pwc-archive/papers-with-abstracts huggingface dataset:
An Iterative Transfer Learning Based Ensemble Technique for Automatic Short Answer Grading
Authors: Shourya Roy, Himanshu S. Bhatt, Y. Narahari
Date: 2016-09-16 00:00:00
Tasks: automatic short answer grading, Transfer Learning

Automatic short answer grading (ASAG) techniques are designed to
automatically assess short answers to questions in natural language, having a
length of a few words to a few sentences. Supervised ASAG techniques have been
demonstrated to be effective but suffer from a couple of key practical
limitations. They are greatly reliant on instructor provided model answers and
need labeled training data in the form of graded student answers for every
assessment task. To overcome these, in this paper, we introduce an ASAG
technique with two novel features. We propose an iterative technique on an
ensemble of (a) a text classifier of student answers and (b) a classifier using
numeric features derived from various similarity measures with respect to model
answers. Second, we employ canonical correlation analysis based transfer
learning on a common feature representation to build the classifier ensemble
for questions having no labelled data. The proposed technique handsomely beats
all winning supervised entries on the SCIENTSBANK dataset from the Student
Response Analysis task of SemEval 2013. Additionally, we demonstrate
generalizability and benefits of the proposed technique through evaluation on
multiple ASAG datasets from different subject topics and standards.
TextBoxes: A Fast Text Detector with a Single Deep Neural Network
Authors: Minghui Liao, Baoguang Shi, Xiang Bai, Xinggang Wang, Wenyu Liu
Date: 2016-11-21 00:00:00

This paper presents an end-to-end trainable fast scene text detector, named
TextBoxes, which detects scene text with both high accuracy and efficiency in a
single network forward pass, involving no post-process except for a standard
non-maximum suppression. TextBoxes outperforms competing methods in terms of
text localization accuracy and is much faster, taking only 0.09s per image in a
fast implementation. Furthermore, combined with a text recognizer, TextBoxes
significantly outperforms state-of-the-art approaches on word spotting and
end-to-end text recognition tasks.
Effective Deterministic Initialization for $k$-Means-Like Methods via Local Density Peaks Searching
Authors: Fengfu Li, Hong Qiao, Bo Zhang
Date: 2016-11-21 00:00:00
Tasks: Clustering, Object Categorization

The $k$-means clustering algorithm is popular but has the following main
drawbacks: 1) the number of clusters, $k$, needs to be provided by the user in
advance, 2) it can easily reach local minima with randomly selected initial
centers, 3) it is sensitive to outliers, and 4) it can only deal with well
separated hyperspherical clusters. In this paper, we propose a Local Density
Peaks Searching (LDPS) initialization framework to address these issues. The
LDPS framework includes two basic components: one of them is the local density
that characterizes the density distribution of a data set, and the other is the
local distinctiveness index (LDI) which we introduce to characterize how
distinctive a data point is compared with its neighbors. Based on these two
components, we search for the local density peaks which are characterized with
high local densities and high LDIs to deal with 1) and 2). Moreover, we detect
outliers characterized with low local densities but high LDIs, and exclude them
out before clustering begins. Finally, we apply the LDPS initialization
framework to $k$-medoids, which is a variant of $k$-means and chooses data
samples as centers, with diverse similarity measures other than the Euclidean
distance to fix the last drawback of $k$-means. Combining the LDPS
initialization framework with $k$-means and $k$-medoids, we obtain two novel
clustering methods called LDPS-means and LDPS-medoids, respectively.
Experiments on synthetic data sets verify the effectiveness of the proposed
methods, especially when the ground truth of the cluster number $k$ is large.
Further, experiments on several real world data sets, Handwritten Pendigits,
Coil-20, Coil-100 and Olivetti Face Database, illustrate that our methods give
a superior performance than the analogous approaches on both estimating $k$ and
unsupervised object categorization.
Efficient Convolutional Neural Network with Binary Quantization Layer
Authors: Mahdyar Ravanbakhsh, Hossein Mousavi, Moin Nabi, Lucio Marcenaro, Carlo Regazzoni
Date: 2016-11-21 00:00:00
Tasks: Clustering, Image Segmentation, Quantization, Segmentation, Semantic Segmentation

