pwc-archive/papers-with-abstracts huggingface dataset:
An Empirical Bayes Approach for High Dimensional Classification
Authors: Yunbo Ouyang, Feng Liang
Date: 2017-02-16 00:00:00
Tasks: Classification, General Classification, Vocal Bursts Intensity Prediction

We propose an empirical Bayes estimator based on Dirichlet process mixture
model for estimating the sparse normalized mean difference, which could be
directly applied to the high dimensional linear classification. In theory, we
build a bridge to connect the estimation error of the mean difference and the
misclassification error, also provide sufficient conditions of sub-optimal
classifiers and optimal classifiers. In implementation, a variational Bayes
algorithm is developed to compute the posterior efficiently and could be
parallelized to deal with the ultra-high dimensional case.
Addressing the Data Sparsity Issue in Neural AMR Parsing
Authors: Xiaochang Peng, Chuan Wang, Daniel Gildea, Nianwen Xue
Date: 2017-02-16 00:00:00
Proceeding: EACL 2017 4
Tasks: AMR Parsing

Neural attention models have achieved great success in different NLP tasks.
How- ever, they have not fulfilled their promise on the AMR parsing task due to
the data sparsity issue. In this paper, we de- scribe a sequence-to-sequence
model for AMR parsing and present different ways to tackle the data sparsity
problem. We show that our methods achieve significant improvement over a
baseline neural atten- tion model and our results are also compet- itive
against state-of-the-art systems that do not use extra linguistic resources.
Normative theory of visual receptive fields
Authors: Tony Lindeberg
Date: 2017-01-23 00:00:00

This article gives an overview of a normative computational theory of visual
receptive fields, by which idealized functional models of early spatial,
spatio-chromatic and spatio-temporal receptive fields can be derived in an
axiomatic way based on structural properties of the environment in combination
with assumptions about the internal structure of a vision system to guarantee
consistent handling of image representations over multiple spatial and temporal
scales. Interestingly, this theory leads to predictions about visual receptive
field shapes with qualitatively very good similarity to biological receptive
fields measured in the retina, the LGN and the primary visual cortex (V1) of
mammals.
Reflexive Regular Equivalence for Bipartite Data
Authors: Aaron Gerow, Mingyang Zhou, Stan Matwin, Feng Shi
Date: 2017-02-16 00:00:00
Tasks: Clustering

Bipartite data is common in data engineering and brings unique challenges,
particularly when it comes to clustering tasks that impose on strong structural
assumptions. This work presents an unsupervised method for assessing similarity
in bipartite data. Similar to some co-clustering methods, the method is based
on regular equivalence in graphs. The algorithm uses spectral properties of a
bipartite adjacency matrix to estimate similarity in both dimensions. The
method is reflexive in that similarity in one dimension is used to inform
similarity in the other. Reflexive regular equivalence can also use the
structure of transitivities -- in a network sense -- the contribution of which
is controlled by the algorithm's only free-parameter, $\alpha$. The method is
completely unsupervised and can be used to validate assumptions of
co-similarity, which are required but often untested, in co-clustering
analyses. Three variants of the method with different normalizations are tested
on synthetic data. The method is found to be robust to noise and well-suited to
asymmetric co-similar structure, making it particularly informative for cluster
analysis and recommendation in bipartite data of unknown structure. In
experiments, the convergence and speed of the algorithm are found to be stable
for different levels of noise. Real-world data from a network of malaria genes
are analyzed, where the similarity produced by the reflexive method is shown to
out-perform other measures' ability to correctly classify genes.
Pruned non-local means
Authors: Sanjay Ghosh, Amit K. Mandal, Kunal. N. Chaudhury
Date: 2017-01-28 00:00:00
Tasks: Denoising

