pwc-archive/papers-with-abstracts huggingface dataset:
A Comparative Analysis of Registration Tools: Traditional vs Deep Learning Approach on High Resolution Tissue Cleared Data
Authors: Abdullah Nazib, Clinton Fookes, Dimitri Perrin
Date: 2018-10-19 00:00:00
Tasks: Deep Learning, Image Registration

Image registration plays an important role in comparing images. It is
particularly important in analyzing medical images like CT, MRI, PET, etc. to
quantify different biological samples, to monitor disease progression and to
fuse different modalities to support better diagnosis. The recent emergence of
tissue clearing protocols enable us to take images at cellular level
resolution. Image registration tools developed for other modalities are
currently unable to manage images of such extreme high resolution. The recent
popularity of deep learning based methods in the computer vision community
justifies a rigorous investigation of deep-learning based methods on tissue
cleared images along with their traditional counterparts. In this paper, we
investigate and compare the performance of a deep learning based registration
method with traditional optimization based methods on samples from
tissue-clearing methods. From the comparative results it is found that a
deep-learning based method outperforms all traditional registration tools in
terms of registration time and has achieved promising registration accuracy.
Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD
Authors: Jianyu Wang, Gauri Joshi
Date: 2018-10-19 00:00:00

Large-scale machine learning training, in particular distributed stochastic
gradient descent, needs to be robust to inherent system variability such as
node straggling and random communication delays. This work considers a
distributed training framework where each worker node is allowed to perform
local model updates and the resulting models are averaged periodically. We
analyze the true speed of error convergence with respect to wall-clock time
(instead of the number of iterations), and analyze how it is affected by the
frequency of averaging. The main contribution is the design of AdaComm, an
adaptive communication strategy that starts with infrequent averaging to save
communication delay and improve convergence speed, and then increases the
communication frequency in order to achieve a low error floor. Rigorous
experiments on training deep neural networks show that AdaComm can take $3
\times$ less time than fully synchronous SGD, and still reach the same final
training loss.
An Efficient Data Retrieval Parallel Reeb Graph Algorithm
Authors: Mustafa Hajij, Paul Rosen
Date: 2018-10-18 00:00:00
Tasks: Retrieval

The Reeb graph of a scalar function defined on a domain gives a topologically meaningful summary of that domain. Reeb graphs have been shown in the past decade to be of great importance in geometric processing, image processing, computer graphics, and computational topology. The demand for analyzing large data sets has increased in the last decade. Hence the parallelization of topological computations needs to be more fully considered. We propose a parallel augmented Reeb graph algorithm on triangulated meshes with and without a boundary. That is, in addition to our parallel algorithm for computing a Reeb graph, we describe a method for extracting the original manifold data from the Reeb graph structure. We demonstrate the running time of our algorithm on standard datasets. As an application, we show how our algorithm can be utilized in mesh segmentation algorithms.
Unsupervised Anomalous Data Space Specification
Authors: Ian J Davis
Date: 2018-10-18 00:00:00
Tasks: Anomaly Detection

Computer algorithms are written with the intent that when run they perform a
useful function. Typically any information obtained is unknown until the
algorithm is run. However, if the behavior of an algorithm can be fully
described by precomputing just once how this algorithm will respond when
executed on any input, this precomputed result provides a complete
specification for all solutions in the problem domain. We apply this idea to a
previous anomaly detection algorithm, and in doing so transform it from one
that merely detects individual anomalies when asked to discover potentially
anomalous values, into an algorithm also capable of generating a complete
specification for those values it would deem to be anomalous. This
specification is derived by examining no more than a small training data, can
be obtained in very small constant time, and is inherently far more useful than
results obtained by repeated execution of this tool. For example, armed with
such a specification one can ask how close an anomaly is to being deemed
normal, and can validate this answer not by exhaustively testing the algorithm
but by examining if the specification so generated is indeed correct. This
powerful idea can be applied to any algorithm whose runtime behavior can be
recovered from its construction and so has wide applicability.
Reduction of Parameter Redundancy in Biaffine Classifiers with Symmetric and Circulant Weight Matrices
Authors: Tomoki Matsuno, Katsuhiko Hayashi, Takahiro Ishihara, Hitoshi Manabe, Yuji Matsumoto
Date: 2018-10-18 00:00:00
Proceeding: PACLIC 2018 12
Tasks: Dependency Parsing