In this paper we introduce a novel method for segmentation that can benefit
from general semantics of Convolutional Neural Network (CNN). Our segmentation
proposes visually and semantically coherent image segments. We use binary
encoding of CNN features to overcome the difficulty of the clustering on the
high-dimensional CNN feature space. These binary encoding can be embedded into
the CNN as an extra layer at the end of the network. This results in real-time
segmentation. To the best of our knowledge our method is the first attempt on
general semantic image segmentation using CNN. All the previous papers were
limited to few number of category of the images (e.g. PASCAL VOC). Experiments
show that our segmentation algorithm outperform the state-of-the-art
non-semantic segmentation methods by a large margin.
Entropy/IP: Uncovering Structure in IPv6 Addresses
Authors: Pawel Foremski, David Plonka, Arthur Berger
Date: 2016-06-14 00:00:00

In this paper, we introduce Entropy/IP: a system that discovers Internet
address structure based on analyses of a subset of IPv6 addresses known to be
active, i.e., training data, gleaned by readily available passive and active
means. The system is completely automated and employs a combination of
information-theoretic and machine learning techniques to probabilistically
model IPv6 addresses. We present results showing that our system is effective
in exposing structural characteristics of portions of the IPv6 Internet address
space populated by active client, service, and router addresses.
  In addition to visualizing the address structure for exploration, the system
uses its models to generate candidate target addresses for scanning. For each
of 15 evaluated datasets, we train on 1K addresses and generate 1M candidates
for scanning. We achieve some success in 14 datasets, finding up to 40% of the
generated addresses to be active. In 11 of these datasets, we find active
network identifiers (e.g., /64 prefixes or `subnets') not seen in training.
Thus, we provide the first evidence that it is practical to discover subnets
and hosts by scanning probabilistically selected areas of the IPv6 address
space not known to contain active hosts a priori.
Crowd Counting by Adapting Convolutional Neural Networks with Side Information
Authors: Di Kang, Debarun Dhar, Antoni B. Chan
Date: 2016-11-21 00:00:00
Tasks: Crowd Counting, Image Deconvolution

Computer vision tasks often have side information available that is helpful
to solve the task. For example, for crowd counting, the camera perspective
(e.g., camera angle and height) gives a clue about the appearance and scale of
people in the scene. While side information has been shown to be useful for
counting systems using traditional hand-crafted features, it has not been fully
utilized in counting systems based on deep learning. In order to incorporate
the available side information, we propose an adaptive convolutional neural
network (ACNN), where the convolutional filter weights adapt to the current
scene context via the side information. In particular, we model the filter
weights as a low-dimensional manifold, parametrized by the side information,
within the high-dimensional space of filter weights. With the help of side
information and adaptive weights, the ACNN can disentangle the variations
related to the side information, and extract discriminative features related to
the current context. Since existing crowd counting datasets do not contain
ground-truth side information, we collect a new dataset with the ground-truth
camera angle and height as the side information. On experiments in crowd
counting, the ACNN improves counting accuracy compared to a plain CNN with a
similar number of parameters. We also apply ACNN to image deconvolution to show
its potential effectiveness on other computer vision applications.
Variational Graph Auto-Encoders
Authors: Thomas N. Kipf, Max Welling
Date: 2016-11-21 00:00:00
Tasks: Decoder, Graph Clustering, Link Prediction, Prediction

We introduce the variational graph auto-encoder (VGAE), a framework for
unsupervised learning on graph-structured data based on the variational
auto-encoder (VAE). This model makes use of latent variables and is capable of
learning interpretable latent representations for undirected graphs. We
demonstrate this model using a graph convolutional network (GCN) encoder and a
simple inner product decoder. Our model achieves competitive results on a link
prediction task in citation networks. In contrast to most existing models for
unsupervised learning on graph-structured data and link prediction, our model
can naturally incorporate node features, which significantly improves
predictive performance on a number of benchmark datasets.
False-Friend Detection and Entity Matching via Unsupervised Transliteration
Authors: Yanqing Chen, Steven Skiena
Date: 2016-11-21 00:00:00
Tasks: Machine Translation, Translation, Transliteration