In Non-Local Means (NLM), each pixel is denoised by performing a weighted
averaging of its neighboring pixels, where the weights are computed using image
patches. We demonstrate that the denoising performance of NLM can be improved
by pruning the neighboring pixels, namely, by rejecting neighboring pixels
whose weights are below a certain threshold $\lambda$. While pruning can
potentially reduce pixel averaging in uniform-intensity regions, we demonstrate
that there is generally an overall improvement in the denoising performance. In
particular, the improvement comes from pixels situated close to edges and
corners. The success of the proposed method strongly depends on the choice of
the global threshold $\lambda$, which in turn depends on the noise level and
the image characteristics. We show how Stein's unbiased estimator of the
mean-squared error can be used to optimally tune $\lambda$, at a marginal
computational overhead. We present some representative denoising results to
demonstrate the superior performance of the proposed method over NLM and its
variants.
Fast and unsupervised methods for multilingual cognate clustering
Authors: Taraka Rama, Johannes Wahle, Pavel Sofroniev, Gerhard Jäger
Date: 2017-02-16 00:00:00
Tasks: Clustering

In this paper we explore the use of unsupervised methods for detecting
cognates in multilingual word lists. We use online EM to train sound segment
similarity weights for computing similarity between two words. We tested our
online systems on geographically spread sixteen different language groups of
the world and show that the Online PMI system (Pointwise Mutual Information)
outperforms a HMM based system and two linguistically motivated systems:
LexStat and ALINE. Our results suggest that a PMI system trained in an online
fashion can be used by historical linguists for fast and accurate
identification of cognates in not so well-studied language families.
Unsupervised Domain Adaptation with Residual Transfer Networks
Authors: Mingsheng Long, Han Zhu, Jian-Min Wang, Michael. I. Jordan
Date: 2016-02-14 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

The recent success of deep neural networks relies on massive amounts of
labeled data. For a target task where labeled data is unavailable, domain
adaptation can transfer a learner from a different source domain. In this
paper, we propose a new approach to domain adaptation in deep networks that can
jointly learn adaptive classifiers and transferable features from labeled data
in the source domain and unlabeled data in the target domain. We relax a
shared-classifier assumption made by previous methods and assume that the
source classifier and target classifier differ by a residual function. We
enable classifier adaptation by plugging several layers into deep network to
explicitly learn the residual function with reference to the target classifier.
We fuse features of multiple layers with tensor product and embed them into
reproducing kernel Hilbert spaces to match distributions for feature
adaptation. The adaptation can be achieved in most feed-forward models by
extending them with new residual layers and loss functions, which can be
trained efficiently via back-propagation. Empirical evidence shows that the new
approach outperforms state of the art methods on standard domain adaptation
benchmarks.
Recognizing Dynamic Scenes with Deep Dual Descriptor based on Key Frames and Key Segments
Authors: Sungeun Hong, Jongbin Ryu, Woobin Im, Hyun S. Yang
Date: 2017-02-15 00:00:00
Tasks: Scene Recognition, Scene Understanding

Recognizing dynamic scenes is one of the fundamental problems in scene
understanding, which categorizes moving scenes such as a forest fire,
landslide, or avalanche. While existing methods focus on reliable capturing of
static and dynamic information, few works have explored frame selection from a
dynamic scene sequence. In this paper, we propose dynamic scene recognition
using a deep dual descriptor based on `key frames' and `key segments.' Key
frames that reflect the feature distribution of the sequence with a small
number are used for capturing salient static appearances. Key segments, which
are captured from the area around each key frame, provide an additional
discriminative power by dynamic patterns within short time intervals. To this
end, two types of transferred convolutional neural network features are used in
our approach. A fully connected layer is used to select the key frames and key
segments, while the convolutional layer is used to describe them. We conducted
experiments using public datasets as well as a new dataset comprised of 23
dynamic scene classes with 10 videos per class. The evaluation results
demonstrated the state-of-the-art performance of the proposed method.
Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach
Authors: Sergi Valverde, Mariano Cabezas, Eloy Roura, Sandra González-Villà, Deborah Pareto, Joan-Carles Vilanova, Lluís Ramió-Torrentà, Àlex Rovira, Arnau Oliver, Xavier Lladó
Date: 2017-02-16 00:00:00
Tasks: Lesion Segmentation, Segmentation

