate our approximations on synthetic and real-world
problems, and report an order of magnitude improvements over the CM sketch.
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
Authors: Wenhan Yang, Jiashi Feng, Jianchao Yang, Fang Zhao, Jiaying Liu, Zongming Guo, Shuicheng Yan
Date: 2016-04-29 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

In this work, we consider the image super-resolution (SR) problem. The main
challenge of image SR is to recover high-frequency details of a low-resolution
(LR) image that are important for human perception. To address this essentially
ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual~(DEGREE)
network to progressively recover the high-frequency details. Different from
most of existing methods that aim at predicting high-resolution (HR) images
directly, DEGREE investigates an alternative route to recover the difference
between a pair of LR and HR images by recurrent residual learning. DEGREE
further augments the SR process with edge-preserving capability, namely the LR
image and its edge map can jointly infer the sharp edge details of the HR image
during the recurrent recovery process. To speed up its training convergence
rate, by-pass connections across multiple layers of DEGREE are constructed. In
addition, we offer an understanding on DEGREE from the view-point of sub-band
frequency decomposition on image signal and experimentally demonstrate how
DEGREE can recover different frequency bands separately. Extensive experiments
on three benchmark datasets clearly demonstrate the superiority of DEGREE over
well-established baselines and DEGREE also provides new state-of-the-arts on
these datasets.
Sparse Representation-Based Classification: Orthogonal Least Squares or Orthogonal Matching Pursuit?
Authors: Minshan Cui, Saurabh Prasad
Date: 2016-07-18 00:00:00
Tasks: Benchmarking, Classification, Face Recognition, General Classification, image-classification, Image Classification, Remote Sensing Image Classification, Sparse Representation-based Classification

Spare representation of signals has received significant attention in recent
years. Based on these developments, a sparse representation-based
classification (SRC) has been proposed for a variety of classification and
related tasks, including face recognition. Recently, a class dependent variant
of SRC was proposed to overcome the limitations of SRC for remote sensing image
classification. Traditionally, greedy pursuit based method such as orthogonal
matching pursuit (OMP) are used for sparse coefficient recovery due to their
simplicity as well as low time-complexity. However, orthogonal least square
(OLS) has not yet been widely used in classifiers that exploit the sparse
representation properties of data. Since OLS produces lower signal
reconstruction error than OMP under similar conditions, we hypothesize that
more accurate signal estimation will further improve the classification
performance of classifiers that exploiting the sparsity of data. In this paper,
we present a classification method based on OLS, which implements OLS in a
classwise manner to perform the classification. We also develop and present its
kernelized variant to handle nonlinearly separable data. Based on two
real-world benchmarking hyperspectral datasets, we demonstrate that class
dependent OLS based methods outperform several baseline methods including
traditional SRC and the support vector machine classifier.
Composite Kernel Local Angular Discriminant Analysis for Multi-Sensor Geospatial Image Analysis
Authors: Saurabh Prasad, Minshan Cui, Lifeng Yan
Date: 2016-07-18 00:00:00
Tasks: Anomaly Detection

With the emergence of passive and active optical sensors available for
geospatial imaging, information fusion across sensors is becoming ever more
important. An important aspect of single (or multiple) sensor geospatial image
analysis is feature extraction - the process of finding "optimal" lower
dimensional subspaces that adequately characterize class-specific information
for subsequent analysis tasks, such as classification, change and anomaly
detection etc. In recent work, we proposed and developed an angle-based
discriminant analysis approach that projected data onto subspaces with maximal
"angular" separability in the input (raw) feature space and Reproducible Kernel
Hilbert Space (RKHS). We also developed an angular locality preserving variant
of this algorithm. In this letter, we advance this work and make it suitable
for information fusion - we propose and validate a composite kernel local
angular discriminant analysis projection, that can operate on an ensemble of
feature sources (e.g. from different sources), and project the data onto a
unified space through composite kernels where the data are maximally separated
in an angular sense. We validate this method with the multi-sensor University
of Houston hyperspectral and LiDAR dataset, and demonstrate that the proposed
method significantly outperforms other composite kernel approaches to sensor
(information) fusion.
Traffic Sign Classification Using Deep Inception Based Convolutional Networks
Authors: Mrinal Haloi
Date: 2015-11-10 00:00:00
Tasks: Classification, General Classification, Traffic Sign Recognition, Translation

In this work, we propose a novel deep network for traffic sign classification
that achieves outstanding performance on GTSRB surpassing all previous methods.
Our deep network consists of spatial transformer layers and a modified version
of inception module specifically designed for capturing local and global
features together. This features adoption allows our network to classify
precisely intraclass samples even under deformations. Use of spatial
transformer layer makes this network more robust to deformations such as
translation, rotation, scaling of input images. Unlike existing approaches that
are developed with hand-crafted features, multiple deep networks with huge
parameters and data augmentations, our method addresses the concern of
exploding parameters and augmentations. We have achieved the state-of-the-art
performance of 99.81\% on GTSRB dataset.
Improved Microaneurysm Detection using Deep Neural Networks
Authors: Mrinal Haloi
Date: 2015-05-17 00:00:00
Tasks: General Classification

In this work, we propose a novel microaneurysm (MA) detection for early
diabetic retinopathy screening using color fundus images. Since MA usually the
first lesions to appear as an indicator of diabetic retinopathy, accurate
detection of MA is necessary for treatment. Each pixel of the image is
classified as either MA or non-MA using a deep neural network with dropout
training procedure using maxout activation function. No preprocessing step or
manual feature extraction is required. Substantial improvements over standard
MA detection method based on the pipeline of preprocessing, feature extraction,
classification followed by post processing is achieved. The presented method is
evaluated in publicly available Retinopathy Online Challenge (ROC) and
Diaretdb1v2 database and achieved state-of-the-art accuracy.
GP-select: Accelerating EM using adaptive subspace preselection
Authors: Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Joerg Luecke, Arthur Gretton
Date: 2014-12-10 00:00:00
Tasks: Object Localization

We propose a nonparametric procedure to achieve fast inference in generative
graphical models when the number of latent states is very large. The approach
is based on iterative latent variable preselection, where we alternate between
learning a 'selection function' to reveal the relevant latent variables, and
use this to obtain a compact approximation of the posterior distribution for
EM; this can make inference possible where the number of possible latent states
is e.g. exponential in the number of latent variables, whereas an exact
approach would be computationally unfeasible. We learn the selection function
entirely from the observed data and current EM state via Gaussian process
regression. This is by contrast with earlier approaches, where selection
functions were manually-designed for each problem setting. We show that our
approach performs as well as these bespoke selection functions on a wide
variety of inference problems: in particular, for the challenging case of a
hierarchical model for object localization with occlusion, we achieve results
that match a customized state-of-the-art selection method, at a far lower
computational cost.