Currently, the biaffine classifier has been attracting attention as a method
to introduce an attention mechanism into the modeling of binary relations. For
instance, in the field of dependency parsing, the Deep Biaffine Parser by Dozat
and Manning has achieved state-of-the-art performance as a graph-based
dependency parser on the English Penn Treebank and CoNLL 2017 shared task. On
the other hand, it is reported that parameter redundancy in the weight matrix
in biaffine classifiers, which has O(n^2) parameters, results in overfitting (n
is the number of dimensions). In this paper, we attempted to reduce the
parameter redundancy by assuming either symmetry or circularity of weight
matrices. In our experiments on the CoNLL 2017 shared task dataset, our model
achieved better or comparable accuracy on most of the treebanks with more than
16% parameter reduction.
Open Vocabulary Learning on Source Code with a Graph-Structured Cache
Authors: Milan Cvitkovic, Badal Singh, Anima Anandkumar
Date: 2018-10-18 00:00:00
Proceeding: ICLR 2019 5
Tasks: Code Completion, Graph Neural Network

Machine learning models that take computer program source code as input typically use Natural Language Processing (NLP) techniques. However, a major challenge is that code is written using an open, rapidly changing vocabulary due to, e.g., the coinage of new variable and method names. Reasoning over such a vocabulary is not something for which most NLP methods are designed. We introduce a Graph-Structured Cache to address this problem; this cache contains a node for each new word the model encounters with edges connecting each word to its occurrences in the code. We find that combining this graph-structured cache strategy with recent Graph-Neural-Network-based models for supervised learning on code improves the models' performance on a code completion task and a variable naming task --- with over $100\%$ relative improvement on the latter --- at the cost of a moderate increase in computation time.
Compositional Verification for Autonomous Systems with Deep Learning Components
Authors: Corina S. Pasareanu, Divya Gopinath, Huafeng Yu
Date: 2018-10-18 00:00:00
Tasks: Autonomous Vehicles, Deep Learning, Recommendation Systems

As autonomy becomes prevalent in many applications, ranging from
recommendation systems to fully autonomous vehicles, there is an increased need
to provide safety guarantees for such systems. The problem is difficult, as
these are large, complex systems which operate in uncertain environments,
requiring data-driven machine-learning components. However, learning techniques
such as Deep Neural Networks, widely used today, are inherently unpredictable
and lack the theoretical foundations to provide strong assurance guarantees. We
present a compositional approach for the scalable, formal verification of
autonomous systems that contain Deep Neural Network components. The approach
uses assume-guarantee reasoning whereby {\em contracts}, encoding the
input-output behavior of individual components, allow the designer to model and
incorporate the behavior of the learning-enabled components working
side-by-side with the other components. We illustrate the approach on an
example taken from the autonomous vehicles domain.
CURE-OR: Challenging Unreal and Real Environments for Object Recognition
Authors: Dogancan Temel, Jinsol Lee, Ghassan AlRegib
Date: 2018-10-18 00:00:00
Tasks: Object, Object Recognition

