pwc-archive/papers-with-abstracts huggingface dataset:
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
Authors: Mohsen Ghafoorian, Alireza Mehrtash, Tina Kapur, Nico Karssemeijer, Elena Marchiori, Mehran Pesteie, Charles R. G. Guttmann, Frank-Erik de Leeuw, Clare M. Tempany, Bram van Ginneken, Andriy Fedorov, Purang Abolmaesumi, Bram Platel, William M. Wells III
Date: 2017-02-25 00:00:00
Tasks: Domain Adaptation, Lesion Segmentation, Medical Image Analysis, Transfer Learning

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis
and treatment. However, variations in MRI acquisition protocols result in
different appearances of normal and diseased tissue in the images.
Convolutional neural networks (CNNs), which have shown to be successful in many
medical image analysis tasks, are typically sensitive to the variations in
imaging protocols. Therefore, in many cases, networks trained on data acquired
with one MRI protocol, do not perform satisfactorily on data acquired with
different protocols. This limits the use of models trained with large annotated
legacy datasets on a new dataset with a different domain which is often a
recurring situation in clinical settings. In this study, we aim to answer the
following central questions regarding domain adaptation in medical image
analysis: Given a fitted legacy model, 1) How much data from the new domain is
required for a decent adaptation of the original network?; and, 2) What portion
of the pre-trained model parameters should be retrained given a certain number
of the new domain training samples? To address these questions, we conducted
extensive experiments in white matter hyperintensity segmentation task. We
trained a CNN on legacy MR images of brain and evaluated the performance of the
domain-adapted network on the same task with images from a different domain. We
then compared the performance of the model to the surrogate scenarios where
either the same trained network is used or a new network is trained from
scratch on the new dataset.The domain-adapted network tuned only by two
training examples achieved a Dice score of 0.63 substantially outperforming a
similar network trained on the same set of examples from scratch.
Dynamic Task Allocation for Crowdsourcing Settings
Authors: Angela Zhou, Irineo Cabreros, Karan Singh
Date: 2017-01-30 00:00:00

We consider the problem of optimal budget allocation for crowdsourcing
problems, allocating users to tasks to maximize our final confidence in the
crowdsourced answers. Such an optimized worker assignment method allows us to
boost the efficacy of any popular crowdsourcing estimation algorithm. We
consider a mutual information interpretation of the crowdsourcing problem,
which leads to a stochastic subset selection problem with a submodular
objective function. We present experimental simulation results which
demonstrate the effectiveness of our dynamic task allocation method for
achieving higher accuracy, possibly requiring fewer labels, as well as
improving upon a previous method which is sensitive to the proportion of users
to questions.
Critical Survey of the Freely Available Arabic Corpora
Authors: Wajdi Zaghouani
Date: 2017-02-25 00:00:00
Tasks: Survey

The availability of corpora is a major factor in building natural language
processing applications. However, the costs of acquiring corpora can prevent
some researchers from going further in their endeavours. The ease of access to
freely available corpora is urgent needed in the NLP research community
especially for language such as Arabic. Currently, there is not easy was to
access to a comprehensive and updated list of freely available Arabic corpora.
We present in this paper, the results of a recent survey conducted to identify
the list of the freely available Arabic corpora and language resources. Our
preliminary results showed an initial list of 66 sources. We presents our
findings in the various categories studied and we provided the direct links to
get the data when possible.
Efficient coordinate-wise leading eigenvector computation
Authors: Jialei Wang, Weiran Wang, Dan Garber, Nathan Srebro
Date: 2017-02-25 00:00:00
Tasks: regression

We develop and analyze efficient "coordinate-wise" methods for finding the
leading eigenvector, where each step involves only a vector-vector product. We
establish global convergence with overall runtime guarantees that are at least
as good as Lanczos's method and dominate it for slowly decaying spectrum. Our
methods are based on combining a shift-and-invert approach with coordinate-wise
algorithms for linear regression.
Towards Unsupervised Weed Scouting for Agricultural Robotics
Authors: David Hall, Feras Dayoub, Jason Kulk, Chris McCool
Date: 2017-02-04 00:00:00
Tasks: Clustering, General Classification, Management

Weed scouting is an important part of modern integrated weed management but
can be time consuming and sparse when performed manually. Automated weed
scouting and weed destruction has typically been performed using classification
systems able to classify a set group of species known a priori. This greatly
limits deployability as classification systems must be retrained for any field
with a different set of weed species present within them. In order to overcome
this limitation, this paper works towards developing a clustering approach to
weed scouting which can be utilized in any field without the need for prior
species knowledge. We demonstrate our system using challenging data collected
in the field from an agricultural robotics platform. We show that considerable
improvements can be made by (i) learning low-dimensional (bottleneck) features
using a deep convolutional neural network to represent plants in general and
(ii) tying views of the same area (plant) together. Deploying this algorithm on
in-field data collected by AgBotII, we are able to successfully cluster cotton
plants from grasses without prior knowledge or training for the specific plants
in the field.
Deep Variational Canonical Correlation Analysis
Authors: Weiran Wang, Xinchen Yan, Honglak Lee, Karen Livescu
Date: 2016-10-11 00:00:00
Tasks: MULTI-VIEW LEARNING