Transliterations play an important role in multilingual entity reference
resolution, because proper names increasingly travel between languages in news
and social media. Previous work associated with machine translation targets
transliteration only single between language pairs, focuses on specific classes
of entities (such as cities and celebrities) and relies on manual curation,
which limits the expression power of transliteration in multilingual
environment.
  By contrast, we present an unsupervised transliteration model covering 69
major languages that can generate good transliterations for arbitrary strings
between any language pair. Our model yields top-(1, 20, 100) averages of
(32.85%, 60.44%, 83.20%) in matching gold standard transliteration compared to
results from a recently-published system of (26.71%, 50.27%, 72.79%). We also
show the quality of our model in detecting true and false friends from
Wikipedia high frequency lexicons. Our method indicates a strong signal of
pronunciation similarity and boosts the probability of finding true friends in
68 out of 69 languages.
Neural Machine Translation with Reconstruction
Authors: Zhaopeng Tu, Yang Liu, Lifeng Shang, Xiaohua Liu, Hang Li
Date: 2016-11-07 00:00:00
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

Although end-to-end Neural Machine Translation (NMT) has achieved remarkable
progress in the past two years, it suffers from a major drawback: translations
generated by NMT systems often lack of adequacy. It has been widely observed
that NMT tends to repeatedly translate some source words while mistakenly
ignoring other words. To alleviate this problem, we propose a novel
encoder-decoder-reconstructor framework for NMT. The reconstructor,
incorporated into the NMT model, manages to reconstruct the input source
sentence from the hidden layer of the output target sentence, to ensure that
the information in the source side is transformed to the target side as much as
possible. Experiments show that the proposed framework significantly improves
the adequacy of NMT output and achieves superior translation result over
state-of-the-art NMT and statistical MT systems.
Training Sparse Neural Networks
Authors: Suraj Srinivas, Akshayvarun Subramanya, R. Venkatesh Babu
Date: 2016-11-21 00:00:00
Tasks: General Classification, image-classification, Image Classification

Deep neural networks with lots of parameters are typically used for
large-scale computer vision tasks such as image classification. This is a
result of using dense matrix multiplications and convolutions. However, sparse
computations are known to be much more efficient. In this work, we train and
build neural networks which implicitly use sparse computations. We introduce
additional gate variables to perform parameter selection and show that this is
equivalent to using a spike-and-slab prior. We experimentally validate our
method on both small and large networks and achieve state-of-the-art
compression results for sparse neural network models.
Scalable Approximations for Generalized Linear Problems
Authors: Murat A. Erdogdu, Mohsen Bayati, Lee H. Dicker
Date: 2016-11-21 00:00:00
Tasks: Binary Classification, General Classification, regression, Stochastic Optimization

In stochastic optimization, the population risk is generally approximated by
the empirical risk. However, in the large-scale setting, minimization of the
empirical risk may be computationally restrictive. In this paper, we design an
efficient algorithm to approximate the population risk minimizer in generalized
linear problems such as binary classification with surrogate losses and
generalized linear regression models. We focus on large-scale problems, where
the iterative minimization of the empirical risk is computationally
intractable, i.e., the number of observations $n$ is much larger than the
dimension of the parameter $p$, i.e. $n \gg p \gg 1$. We show that under random
sub-Gaussian design, the true minimizer of the population risk is approximately
proportional to the corresponding ordinary least squares (OLS) estimator. Using
this relation, we design an algorithm that achieves the same accuracy as the
empirical risk minimizer through iterations that attain up to a cubic
convergence rate, and that are cheaper than any batch optimization algorithm by
at least a factor of $\mathcal{O}(p)$. We provide theoretical guarantees for
our algorithm, and analyze the convergence behavior in terms of data
dimensions. Finally, we demonstrate the performance of our algorithm on
well-known classification and regression problems, through extensive numerical
studies on large-scale datasets, and show that it achieves the highest
performance compared to several other widely used and specialized optimization
algorithms.
Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring
Authors: Lars Mescheder, Sebastian Nowozin, Andreas Geiger
Date: 2016-11-21 00:00:00
Tasks: Blocking