In this paper, we present a novel automated method for White Matter (WM)
lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is
based on a cascade of two 3D patch-wise convolutional neural networks (CNN).
The first network is trained to be more sensitive revealing possible candidate
lesion voxels while the second network is trained to reduce the number of
misclassified voxels coming from the first network. This cascaded CNN
architecture tends to learn well from small sets of training data, which can be
very interesting in practice, given the difficulty to obtain manual label
annotations and the large amount of available unlabeled Magnetic Resonance
Imaging (MRI) data. We evaluate the accuracy of the proposed method on the
public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with
respect to other state-of-the-art MS lesion segmentation tools. Furthermore,
the proposed method is also evaluated on two private MS clinical datasets,
where the performance of our method is also compared with different recent
public available state-of-the-art MS lesion segmentation methods. At the time
of writing this paper, our method is the best ranked approach on the MICCAI2008
challenge, outperforming the rest of 60 participant methods when using all the
available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank
(3rd position) when using only T1-w and FLAIR modalities. On clinical MS data,
our approach exhibits a significant increase in the accuracy segmenting of WM
lesions when compared with the rest of evaluated methods, highly correlating
($r \ge 0.97$) also with the expected lesion volume.
Chord Angle Deviation using Tangent (CADT), an Efficient and Robust Contour-based Corner Detector
Authors: Mohammad Asiful Hossain, Abdul Kawsar Tushar
Date: 2017-02-16 00:00:00

Detection of corner is the most essential process in a large number of
computer vision and image processing applications. We have mentioned a number
of popular contour-based corner detectors in our paper. Among all these
detectors chord to triangular arm angle (CTAA) has been demonstrated as the
most dominant corner detector in terms of average repeatability. We introduce a
new effective method to calculate the value of curvature in this paper. By
demonstrating experimental results, our proposed technique outperforms CTAA and
other detectors mentioned in this paper. The results exhibit that our proposed
method is simple yet efficient at finding out corners more accurately and
reliably.
Dynamic Partition Models
Authors: Marc Goessling, Yali Amit
Date: 2017-02-16 00:00:00

We present a new approach for learning compact and intuitive distributed
representations with binary encoding. Rather than summing up expert votes as in
products of experts, we employ for each variable the opinion of the most
reliable expert. Data points are hence explained through a partitioning of the
variables into expert supports. The partitions are dynamically adapted based on
which experts are active. During the learning phase we adopt a smoothed version
of this model that uses separate mixtures for each data dimension. In our
experiments we achieve accurate reconstructions of high-dimensional data points
with at most a dozen experts.
Learning to Poke by Poking: Experiential Learning of Intuitive Physics
Authors: Pulkit Agrawal, Ashvin Nair, Pieter Abbeel, Jitendra Malik, Sergey Levine
Date: 2016-06-23 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Decision Making

We investigate an experiential learning paradigm for acquiring an internal
model of intuitive physics. Our model is evaluated on a real-world robotic
manipulation task that requires displacing objects to target locations by
poking. The robot gathered over 400 hours of experience by executing more than
100K pokes on different objects. We propose a novel approach based on deep
neural networks for modeling the dynamics of robot's interactions directly from
images, by jointly estimating forward and inverse models of dynamics. The
inverse model objective provides supervision to construct informative visual
features, which the forward model can then predict and in turn regularize the
feature space for the inverse model. The interplay between these two objectives
creates useful, accurate models that can then be used for multi-step decision
making. This formulation has the additional benefit that it is possible to
learn forward models in an abstract feature space and thus alleviate the need
of predicting pixels. Our experiments show that this joint modeling approach
outperforms alternative methods.
Random Forest regression for manifold-valued responses
Authors: Dimosthenis Tsagkrasoulis, Giovanni Montana
Date: 2017-01-29 00:00:00
Tasks: regression