We present deep variational canonical correlation analysis (VCCA), a deep
multi-view learning model that extends the latent variable model interpretation
of linear CCA to nonlinear observation models parameterized by deep neural
networks. We derive variational lower bounds of the data likelihood by
parameterizing the posterior probability of the latent variables from the view
that is available at test time. We also propose a variant of VCCA called
VCCA-private that can, in addition to the "common variables" underlying both
views, extract the "private variables" within each view, and disentangles the
shared and private information for multi-view data without hard supervision.
Experimental results on real-world datasets show that our methods are
competitive across domains.
Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods
Authors: Thai Pham, Steven Lee
Date: 2016-11-12 00:00:00
Tasks: Anomaly Detection, Clustering

The problem of anomaly detection has been studied for a long time. In short,
anomalies are abnormal or unlikely things. In financial networks, thieves and
illegal activities are often anomalous in nature. Members of a network want to
detect anomalies as soon as possible to prevent them from harming the network's
community and integrity. Many Machine Learning techniques have been proposed to
deal with this problem; some results appear to be quite promising but there is
no obvious superior method. In this paper, we consider anomaly detection
particular to the Bitcoin transaction network. Our goal is to detect which
users and transactions are the most suspicious; in this case, anomalous
behavior is a proxy for suspicious behavior. To this end, we use three
unsupervised learning methods including k-means clustering, Mahalanobis
distance, and Unsupervised Support Vector Machine (SVM) on two graphs generated
by the Bitcoin transaction network: one graph has users as nodes, and the other
has transactions as nodes.
Representation of texts as complex networks: a mesoscopic approach
Authors: Henrique F. de Arruda, Filipi N. Silva, Vanessa Q. Marinho, Diego R. Amancio, Luciano da F. Costa
Date: 2016-06-30 00:00:00

Statistical techniques that analyze texts, referred to as text analytics,
have departed from the use of simple word count statistics towards a new
paradigm. Text mining now hinges on a more sophisticated set of methods,
including the representations in terms of complex networks. While
well-established word-adjacency (co-occurrence) methods successfully grasp
syntactical features of written texts, they are unable to represent important
aspects of textual data, such as its topical structure, i.e. the sequence of
subjects developing at a mesoscopic level along the text. Such aspects are
often overlooked by current methodologies. In order to grasp the mesoscopic
characteristics of semantical content in written texts, we devised a network
model which is able to analyze documents in a multi-scale fashion. In the
proposed model, a limited amount of adjacent paragraphs are represented as
nodes, which are connected whenever they share a minimum semantical content. To
illustrate the capabilities of our model, we present, as a case example, a
qualitative analysis of "Alice's Adventures in Wonderland". We show that the
mesoscopic structure of a document, modeled as a network, reveals many semantic
traits of texts. Such an approach paves the way to a myriad of semantic-based
applications. In addition, our approach is illustrated in a machine learning
context, in which texts are classified among real texts and randomized
instances.
Nonparanormal Information Estimation
Authors: Shashank Singh, Barnabás Pøczos
Date: 2017-02-24 00:00:00
Proceeding: ICML 2017 8
Tasks: Mutual Information Estimation

We study the problem of using i.i.d. samples from an unknown multivariate
probability distribution $p$ to estimate the mutual information of $p$. This
problem has recently received attention in two settings: (1) where $p$ is
assumed to be Gaussian and (2) where $p$ is assumed only to lie in a large
nonparametric smoothness class. Estimators proposed for the Gaussian case
converge in high dimensions when the Gaussian assumption holds, but are
brittle, failing dramatically when $p$ is not Gaussian. Estimators proposed for
the nonparametric case fail to converge with realistic sample sizes except in
very low dimensions. As a result, there is a lack of robust mutual information
estimators for many realistic data. To address this, we propose estimators for
mutual information when $p$ is assumed to be a nonparanormal (a.k.a., Gaussian
copula) model, a semiparametric compromise between Gaussian and nonparametric
extremes. Using theoretical bounds and experiments, we show these estimators
strike a practical balance between robustness and scaling with dimensionality.
Rank-to-engage: New Listwise Approaches to Maximize Engagement
Authors: Swayambhoo Jain, Akshay Soni, Nikolay Laptev, Yashar Mehdad
Date: 2017-02-24 00:00:00
Tasks: Articles, Learning-To-Rank

