pwc-archive/papers-with-abstracts huggingface dataset:
Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
Authors: Chen Qin, Jo Schlemper, Jose Caballero, Anthony Price, Joseph V. Hajnal, Daniel Rueckert
Date: 2017-12-05 00:00:00
Tasks: Image Reconstruction, Temporal Sequences

Accelerating the data acquisition of dynamic magnetic resonance imaging (MRI)
leads to a challenging ill-posed inverse problem, which has received great
interest from both the signal processing and machine learning community over
the last decades. The key ingredient to the problem is how to exploit the
temporal correlation of the MR sequence to resolve the aliasing artefact.
Traditionally, such observation led to a formulation of a non-convex
optimisation problem, which were solved using iterative algorithms. Recently,
however, deep learning based-approaches have gained significant popularity due
to its ability to solve general inversion problems. In this work, we propose a
unique, novel convolutional recurrent neural network (CRNN) architecture which
reconstructs high quality cardiac MR images from highly undersampled k-space
data by jointly exploiting the dependencies of the temporal sequences as well
as the iterative nature of the traditional optimisation algorithms. In
particular, the proposed architecture embeds the structure of the traditional
iterative algorithms, efficiently modelling the recurrence of the iterative
reconstruction stages by using recurrent hidden connections over such
iterations. In addition, spatiotemporal dependencies are simultaneously learnt
by exploiting bidirectional recurrent hidden connections across time sequences.
The proposed algorithm is able to learn both the temporal dependency and the
iterative reconstruction process effectively with only a very small number of
parameters, while outperforming current MR reconstruction methods in terms of
computational complexity, reconstruction accuracy and speed.
Elastic-net Regularized High-dimensional Negative Binomial Regression: Consistency and Weak Signals Detection
Authors: Huiming Zhang, Jinzhu Jia
Date: 2017-12-09 00:00:00
Tasks: regression

We study a sparse negative binomial regression (NBR) for count data by showing the non-asymptotic advantages of using the elastic-net estimator. Two types of oracle inequalities are derived for the NBR's elastic-net estimates by using the Compatibility Factor Condition and the Stabil Condition. The second type of oracle inequality is for the random design and can be extended to many $\ell_1 + \ell_2$ regularized M-estimations, with the corresponding empirical process having stochastic Lipschitz properties. We derive the concentration inequality for the suprema empirical processes for the weighted sum of negative binomial variables to show some high--probability events. We apply the method by showing the sign consistency, provided that the nonzero components in the true sparse vector are larger than a proper choice of the weakest signal detection threshold. In the second application, we show the grouping effect inequality with high probability. Third, under some assumptions for a design matrix, we can recover the true variable set with a high probability if the weakest signal detection threshold is large than the turning parameter up to a known constant. Lastly, we briefly discuss the de-biased elastic-net estimator, and numerical studies are given to support the proposal.
Deep Learning Angiography (DLA): Three-dimensional C-arm Cone Beam CT Angiography Using Deep Learning
Authors: Juan C. Montoya, Yinsheng Li, Charles Strother, Guang-Hong Chen
Date: 2018-01-26 00:00:00
Tasks: Anatomy, Deep Learning, General Classification

Background and Purpose: Our purpose was to develop a deep learning
angiography (DLA) method to generate 3D cerebral angiograms from a single
contrast-enhanced acquisition.
  Material and Methods: Under an approved IRB protocol 105 3D-DSA exams were
randomly selected from an internal database. All were acquired using a clinical
system (Axiom Artis zee, Siemens Healthineers) in conjunction with a standard
injection protocol. More than 150 million labeled voxels from 35 subjects were
used for training. A deep convolutional neural network was trained to classify
each image voxel into three tissue types (vasculature, bone and soft tissue).
The trained DLA model was then applied for tissue classification in a
validation cohort of 8 subjects and a final testing cohort consisting of the
remaining 62 subjects. The final vasculature tissue class was used to generate
the 3D-DLA images. To quantify the generalization error of the trained model,
accuracy, sensitivity, precision and F1-scores were calculated for vasculature
classification in relevant anatomy. The 3D-DLA and clinical 3D-DSA images were
subject to a qualitative assessment for the presence of inter-sweep motion
artifacts.
  Results: Vasculature classification accuracy and 95% CI in the testing
dataset was 98.7% ([98.3, 99.1] %). No residual signal from osseous structures
was observed for all 3D-DLA testing cases except for small regions in the otic
capsule and nasal cavity compared to 37% (23/62) of the 3D-DSAs.
  Conclusion: DLA accurately recreated the vascular anatomy of the 3D-DSA
reconstructions without mask. DLA reduced mis-registration artifacts induced by
inter-sweep motion. DLA reduces radiation exposure required to obtain
clinically useful 3D-DSA
Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
Authors: Stefanos Angelidis, Mirella Lapata
Date: 2017-11-27 00:00:00
Proceeding: TACL 2018 1
Tasks: Multiple Instance Learning, Sentence, Sentiment Analysis