In this paper, we introduce a large-scale, controlled, and multi-platform
object recognition dataset denoted as Challenging Unreal and Real Environments
for Object Recognition (CURE-OR). In this dataset, there are 1,000,000 images
of 100 objects with varying size, color, and texture that are positioned in
five different orientations and captured using five devices including a webcam,
a DSLR, and three smartphone cameras in real-world (real) and studio (unreal)
environments. The controlled challenging conditions include underexposure,
overexposure, blur, contrast, dirty lens, image noise, resizing, and loss of
color information. We utilize CURE-OR dataset to test recognition APIs-Amazon
Rekognition and Microsoft Azure Computer Vision- and show that their
performance significantly degrades under challenging conditions. Moreover, we
investigate the relationship between object recognition and image quality and
show that objective quality algorithms can estimate recognition performance
under certain photometric challenging conditions. The dataset is publicly
available at https://ghassanalregib.com/cure-or/.
Deep Learning vs. Human Graders for Classifying Severity Levels of Diabetic Retinopathy in a Real-World Nationwide Screening Program
Authors: Paisan Raumviboonsuk, Jonathan Krause, Peranut Chotcomwongse, Rory Sayres, Rajiv Raman, Kasumi Widner, Bilson J L Campana, Sonia Phene, Kornwipa Hemarat, Mongkol Tadarati, Sukhum Silpa-Acha, Jirawut Limwattanayingyong, Chetan Rao, Oscar Kuruvilla, Jesse Jung, Jeffrey Tan, Surapong Orprayoon, Chawawat Kangwanwongpaisan, Ramase Sukulmalpaiboon, Chainarong Luengchaichawang, Jitumporn Fuangkaew, Pipat Kongsap, Lamyong Chualinpha, Sarawuth Saree, Srirat Kawinpanitan, Korntip Mitvongsa, Siriporn Lawanasakol, Chaiyasit Thepchatri, Lalita Wongpichedchai, Greg S. Corrado, Lily Peng, Dale R. Webster
Date: 2018-10-18 00:00:00
Tasks: Deep Learning

Deep learning algorithms have been used to detect diabetic retinopathy (DR)
with specialist-level accuracy. This study aims to validate one such algorithm
on a large-scale clinical population, and compare the algorithm performance
with that of human graders. 25,326 gradable retinal images of patients with
diabetes from the community-based, nation-wide screening program of DR in
Thailand were analyzed for DR severity and referable diabetic macular edema
(DME). Grades adjudicated by a panel of international retinal specialists
served as the reference standard. Across different severity levels of DR for
determining referable disease, deep learning significantly reduced the false
negative rate (by 23%) at the cost of slightly higher false positive rates
(2%). Deep learning algorithms may serve as a valuable tool for DR screening.
Exploring Adversarial Examples in Malware Detection
Authors: Octavian Suciu, Scott E. Coull, Jeffrey Johns
Date: 2018-10-18 00:00:00
Tasks: Feature Engineering, General Classification, Malware Classification, Malware Detection

The convolutional neural network (CNN) architecture is increasingly being
applied to new domains, such as malware detection, where it is able to learn
malicious behavior from raw bytes extracted from executables. These
architectures reach impressive performance with no feature engineering effort
involved, but their robustness against active attackers is yet to be
understood. Such malware detectors could face a new attack vector in the form
of adversarial interference with the classification model. Existing evasion
attacks intended to cause misclassification on test-time instances, which have
been extensively studied for image classifiers, are not applicable because of
the input semantics that prevents arbitrary changes to the binaries. This paper
explores the area of adversarial examples for malware detection. By training an
existing model on a production-scale dataset, we show that some previous
attacks are less effective than initially reported, while simultaneously
highlighting architectural weaknesses that facilitate new attack strategies for
malware classification. Finally, we explore how generalizable different attack
strategies are, the trade-offs when aiming to increase their effectiveness, and
the transferability of single-step attacks.
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
Authors: Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, Yoshua Bengio
Date: 2018-10-18 00:00:00
Proceeding: ICLR 2019 5
Tasks: Grounded language learning

Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts. Here, we introduce the BabyAI research platform to support investigations towards including humans in the loop for grounded language learning. The BabyAI platform comprises an extensible suite of 19 levels of increasing difficulty. The levels gradually lead the agent towards acquiring a combinatorially rich synthetic language which is a proper subset of English. The platform also provides a heuristic expert agent for the purpose of simulating a human teacher. We report baseline results and estimate the amount of human involvement that would be required to train a neural network-based agent on some of the BabyAI levels. We put forward strong evidence that current deep learning methods are not yet sufficiently sample efficient when it comes to learning a language with compositional properties.
Quantile Regression Under Memory Constraint
Authors: Xi Chen, Weidong Liu, Yichen Zhang
Date: 2018-10-18 00:00:00
Tasks: Distributed Computing, quantile regression, regression