An increasing array of biomedical and computer vision applications requires
the predictive modeling of complex data, for example images and shapes. The
main challenge when predicting such objects lies in the fact that they do not
comply to the assumptions of Euclidean geometry. Rather, they occupy non-linear
spaces, a.k.a. manifolds, where it is difficult to define concepts such as
coordinates, vectors and expected values. In this work, we construct a
non-parametric predictive methodology for manifold-valued objects, based on a
distance modification of the Random Forest algorithm. Our method is versatile
and can be applied both in cases where the response space is a well-defined
manifold, but also when such knowledge is not available. Model fitting and
prediction phases only require the definition of a suitable distance function
for the observed responses. We validate our methodology using simulations and
apply it on a series of illustrative image completion applications, showcasing
superior predictive performance, compared to various established regression
methods.
Grammatical Templates: Improving Text Difficulty Evaluation for Language Learners
Authors: Shuhan Wang, Erik Andersen
Date: 2016-09-16 00:00:00
Proceeding: COLING 2016 12
Tasks: General Classification

Language students are most engaged while reading texts at an appropriate
difficulty level. However, existing methods of evaluating text difficulty focus
mainly on vocabulary and do not prioritize grammatical features, hence they do
not work well for language learners with limited knowledge of grammar. In this
paper, we introduce grammatical templates, the expert-identified units of
grammar that students learn from class, as an important feature of text
difficulty evaluation. Experimental classification results show that
grammatical template features significantly improve text difficulty prediction
accuracy over baseline readability features by 7.4%. Moreover, we build a
simple and human-understandable text difficulty evaluation approach with 87.7%
accuracy, using only 5 grammatical template features.
Training Language Models Using Target-Propagation
Authors: Sam Wiseman, Sumit Chopra, Marc'Aurelio Ranzato, Arthur Szlam, Ruoyu Sun, Soumith Chintala, Nicolas Vasilache
Date: 2017-02-15 00:00:00

While Truncated Back-Propagation through Time (BPTT) is the most popular
approach to training Recurrent Neural Networks (RNNs), it suffers from being
inherently sequential (making parallelization difficult) and from truncating
gradient flow between distant time-steps. We investigate whether Target
Propagation (TPROP) style approaches can address these shortcomings.
Unfortunately, extensive experiments suggest that TPROP generally underperforms
BPTT, and we end with an analysis of this phenomenon, and suggestions for
future work.
Dropout with Expectation-linear Regularization
Authors: Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yao-Liang Yu, Yuntian Deng, Eduard Hovy
Date: 2016-09-26 00:00:00
Tasks: image-classification, Image Classification

Dropout, a simple and effective way to train deep neural networks, has led to
a number of impressive empirical successes and spawned many recent theoretical
investigations. However, the gap between dropout's training and inference
phases, introduced due to tractability considerations, has largely remained
under-appreciated. In this work, we first formulate dropout as a tractable
approximation of some latent variable model, leading to a clean view of
parameter sharing and enabling further theoretical analysis. Then, we introduce
(approximate) expectation-linear dropout neural networks, whose inference gap
we are able to formally characterize. Algorithmically, we show that our
proposed measure of the inference gap can be used to regularize the standard
dropout training objective, resulting in an \emph{explicit} control of the gap.
Our method is as simple and efficient as standard dropout. We further prove the
upper bounds on the loss in accuracy due to expectation-linearization, describe
classes of input distributions that expectation-linearize easily. Experiments
on three image classification benchmark datasets demonstrate that reducing the
inference gap can indeed improve the performance consistently.
Decentralized Collaborative Learning of Personalized Models over Networks
Authors: Paul Vanhaesebrouck, Aurélien Bellet, Marc Tommasi
Date: 2016-10-17 00:00:00

We consider a set of learning agents in a collaborative peer-to-peer network,
where each agent learns a personalized model according to its own learning
objective. The question addressed in this paper is: how can agents improve upon
their locally trained model by communicating with other agents that have
similar objectives? We introduce and analyze two asynchronous gossip algorithms
running in a fully decentralized manner. Our first approach, inspired from
label propagation, aims to smooth pre-trained local models over the network
while accounting for the confidence that each agent has in its initial model.
In our second approach, agents jointly learn and propagate their model by
making iterative updates based on both their local dataset and the behavior of
their neighbors. To optimize this challenging objective, our decentralized
algorithm is based on ADMM.
ChaLearn Looking at People: A Review of Events and Resources
Authors: Sergio Escalera, Xavier Baró, Hugo Jair Escalante, Isabelle Guyon
Date: 2017-01-10 00:00:00
Tasks: Gesture Recognition