We consider the task of fine-grained sentiment analysis from the perspective
of multiple instance learning (MIL). Our neural model is trained on document
sentiment labels, and learns to predict the sentiment of text segments, i.e.
sentences or elementary discourse units (EDUs), without segment-level
supervision. We introduce an attention-based polarity scoring method for
identifying positive and negative text snippets and a new dataset which we call
SPOT (as shorthand for Segment-level POlariTy annotations) for evaluating
MIL-style sentiment models like ours. Experimental results demonstrate superior
performance against multiple baselines, whereas a judgement elicitation study
shows that EDU-level opinion extraction produces more informative summaries
than sentence-based alternatives.
FlashRL: A Reinforcement Learning Platform for Flash Games
Authors: Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Date: 2018-01-26 00:00:00
Tasks: CPU, Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) is a research area that has blossomed
tremendously in recent years and has shown remarkable potential in among others
successfully playing computer games. However, there only exists a few game
platforms that provide diversity in tasks and state-space needed to advance RL
algorithms. The existing platforms offer RL access to Atari- and a few
web-based games, but no platform fully expose access to Flash games. This is
unfortunate because applying RL to Flash games have potential to push the
research of RL algorithms.
  This paper introduces the Flash Reinforcement Learning platform (FlashRL)
which attempts to fill this gap by providing an environment for thousands of
Flash games on a novel platform for Flash automation. It opens up easy
experimentation with RL algorithms for Flash games, which has previously been
challenging. The platform shows excellent performance with as little as 5% CPU
utilization on consumer hardware. It shows promising results for novel
reinforcement learning algorithms.
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
Authors: Keith Feldman, Louis Faust, Xian Wu, Chao Huang, Nitesh V. Chawla
Date: 2017-06-01 00:00:00
Tasks: BIG-bench Machine Learning

From medical charts to national census, healthcare has traditionally operated
under a paper-based paradigm. However, the past decade has marked a long and
arduous transformation bringing healthcare into the digital age. Ranging from
electronic health records, to digitized imaging and laboratory reports, to
public health datasets, today, healthcare now generates an incredible amount of
digital information. Such a wealth of data presents an exciting opportunity for
integrated machine learning solutions to address problems across multiple
facets of healthcare practice and administration. Unfortunately, the ability to
derive accurate and informative insights requires more than the ability to
execute machine learning models. Rather, a deeper understanding of the data on
which the models are run is imperative for their success. While a significant
effort has been undertaken to develop models able to process the volume of data
obtained during the analysis of millions of digitalized patient records, it is
important to remember that volume represents only one aspect of the data. In
fact, drawing on data from an increasingly diverse set of sources, healthcare
data presents an incredibly complex set of attributes that must be accounted
for throughout the machine learning pipeline. This chapter focuses on
highlighting such challenges, and is broken down into three distinct
components, each representing a phase of the pipeline. We begin with attributes
of the data accounted for during preprocessing, then move to considerations
during model building, and end with challenges to the interpretation of model
output. For each component, we present a discussion around data as it relates
to the healthcare domain and offer insight into the challenges each may impose
on the efficiency of machine learning techniques.
A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction
Authors: Shamil Chollampatt, Hwee Tou Ng
Date: 2018-01-26 00:00:00
Tasks: Decoder, Grammatical Error Correction, Language Modeling, Language Modelling, Translation

We improve automatic correction of grammatical, orthographic, and collocation
errors in text using a multilayer convolutional encoder-decoder neural network.
The network is initialized with embeddings that make use of character N-gram
information to better suit this task. When evaluated on common benchmark test
data sets (CoNLL-2014 and JFLEG), our model substantially outperforms all prior
neural approaches on this task as well as strong statistical machine
translation-based systems with neural and task-specific features trained on the
same data. Our analysis shows the superiority of convolutional neural networks
over recurrent neural networks such as long short-term memory (LSTM) networks
in capturing the local context via attention, and thereby improving the
coverage in correcting grammatical errors. By ensembling multiple models, and
incorporating an N-gram language model and edit features via rescoring, our
novel method becomes the first neural approach to outperform the current
state-of-the-art statistical machine translation-based approach, both in terms
of grammaticality and fluency.
Symbol Emergence in Cognitive Developmental Systems: a Survey
Authors: Tadahiro Taniguchi, Emre Ugur, Matej Hoffmann, Lorenzo Jamone, Takayuki Nagai, Benjamin Rosman, Toshihiko Matsuka, Naoto Iwahashi, Erhan Oztop, Justus Piater, Florentin Wörgötter
Date: 2018-01-26 00:00:00
Tasks: Survey