This paper studies the inference problem in quantile regression (QR) for a
large sample size $n$ but under a limited memory constraint, where the memory
can only store a small batch of data of size $m$. A natural method is the
na\"ive divide-and-conquer approach, which splits data into batches of size
$m$, computes the local QR estimator for each batch, and then aggregates the
estimators via averaging. However, this method only works when $n=o(m^2)$ and
is computationally expensive. This paper proposes a computationally efficient
method, which only requires an initial QR estimator on a small batch of data
and then successively refines the estimator via multiple rounds of
aggregations. Theoretically, as long as $n$ grows polynomially in $m$, we
establish the asymptotic normality for the obtained estimator and show that our
estimator with only a few rounds of aggregations achieves the same efficiency
as the QR estimator computed on all the data. Moreover, our result allows the
case that the dimensionality $p$ goes to infinity. The proposed method can also
be applied to address the QR problem under distributed computing environment
(e.g., in a large-scale sensor network) or for real-time streaming data.
Large-scale Hierarchical Alignment for Data-driven Text Rewriting
Authors: Nikola I. Nikolov, Richard H. R. Hahnloser
Date: 2018-10-18 00:00:00
Proceeding: RANLP 2019 9
Tasks: Articles, Sentence, Style Transfer, Text Simplification

We propose a simple unsupervised method for extracting pseudo-parallel monolingual sentence pairs from comparable corpora representative of two different text styles, such as news articles and scientific papers. Our approach does not require a seed parallel corpus, but instead relies solely on hierarchical search over pre-trained embeddings of documents and sentences. We demonstrate the effectiveness of our method through automatic and extrinsic evaluation on text simplification from the normal to the Simple Wikipedia. We show that pseudo-parallel sentences extracted with our method not only supplement existing parallel data, but can even lead to competitive performance on their own.
Semantic Integration in the Information Flow Framework
Authors: Robert E. Kent
Date: 2018-10-18 00:00:00
Tasks: Descriptive

The Information Flow Framework (IFF) is a descriptive category metatheory
currently under development, which is being offered as the structural aspect of
the Standard Upper Ontology (SUO). The architecture of the IFF is composed of
metalevels, namespaces and meta-ontologies. The main application of the IFF is
institutional: the notion of institutions and their morphisms are being
axiomatized in the upper metalevels of the IFF, and the lower metalevel of the
IFF has axiomatized various institutions in which semantic integration has a
natural expression as the colimit of theories.
MRI Reconstruction via Cascaded Channel-wise Attention Network
Authors: Qiaoying Huang, Dong Yang, Pengxiang Wu, Hui Qu, Jingru Yi, Dimitris Metaxas
Date: 2018-10-18 00:00:00
Tasks: compressed sensing, MRI Reconstruction

We consider an MRI reconstruction problem with input of k-space data at a
very low undersampled rate. This can practically benefit patient due to reduced
time of MRI scan, but it is also challenging since quality of reconstruction
may be compromised. Currently, deep learning based methods dominate MRI
reconstruction over traditional approaches such as Compressed Sensing, but they
rarely show satisfactory performance in the case of low undersampled k-space
data. One explanation is that these methods treat channel-wise features
equally, which results in degraded representation ability of the neural
network. To solve this problem, we propose a new model called MRI Cascaded
Channel-wise Attention Network (MICCAN), highlighted by three components: (i) a
variant of U-net with Channel-wise Attention (UCA) module, (ii) a long skip
connection and (iii) a combined loss. Our model is able to attend to salient
information by filtering irrelevant features and also concentrate on
high-frequency information by enforcing low-frequency information bypassed to
the final output. We conduct both quantitative evaluation and qualitative
analysis of our method on a cardiac dataset. The experiment shows that our
method achieves very promising results in terms of three common metrics on the
MRI reconstruction with low undersampled k-space data.
Micro-Browsing Models for Search Snippets
Authors: Muhammad Asiful Islam, Ramakrishnan Srikant, Sugato Basu
Date: 2018-10-18 00:00:00