For many internet businesses, presenting a given list of items in an order
that maximizes a certain metric of interest (e.g., click-through-rate, average
engagement time etc.) is crucial. We approach the aforementioned task from a
learning-to-rank perspective which reveals a new problem setup. In traditional
learning-to-rank literature, it is implicitly assumed that during the training
data generation one has access to the \emph{best or desired} order for the
given list of items. In this work, we consider a problem setup where we do not
observe the desired ranking. We present two novel solutions: the first solution
is an extension of already existing listwise learning-to-rank
technique--Listwise maximum likelihood estimation (ListMLE)--while the second
one is a generic machine learning based framework that tackles the problem in
its entire generality. We discuss several challenges associated with this
generic framework, and propose a simple \emph{item-payoff} and
\emph{positional-gain} model that addresses these challenges. We provide
training algorithms, inference procedures, and demonstrate the effectiveness of
the two approaches over traditional ListMLE on synthetic as well as on
real-life setting of ranking news articles for increased dwell time.
Residual Convolutional CTC Networks for Automatic Speech Recognition
Authors: Yisen Wang, Xuejiao Deng, Songbai Pu, Zhiheng Huang
Date: 2017-02-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Deep learning approaches have been widely used in Automatic Speech
Recognition (ASR) and they have achieved a significant accuracy improvement.
Especially, Convolutional Neural Networks (CNNs) have been revisited in ASR
recently. However, most CNNs used in existing work have less than 10 layers
which may not be deep enough to capture all human speech signal information. In
this paper, we propose a novel deep and wide CNN architecture denoted as
RCNN-CTC, which has residual connections and Connectionist Temporal
Classification (CTC) loss function. RCNN-CTC is an end-to-end system which can
exploit temporal and spectral structures of speech signals simultaneously.
Furthermore, we introduce a CTC-based system combination, which is different
from the conventional frame-wise senone-based one. The basic subsystems adopted
in the combination are different types and thus mutually complementary to each
other. Experimental results show that our proposed single system RCNN-CTC can
achieve the lowest word error rate (WER) on WSJ and Tencent Chat data sets,
compared to several widely used neural network systems in ASR. In addition, the
proposed system combination can offer a further error reduction on these two
data sets, resulting in relative WER reductions of $14.91\%$ and $6.52\%$ on
WSJ dev93 and Tencent Chat data sets respectively.
Activation Ensembles for Deep Neural Networks
Authors: Mark Harmon, Diego Klabjan
Date: 2017-02-24 00:00:00

Many activation functions have been proposed in the past, but selecting an
adequate one requires trial and error. We propose a new methodology of
designing activation functions within a neural network at each layer. We call
this technique an "activation ensemble" because it allows the use of multiple
activation functions at each layer. This is done by introducing additional
variables, $\alpha$, at each activation layer of a network to allow for
multiple activation functions to be active at each neuron. By design,
activations with larger $\alpha$ values at a neuron is equivalent to having the
largest magnitude. Hence, those higher magnitude activations are "chosen" by
the network. We implement the activation ensembles on a variety of datasets
using an array of Feed Forward and Convolutional Neural Networks. By using the
activation ensemble, we achieve superior results compared to traditional
techniques. In addition, because of the flexibility of this methodology, we
more deeply explore activation functions and the features that they capture.
Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging
Authors: Yong Xu, Qiuqiang Kong, Qiang Huang, Wenwu Wang, Mark D. Plumbley
Date: 2017-02-24 00:00:00
Tasks: Audio Tagging

Environmental audio tagging is a newly proposed task to predict the presence
or absence of a specific audio event in a chunk. Deep neural network (DNN)
based methods have been successfully adopted for predicting the audio tags in
the domestic audio scene. In this paper, we propose to use a convolutional
neural network (CNN) to extract robust features from mel-filter banks (MFBs),
spectrograms or even raw waveforms for audio tagging. Gated recurrent unit
(GRU) based recurrent neural networks (RNNs) are then cascaded to model the
long-term temporal structure of the audio signal. To complement the input
information, an auxiliary CNN is designed to learn on the spatial features of
stereo recordings. We evaluate our proposed methods on Task 4 (audio tagging)
of the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE
2016) challenge. Compared with our recent DNN-based method, the proposed
structure can reduce the equal error rate (EER) from 0.13 to 0.11 on the
development set. The spatial features can further reduce the EER to 0.10. The
performance of the end-to-end learning on raw waveforms is also comparable.
Finally, on the evaluation set, we get the state-of-the-art performance with
0.12 EER while the performance of the best existing system is 0.15 EER.
Automatic Rule Extraction from Long Short Term Memory Networks
Authors: W. James Murdoch, Arthur Szlam
Date: 2017-02-08 00:00:00
Tasks: Question Answering, Sentiment Analysis

