pwc-archive/papers-with-abstracts huggingface dataset:
A Visual Attention Grounding Neural Model for Multimodal Machine Translation
Authors: Mingyang Zhou, Runxiang Cheng, Yong Jae Lee, Zhou Yu
Date: 2018-08-24 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Multimodal Machine Translation, Translation

We introduce a novel multimodal machine translation model that utilizes
parallel visual and textual information. Our model jointly optimizes the
learning of a shared visual-language embedding and a translator. The model
leverages a visual attention grounding mechanism that links the visual
semantics with the corresponding textual semantics. Our approach achieves
competitive state-of-the-art results on the Multi30K and the Ambiguous COCO
datasets. We also collected a new multilingual multimodal product description
dataset to simulate a real-world international online shopping scenario. On
this dataset, our visual attention grounding model outperforms other methods by
a large margin.
Adaptive Grey-Box Fuzz-Testing with Thompson Sampling
Authors: Siddharth Karamcheti, Gideon Mann, David Rosenberg
Date: 2018-08-24 00:00:00
Tasks: Thompson Sampling

Fuzz testing, or "fuzzing," refers to a widely deployed class of techniques
for testing programs by generating a set of inputs for the express purpose of
finding bugs and identifying security flaws. Grey-box fuzzing, the most popular
fuzzing strategy, combines light program instrumentation with a data driven
process to generate new program inputs. In this work, we present a machine
learning approach that builds on AFL, the preeminent grey-box fuzzer, by
adaptively learning a probability distribution over its mutation operators on a
program-specific basis. These operators, which are selected uniformly at random
in AFL and mutational fuzzers in general, dictate how new inputs are generated,
a core part of the fuzzer's efficacy. Our main contributions are two-fold:
First, we show that a sampling distribution over mutation operators estimated
from training programs can significantly improve performance of AFL. Second, we
introduce a Thompson Sampling, bandit-based optimization approach that
fine-tunes the mutator distribution adaptively, during the course of fuzzing an
individual program. A set of experiments across complex programs demonstrates
that tuning the mutational operator distribution generates sets of inputs that
yield significantly higher code coverage and finds more crashes faster and more
reliably than both baseline versions of AFL as well as other AFL-based learning
approaches.
Using Apple Machine Learning Algorithms to Detect and Subclassify Non-Small Cell Lung Cancer
Authors: Andrew A. Borkowski, Catherine P. Wilson, Steven A. Borkowski, Lauren A. Deland, Stephen M. Mastorides
Date: 2018-08-24 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic

Lung cancer continues to be a major healthcare challenge with high morbidity
and mortality rates among both men and women worldwide. The majority of lung
cancer cases are of non-small cell lung cancer type. With the advent of
targeted cancer therapy, it is imperative not only to properly diagnose but
also sub-classify non-small cell lung cancer. In our study, we evaluated the
utility of using Apple Create ML module to detect and sub-classify non-small
cell carcinomas based on histopathological images. After module optimization,
the program detected 100% of non-small cell lung cancer images and successfully
subclassified the majority of the images. Trained modules, such as ours, can be
utilized in diagnostic smartphone-based applications, augmenting diagnostic
services in understaffed areas of the world.
Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI
Authors: Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang, Pamela Ventola, James S. Duncan
Date: 2018-08-23 00:00:00
Tasks: Decision Making, Deep Learning

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder.
Finding the biomarkers associated with ASD is extremely helpful to understand
the underlying roots of the disorder and can lead to earlier diagnosis and more
targeted treatment. Although Deep Neural Networks (DNNs) have been applied in
functional magnetic resonance imaging (fMRI) to identify ASD, understanding the
data-driven computational decision making procedure has not been previously
explored. Therefore, in this work, we address the problem of interpreting
reliable biomarkers associated with identifying ASD; specifically, we propose a
2-stage method that classifies ASD and control subjects using fMRI images and
interprets the saliency features activated by the classifier. First, we trained
an accurate DNN classifier. Then, for detecting the biomarkers, different from
the DNN visualization works in computer vision, we take advantage of the
anatomical structure of brain fMRI and develop a frequency-normalized sampling
method to corrupt images. Furthermore, in the ASD vs. control subjects
classification scenario, we provide a new approach to detect and characterize
important brain features into three categories. The biomarkers we found by the
proposed method are robust and consistent with previous findings in the
literature. We also validate the detected biomarkers by neurological function
decoding and comparing with the DNN activation maps.
Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images
Authors: Suman Sedai, Dwarikanath Mahapatra, ZongYuan Ge, Rajib Chakravorty, Rahil Garnavi
Date: 2018-08-22 00:00:00
Tasks: General Classification