Click-through rate (CTR) is a key signal of relevance for search engine
results, both organic and sponsored. CTR of a result has two core components:
(a) the probability of examination of a result by a user, and (b) the perceived
relevance of the result given that it has been examined by the user. There has
been considerable work on user browsing models, to model and analyze both the
examination and the relevance components of CTR. In this paper, we propose a
novel formulation: a micro-browsing model for how users read result snippets.
The snippet text of a result often plays a critical role in the perceived
relevance of the result. We study how particular words within a line of snippet
can influence user behavior. We validate this new micro-browsing user model by
considering the problem of predicting which snippet will yield higher CTR, and
show that classification accuracy is dramatically higher with our
micro-browsing user model. The key insight in this paper is that varying
relatively few words within a snippet, and even their location within a
snippet, can have a significant influence on the clickthrough of a snippet.
Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows
Authors: Nils Thuerey, Konstantin Weissenow, Lukas Prantl, Xiangyu Hu
Date: 2018-10-18 00:00:00

With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and evaluate a large number of trained neural networks with respect to their accuracy for the calculation of pressure and velocity distributions. In particular, we illustrate how training data size and the number of weights influence the accuracy of the solutions. With our best models we arrive at a mean relative pressure and velocity error of less than 3% across a range of previously unseen airfoil shapes. In addition all source code is publicly available in order to ensure reproducibility and to provide a starting point for researchers interested in deep learning methods for physics problems. While this work focuses on RANS solutions, the neural network architecture and learning setup are very generic, and applicable to a wide range of PDE boundary value problems on Cartesian grids.
EdgeSpeechNets: Highly Efficient Deep Neural Networks for Speech Recognition on the Edge
Authors: Zhong Qiu Lin, Audrey G. Chung, Alexander Wong
Date: 2018-10-18 00:00:00
Tasks: speech-recognition, Speech Recognition

Despite showing state-of-the-art performance, deep learning for speech
recognition remains challenging to deploy in on-device edge scenarios such as
mobile and other consumer devices. Recently, there have been greater efforts in
the design of small, low-footprint deep neural networks (DNNs) that are more
appropriate for edge devices, with much of the focus on design principles for
hand-crafting efficient network architectures. In this study, we explore a
human-machine collaborative design strategy for building low-footprint DNN
architectures for speech recognition through a marriage of human-driven
principled network design prototyping and machine-driven design exploration.
The efficacy of this design strategy is demonstrated through the design of a
family of highly-efficient DNNs (nicknamed EdgeSpeechNets) for
limited-vocabulary speech recognition. Experimental results using the Google
Speech Commands dataset for limited-vocabulary speech recognition showed that
EdgeSpeechNets have higher accuracies than state-of-the-art DNNs (with the best
EdgeSpeechNet achieving ~97% accuracy), while achieving significantly smaller
network sizes (as much as 7.8x smaller) and lower computational cost (as much
as 36x fewer multiply-add operations, 10x lower prediction latency, and 16x
smaller memory footprint on a Motorola Moto E phone), making them very
well-suited for on-device edge voice interface applications.
BCWS: Bilingual Contextual Word Similarity
Authors: Ta-Chung Chi, Ching-Yen Shih, Yun-Nung Chen
Date: 2018-10-21 00:00:00
Tasks: Word Similarity

This paper introduces the first dataset for evaluating English-Chinese
Bilingual Contextual Word Similarity, namely BCWS
(https://github.com/MiuLab/BCWS). The dataset consists of 2,091 English-Chinese
word pairs with the corresponding sentential contexts and their similarity
scores annotated by the human. Our annotated dataset has higher consistency
compared to other similar datasets. We establish several baselines for the
bilingual embedding task to benchmark the experiments. Modeling cross-lingual
sense representations as provided in this dataset has the potential of moving
artificial intelligence from monolingual understanding towards multilingual
understanding.