Humans use signs, e.g., sentences in a spoken language, for communication and
thought. Hence, symbol systems like language are crucial for our communication
with other agents and adaptation to our real-world environment. The symbol
systems we use in our human society adaptively and dynamically change over
time. In the context of artificial intelligence (AI) and cognitive systems, the
symbol grounding problem has been regarded as one of the central problems
related to {\it symbols}. However, the symbol grounding problem was originally
posed to connect symbolic AI and sensorimotor information and did not consider
many interdisciplinary phenomena in human communication and dynamic symbol
systems in our society, which semiotics considered. In this paper, we focus on
the symbol emergence problem, addressing not only cognitive dynamics but also
the dynamics of symbol systems in society, rather than the symbol grounding
problem. We first introduce the notion of a symbol in semiotics from the
humanities, to leave the very narrow idea of symbols in symbolic AI.
Furthermore, over the years, it became more and more clear that symbol
emergence has to be regarded as a multifaceted problem. Therefore, secondly, we
review the history of the symbol emergence problem in different fields,
including both biological and artificial systems, showing their mutual
relations. We summarize the discussion and provide an integrative viewpoint and
comprehensive overview of symbol emergence in cognitive systems. Additionally,
we describe the challenges facing the creation of cognitive systems that can be
part of symbol emergence systems.
DiracNets: Training Very Deep Neural Networks Without Skip-Connections
Authors: Sergey Zagoruyko, Nikos Komodakis
Date: 2017-06-01 00:00:00
Tasks: image-classification, Image Classification

Deep neural networks with skip-connections, such as ResNet, show excellent
performance in various image classification benchmarks. It is though observed
that the initial motivation behind them - training deeper networks - does not
actually hold true, and the benefits come from increased capacity, rather than
from depth. Motivated by this, and inspired from ResNet, we propose a simple
Dirac weight parameterization, which allows us to train very deep plain
networks without explicit skip-connections, and achieve nearly the same
performance. This parameterization has a minor computational cost at training
time and no cost at all at inference, as both Dirac parameterization and batch
normalization can be folded into convolutional filters, so that network becomes
a simple chain of convolution-ReLU pairs. We are able to match ResNet-1001
accuracy on CIFAR-10 with 28-layer wider plain DiracNet, and closely match
ResNets on ImageNet. Our parameterization also mostly eliminates the need of
careful initialization in residual and non-residual networks. The code and
models for our experiments are available at
https://github.com/szagoruyko/diracnets
Multivariate normal mixture modeling, clustering and classification with the rebmix package
Authors: Marko Nagode
Date: 2018-01-26 00:00:00
Tasks: Clustering, General Classification

The rebmix package provides R functions for random univariate and
multivariate finite mixture model generation, estimation, clustering and
classification. The paper is focused on multivariate normal mixture models with
unrestricted variance-covariance matrices. The objective is to show how to
generate datasets for a known number of components, numbers of observations and
component parameters, how to estimate the number of components, component
weights and component parameters and how to predict cluster and class
membership based upon a model trained by the REBMIX algorithm. The accompanying
plotting, bootstrapping and other features of the package are dealt with, too.
For demonstration purpose a multivariate normal dataset with unrestricted
variance-covariance matrices is studied.
KRISM --- Krylov Subspace-based Optical Computing of Hyperspectral Images
Authors: Vishwanath Saragadam, Aswin C. Sankaranarayanan
Date: 2018-01-26 00:00:00

We present an adaptive imaging technique that optically computes a low-rank approximation of a scene's hyperspectral image, conceptualized as a matrix. Central to the proposed technique is the optical implementation of two measurement operators: a spectrally-coded imager and a spatially-coded spectrometer. By iterating between the two operators, we show that the top singular vectors and singular values of a hyperspectral image can be adaptively and optically computed with only a few iterations. We present an optical design that uses pupil plane coding for implementing the two operations and show several compelling results using a lab prototype to demonstrate the effectiveness of the proposed hyperspectral imager.
Time Series Segmentation through Automatic Feature Learning
Authors: Wei-Han Lee, Jorge Ortiz, Bongjun Ko, Ruby Lee
Date: 2018-01-16 00:00:00
Tasks: Activity Recognition, EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