Although deep learning models have proven effective at solving problems in
natural language processing, the mechanism by which they come to their
conclusions is often unclear. As a result, these models are generally treated
as black boxes, yielding no insight of the underlying learned patterns. In this
paper we consider Long Short Term Memory networks (LSTMs) and demonstrate a new
approach for tracking the importance of a given input to the LSTM for a given
output. By identifying consistently important patterns of words, we are able to
distill state of the art LSTMs on sentiment analysis and question answering
into a set of representative phrases. This representation is then
quantitatively validated by using the extracted phrases to construct a simple,
rule-based classifier which approximates the output of the LSTM.
Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying
Authors: Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, Athena Vakali
Date: 2017-02-24 00:00:00

Over the past few years, online aggression and abusive behaviors have
occurred in many different forms and on a variety of platforms. In extreme
cases, these incidents have evolved into hate, discrimination, and bullying,
and even materialized into real-world threats and attacks against individuals
or groups. In this paper, we study the Gamergate controversy. Started in August
2014 in the online gaming world, it quickly spread across various social
networking platforms, ultimately leading to many incidents of cyberbullying and
cyberaggression. We focus on Twitter, presenting a measurement study of a
dataset of 340k unique users and 1.6M tweets to study the properties of these
users, the content they post, and how they differ from random Twitter users. We
find that users involved in this "Twitter war" tend to have more friends and
followers, are generally more engaged and post tweets with negative sentiment,
less joy, and more hate than random users. We also perform preliminary
measurements on how the Twitter suspension mechanism deals with such abusive
behaviors. While we focus on Gamergate, our methodology to collect and analyze
tweets related to aggressive and bullying activities is of independent
interest.
Changing Model Behavior at Test-Time Using Reinforcement Learning
Authors: Augustus Odena, Dieterich Lawson, Christopher Olah
Date: 2017-02-24 00:00:00
Tasks: BIG-bench Machine Learning, Mixture-of-Experts, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

Machine learning models are often used at test-time subject to constraints
and trade-offs not present at training-time. For example, a computer vision
model operating on an embedded device may need to perform real-time inference,
or a translation model operating on a cell phone may wish to bound its average
compute time in order to be power-efficient. In this work we describe a
mixture-of-experts model and show how to change its test-time resource-usage on
a per-input basis using reinforcement learning. We test our method on a small
MNIST-based example.
Studying Positive Speech on Twitter
Authors: Marina Sokolova, Vera Sazonova, Kanyi Huang, Rudraneel Chakraboty, Stan Matwin
Date: 2017-02-24 00:00:00
Tasks: General Classification, Opinion Mining, text-classification, Text Classification

We present results of empirical studies on positive speech on Twitter. By
positive speech we understand speech that works for the betterment of a given
situation, in this case relations between different communities in a
conflict-prone country. We worked with four Twitter data sets. Through
semi-manual opinion mining, we found that positive speech accounted for < 1% of
the data . In fully automated studies, we tested two approaches: unsupervised
statistical analysis, and supervised text classification based on distributed
word representation. We discuss benefits and challenges of those approaches and
report empirical evidence obtained in the study.
Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment
Authors: Aneeq Zia, Yachna Sharma, Vinay Bettadapura, Eric L. Sarin, Irfan Essa
Date: 2017-02-24 00:00:00
Tasks: Skills Assessment, Time Series Analysis

Purpose: Basic surgical skills of suturing and knot tying are an essential
part of medical training. Having an automated system for surgical skills
assessment could help save experts time and improve training efficiency. There
have been some recent attempts at automated surgical skills assessment using
either video analysis or acceleration data. In this paper, we present a novel
approach for automated assessment of OSATS based surgical skills and provide an
analysis of different features on multi-modal data (video and accelerometer
data). Methods: We conduct the largest study, to the best of our knowledge, for
basic surgical skills assessment on a dataset that contained video and
accelerometer data for suturing and knot-tying tasks. We introduce "entropy
based" features - Approximate Entropy (ApEn) and Cross-Approximate Entropy
(XApEn), which quantify the amount of predictability and regularity of
fluctuations in time-series data. The proposed features are compared to
existing methods of Sequential Motion Texture (SMT), Discrete Cosine Transform
(DCT) and Discrete Fourier Transform (DFT), for surgical skills assessment.
Results: We report average performance of different features across all
applicable OSATS criteria for suturing and knot tying tasks. Our analysis shows
that the proposed entropy based features out-perform previous state-of-the-art
methods using video data. For accelerometer data, our method performs better
for suturing only. We also show that fusion of video and acceleration features
can improve overall performance with the proposed entropy features achieving
highest accuracy. Conclusions: Automated surgical skills assessment can be
achieved with high accuracy using the proposed entropy features. Such a system
can significantly improve the efficiency of surgical training in medical
schools and teaching hospitals.