We present a new notion of probabilistic duality for random variables
involving mixture distributions. Using this notion, we show how to implement a
highly-parallelizable Gibbs sampler for weakly coupled discrete pairwise
graphical models with strictly positive factors that requires almost no
preprocessing and is easy to implement. Moreover, we show how our method can be
combined with blocking to improve mixing. Even though our method leads to
inferior mixing times compared to a sequential Gibbs sampler, we argue that our
method is still very useful for large dynamic networks, where factors are added
and removed on a continuous basis, as it is hard to maintain a graph coloring
in this setup. Similarly, our method is useful for parallelizing Gibbs sampling
in graphical models that do not allow for graph colorings with a small number
of colors such as densely connected graphs.
One Class Splitting Criteria for Random Forests
Authors: Nicolas Goix, Nicolas Drougard, Romain Brault, Maël Chiapino
Date: 2016-11-07 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Classification, General Classification, One-Class Classification, regression

Random Forests (RFs) are strong machine learning tools for classification and
regression. However, they remain supervised algorithms, and no extension of RFs
to the one-class setting has been proposed, except for techniques based on
second-class sampling. This work fills this gap by proposing a natural
methodology to extend standard splitting criteria to the one-class setting,
structurally generalizing RFs to one-class classification. An extensive
benchmark of seven state-of-the-art anomaly detection algorithms is also
presented. This empirically demonstrates the relevance of our approach.
Covariate conscious approach for Gait recognition based upon Zernike moment invariants
Authors: Himanshu Aggarwal, Dinesh K. Vishwakarma
Date: 2016-11-21 00:00:00
Tasks: Gait Recognition

Gait recognition i.e. identification of an individual from his/her walking
pattern is an emerging field. While existing gait recognition techniques
perform satisfactorily in normal walking conditions, there performance tend to
suffer drastically with variations in clothing and carrying conditions. In this
work, we propose a novel covariate cognizant framework to deal with the
presence of such covariates. We describe gait motion by forming a single 2D
spatio-temporal template from video sequence, called Average Energy Silhouette
image (AESI). Zernike moment invariants (ZMIs) are then computed to screen the
parts of AESI infected with covariates. Following this, features are extracted
from Spatial Distribution of Oriented Gradients (SDOGs) and novel Mean of
Directional Pixels (MDPs) methods. The obtained features are fused together to
form the final well-endowed feature set. Experimental evaluation of the
proposed framework on three publicly available datasets i.e. CASIA dataset B,
OU-ISIR Treadmill dataset B and USF Human-ID challenge dataset with recently
published gait recognition approaches, prove its superior performance.
Deep Temporal Linear Encoding Networks
Authors: Ali Diba, Vivek Sharma, Luc van Gool
Date: 2016-11-21 00:00:00
Proceeding: CVPR 2017 7
Tasks: Representation Learning, Video Classification

The CNN-encoding of features from entire videos for the representation of
human actions has rarely been addressed. Instead, CNN work has focused on
approaches to fuse spatial and temporal networks, but these were typically
limited to processing shorter sequences. We present a new video representation,
called temporal linear encoding (TLE) and embedded inside of CNNs as a new
layer, which captures the appearance and motion throughout entire videos. It
encodes this aggregated information into a robust video feature representation,
via end-to-end learning. Advantages of TLEs are: (a) they encode the entire
video into a compact feature representation, learning the semantics and a
discriminative feature space; (b) they are applicable to all kinds of networks
like 2D and 3D CNNs for video classification; and (c) they model feature
interactions in a more expressive way and without loss of information. We
conduct experiments on two challenging human action datasets: HMDB51 and
UCF101. The experiments show that TLE outperforms current state-of-the-art
methods on both datasets.
Estimation of respiratory pattern from video using selective ensemble aggregation
Authors: A. P. Prathosh, Pragathi Praveena, Lalit K. Mestha, Sanjay Bharadwaj
Date: 2016-11-21 00:00:00