Internet of things (IoT) applications have become increasingly popular in
recent years, with applications ranging from building energy monitoring to
personal health tracking and activity recognition. In order to leverage these
data, automatic knowledge extraction - whereby we map from observations to
interpretable states and transitions - must be done at scale. As such, we have
seen many recent IoT data sets include annotations with a human expert
specifying states, recorded as a set of boundaries and associated labels in a
data sequence. These data can be used to build automatic labeling algorithms
that produce labels as an expert would. Here, we refer to human-specified
boundaries as breakpoints. Traditional changepoint detection methods only look
for statistically-detectable boundaries that are defined as abrupt variations
in the generative parameters of a data sequence. However, we observe that
breakpoints occur on more subtle boundaries that are non-trivial to detect with
these statistical methods. In this work, we propose a new unsupervised
approach, based on deep learning, that outperforms existing techniques and
learns the more subtle, breakpoint boundaries with a high accuracy. Through
extensive experiments on various real-world data sets - including
human-activity sensing data, speech signals, and electroencephalogram (EEG)
activity traces - we demonstrate the effectiveness of our algorithm for
practical applications. Furthermore, we show that our approach achieves
significantly better performance than previous methods.
Safe Exploration in Continuous Action Spaces
Authors: Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerik, Todd Hester, Cosmin Paduraru, Yuval Tassa
Date: 2018-01-26 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration

We address the problem of deploying a reinforcement learning (RL) agent on a
physical system such as a datacenter cooling unit or robot, where critical
constraints must never be violated. We show how to exploit the typically smooth
dynamics of these systems and enable RL algorithms to never violate constraints
during learning. Our technique is to directly add to the policy a safety layer
that analytically solves an action correction formulation per each state. The
novelty of obtaining an elegant closed-form solution is attained due to a
linearized model, learned on past trajectories consisting of arbitrary actions.
This is to mimic the real-world circumstances where data logs were generated
with a behavior policy that is implausible to describe mathematically; such
cases render the known safety-aware off-policy methods inapplicable. We
demonstrate the efficacy of our approach on new representative physics-based
environments, and prevail where reward shaping fails by maintaining zero
constraint violations.
Weakly Supervised Object Detection with Pointwise Mutual Information
Authors: Rene Grzeszick, Sebastian Sudholt, Gernot A. Fink
Date: 2018-01-26 00:00:00
Tasks: Object, object-detection, Object Detection, Weakly Supervised Object Detection

In this work a novel approach for weakly supervised object detection that
incorporates pointwise mutual information is presented. A fully convolutional
neural network architecture is applied in which the network learns one filter
per object class. The resulting feature map indicates the location of objects
in an image, yielding an intuitive representation of a class activation map.
While traditionally such networks are learned by a softmax or binary logistic
regression (sigmoid cross-entropy loss), a learning approach based on a cosine
loss is introduced. A pointwise mutual information layer is incorporated in the
network in order to project predictions and ground truth presence labels in a
non-categorical embedding space. Thus, the cosine loss can be employed in this
non-categorical representation. Besides integrating image level annotations, it
is shown how to integrate point-wise annotations using a Spatial Pyramid
Pooling layer. The approach is evaluated on the VOC2012 dataset for
classification, point localization and weakly supervised bounding box
localization. It is shown that the combination of pointwise mutual information
and a cosine loss eases the learning process and thus improves the accuracy.
The integration of coarse point-wise localizations further improves the results
at minimal annotation costs.
Learning Deep Representations for Word Spotting Under Weak Supervision
Authors: Neha Gurjar, Sebastian Sudholt, Gernot A. Fink
Date: 2017-12-01 00:00:00
Tasks: Word Spotting In Handwritten Documents

Convolutional Neural Networks have made their mark in various fields of
computer vision in recent years. They have achieved state-of-the-art
performance in the field of document analysis as well. However, CNNs require a
large amount of annotated training data and, hence, great manual effort. In our
approach, we introduce a method to drastically reduce the manual annotation
effort while retaining the high performance of a CNN for word spotting in
handwritten documents. The model is learned with weak supervision using a
combination of synthetically generated training data and a small subset of the
training partition of the handwritten data set. We show that the network
achieves results highly competitive to the state-of-the-art in word spotting
with shorter training times and a fraction of the annotation effort.
Spoken English Intelligibility Remediation with PocketSphinx Alignment and Feature Extraction Improves Substantially over the State of the Art
Authors: Yuan Gao, Brij Mohan Lal Srivastava, James Salsman
Date: 2017-09-06 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We use automatic speech recognition to assess spoken English learner
pronunciation based on the authentic intelligibility of the learners' spoken
responses determined from support vector machine (SVM) classifier or deep
learning neural network model predictions of transcription correctness. Using
numeric features produced by PocketSphinx alignment mode and many recognition
passes searching for the substitution and deletion of each expected phoneme and
insertion of unexpected phonemes in sequence, the SVM models achieve 82 percent
agreement with the accuracy of Amazon Mechanical Turk crowdworker
transcriptions, up from 75 percent reported by multiple independent
researchers. Using such features with SVM classifier probability prediction
models can help computer-aided pronunciation teaching (CAPT) systems provide
intelligibility remediation.