This paper reviews the historic of ChaLearn Looking at People (LAP) events.
We started in 2011 (with the release of the first Kinect device) to run
challenges related to human action/activity and gesture recognition. Since then
we have regularly organized events in a series of competitions covering all
aspects of visual analysis of humans. So far we have organized more than 10
international challenges and events in this field. This paper reviews
associated events, and introduces the ChaLearn LAP platform where public
resources (including code, data and preprints of papers) related to the
organized events are available. We also provide a discussion on perspectives of
ChaLearn LAP activities.
Support Vector Machines and generalisation in HEP
Authors: Adrian Bevan, Rodrigo Gamboa Goñi, Jon Hays, Tom Stevenson
Date: 2017-02-15 00:00:00
Tasks: Benchmarking

We review the concept of Support Vector Machines (SVMs) and discuss examples
of their use in a number of scenarios. Several SVM implementations have been
used in HEP and we exemplify this algorithm using the Toolkit for Multivariate
Analysis (TMVA) implementation. We discuss examples relevant to HEP including
background suppression for $H\to\tau^+\tau^-$ at the LHC with several different
kernel functions. Performance benchmarking leads to the issue of generalisation
of hyper-parameter selection. The avoidance of fine tuning (over training or
over fitting) in MVA hyper-parameter optimisation, i.e. the ability to ensure
generalised performance of an MVA that is independent of the training,
validation and test samples, is of utmost importance. We discuss this issue and
compare and contrast performance of hold-out and k-fold cross-validation. We
have extended the SVM functionality and introduced tools to facilitate cross
validation in TMVA and present results based on these improvements.
Nearest Labelset Using Double Distances for Multi-label Classification
Authors: Hyukjun Gweon, Matthias Schonlau, Stefan Steiner
Date: 2017-02-15 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-label classification is a type of supervised learning where an instance
may belong to multiple labels simultaneously. Predicting each label
independently has been criticized for not exploiting any correlation between
labels. In this paper we propose a novel approach, Nearest Labelset using
Double Distances (NLDD), that predicts the labelset observed in the training
data that minimizes a weighted sum of the distances in both the feature space
and the label space to the new instance. The weights specify the relative
tradeoff between the two distances. The weights are estimated from a binomial
regression of the number of misclassified labels as a function of the two
distances. Model parameters are estimated by maximum likelihood. NLDD only
considers labelsets observed in the training data, thus implicitly taking into
account label dependencies. Experiments on benchmark multi-label data sets show
that the proposed method on average outperforms other well-known approaches in
terms of Hamming loss, 0/1 loss, and multi-label accuracy and ranks second
after ECC on the F-measure.
Handwritten Arabic Numeral Recognition using Deep Learning Neural Networks
Authors: Akm Ashiquzzaman, Abdul Kawsar Tushar
Date: 2017-02-15 00:00:00
Tasks: Deep Learning, Handwritten Digit Recognition

Handwritten character recognition is an active area of research with
applications in numerous fields. Past and recent works in this field have
concentrated on various languages. Arabic is one language where the scope of
research is still widespread, with it being one of the most popular languages
in the world and being syntactically different from other major languages. Das
et al. \cite{DBLP:journals/corr/abs-1003-1891} has pioneered the research for
handwritten digit recognition in Arabic. In this paper, we propose a novel
algorithm based on deep learning neural networks using appropriate activation
function and regularization layer, which shows significantly improved accuracy
compared to the existing Arabic numeral recognition methods. The proposed model
gives 97.4 percent accuracy, which is the recorded highest accuracy of the
dataset used in the experiment. We also propose a modification of the method
described in \cite{DBLP:journals/corr/abs-1003-1891}, where our method scores
identical accuracy as that of \cite{DBLP:journals/corr/abs-1003-1891}, with the
value of 93.8 percent.