Non-contact estimation of respiratory pattern (RP) and respiration rate (RR)
has multiple applications. Existing methods for RP and RR measurement fall into
one of the three categories - (i) estimation through nasal air flow
measurement, (ii) estimation from video-based remote photoplethysmography, and
(iii) estimation by measurement of motion induced by respiration using motion
detectors. These methods, however, require specialized sensors, are
computationally expensive and/or critically depend on selection of a region of
interest (ROI) for processing. In this paper a general framework is described
for estimating a periodic signal driving noisy LTI channels connected in
parallel with unknown dynamics. The method is then applied to derive a
computationally inexpensive method for estimating RP using 2D cameras that does
not critically depend on ROI. Specifically, RP is estimated by imaging the
changes in the reflected light caused by respiration-induced motion. Each
spatial location in the field of view of the camera is modeled as a
noise-corrupted linear time-invariant (LTI) measurement channel with unknown
system dynamics, driven by a single generating respiratory signal. Estimation
of RP is cast as a blind deconvolution problem and is solved through a method
comprising subspace projection and statistical aggregation. Experiments are
carried out on 31 healthy human subjects by generating multiple RPs and
comparing the proposed estimates with simultaneously acquired ground truth from
an impedance pneumograph device. The proposed estimator agrees well with the
ground truth device in terms of correlation measures, despite variability in
clothing pattern, angle of view and ROI.
Ontology Driven Disease Incidence Detection on Twitter
Authors: Mark Abraham Magumba, Peter Nabende
Date: 2016-11-21 00:00:00

In this work we address the issue of generic automated disease incidence
monitoring on twitter. We employ an ontology of disease related concepts and
use it to obtain a conceptual representation of tweets. Unlike previous key
word based systems and topic modeling approaches, our ontological approach
allows us to apply more stringent criteria for determining which messages are
relevant such as spatial and temporal characteristics whilst giving a stronger
guarantee that the resulting models will perform well on new data that may be
lexically divergent. We achieve this by training learners on concepts rather
than individual words. For training we use a dataset containing mentions of
influenza and Listeria and use the learned models to classify datasets
containing mentions of an arbitrary selection of other diseases. We show that
our ontological approach achieves good performance on this task using a variety
of Natural Language Processing Techniques. We also show that word vectors can
be learned directly from our concepts to achieve even better results.
Error analysis of regularized least-square regression with Fredholm kernel
Authors: Yanfang Tao, Peipei Yuan, Biqin Song
Date: 2016-11-21 00:00:00
Tasks: Learning Theory, Prediction, regression

Learning with Fredholm kernel has attracted increasing attention recently
since it can effectively utilize the data information to improve the prediction
performance. Despite rapid progress on theoretical and experimental
evaluations, its generalization analysis has not been explored in learning
theory literature. In this paper, we establish the generalization bound of
least square regularized regression with Fredholm kernel, which implies that
the fast learning rate O(l^{-1}) can be reached under mild capacity conditions.
Simulated examples show that this Fredholm regression algorithm can achieve the
satisfactory prediction performance.
ResFeats: Residual Network Based Features for Image Classification
Authors: Ammar Mahmood, Mohammed Bennamoun, Senjian An, Ferdous Sohel
Date: 2016-11-21 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, image-classification, Image Classification, Image Segmentation, Object, object-detection, Object Detection, Scene Classification, Semantic Segmentation

Deep residual networks have recently emerged as the state-of-the-art
architecture in image segmentation and object detection. In this paper, we
propose new image features (called ResFeats) extracted from the last
convolutional layer of deep residual networks pre-trained on ImageNet. We
propose to use ResFeats for diverse image classification tasks namely, object
classification, scene classification and coral classification and show that
ResFeats consistently perform better than their CNN counterparts on these
classification tasks. Since the ResFeats are large feature vectors, we propose
to use PCA for dimensionality reduction. Experimental results are provided to
show the effectiveness of ResFeats with state-of-the-art classification
accuracies on Caltech-101, Caltech-256 and MLC datasets and a significant
performance improvement on MIT-67 dataset compared to the widely used CNN
features.