Localization of chest pathologies in chest X-ray images is a challenging task
because of their varying sizes and appearances. We propose a novel weakly
supervised method to localize chest pathologies using class aware deep
multiscale feature learning. Our method leverages intermediate feature maps
from CNN layers at different stages of a deep network during the training of a
classification model using image level annotations of pathologies. During the
training phase, a set of \emph{layer relevance weights} are learned for each
pathology class and the CNN is optimized to perform pathology classification by
convex combination of feature maps from both shallow and deep layers using the
learned weights. During the test phase, to localize the predicted pathology,
the multiscale attention map is obtained by convex combination of class
activation maps from each stage using the \emph{layer relevance weights}
learned during the training phase. We have validated our method using 112000
X-ray images and compared with the state-of-the-art localization methods. We
experimentally demonstrate that the proposed weakly supervised method can
improve the localization performance of small pathologies such as nodule and
mass while giving comparable performance for bigger pathologies e.g.,
Cardiomegaly
Nuclei Detection Using Mixture Density Networks
Authors: Navid Alemi Koohababni, Mostafa Jahanifar, Ali Gooya, Nasir Rajpoot
Date: 2018-08-22 00:00:00
Tasks: Cell Segmentation, Image-Variation

Nuclei detection is an important task in the histology domain as it is a main
step toward further analysis such as cell counting, cell segmentation, study of
cell connections, etc. This is a challenging task due to the complex texture of
histology image, variation in shape, and touching cells. To tackle these
hurdles, many approaches have been proposed in the literature where deep
learning methods stand on top in terms of performance. Hence, in this paper, we
propose a novel framework for nuclei detection based on Mixture Density
Networks (MDNs). These networks are suitable to map a single input to several
possible outputs and we utilize this property to detect multiple seeds in a
single image patch. A new modified form of a cost function is proposed for
training and handling patches with missing nuclei. The probability maps of the
nuclei in the individual patches are next combined to generate the final
image-wide result. The experimental results show the state-of-the-art
performance on complex colorectal adenocarcinoma dataset.
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection
Authors: Mahdieh Abbasi, Arezoo Rajabi, Azadeh Sadat Mozafari, Rakesh B. Bobba, Christian Gagne
Date: 2018-08-21 00:00:00

Convolutional Neural Networks (CNNs) significantly improve the
state-of-the-art for many applications, especially in computer vision. However,
CNNs still suffer from a tendency to confidently classify out-distribution
samples from unknown classes into pre-defined known classes. Further, they are
also vulnerable to adversarial examples. We are relating these two issues
through the tendency of CNNs to over-generalize for areas of the input space
not covered well by the training set. We show that a CNN augmented with an
extra output class can act as a simple yet effective end-to-end model for
controlling over-generalization. As an appropriate training set for the extra
class, we introduce two resources that are computationally efficient to obtain:
a representative natural out-distribution set and interpolated in-distribution
samples. To help select a representative natural out-distribution set among
available ones, we propose a simple measurement to assess an out-distribution
set's fitness. We also demonstrate that training such an augmented CNN with
representative out-distribution natural datasets and some interpolated samples
allows it to better handle a wide range of unseen out-distribution samples and
black-box adversarial examples without training it on any adversaries. Finally,
we show that generation of white-box adversarial attacks using our proposed
augmented CNN can become harder, as the attack algorithms have to get around
the rejection regions when generating actual adversaries.
Deep Mask For X-ray Based Heart Disease Classification
Authors: Xupeng Chen, Binbin Shi
Date: 2018-08-19 00:00:00
Tasks: Classification, Diagnostic, General Classification

We build a deep learning model to detect and classify heart disease using $X-ray$. We collect data from several hospitals and public datasets. After preprocess we get 3026 images including disease type VSD, ASD, TOF and normal control. The main problem we have to solve is to enable the network to accurately learn the characteristics of the heart, to ensure the reliability of the network while increasing accuracy. By learning the doctor's diagnostic experience, labeling the image and using tools to extract masks of heart region, we train a U-net to generate a mask to give more attention. It forces the model to focus on the characteristics of the heart region and obtain more reliable results.
Predictive Image Regression for Longitudinal Studies with Missing Data
Authors: Sharmin Pathan, Yi Hong
Date: 2018-08-19 00:00:00
Tasks: Decoder, regression

In this paper, we propose a predictive regression model for longitudinal
images with missing data based on large deformation diffeomorphic metric
mapping (LDDMM) and deep neural networks. Instead of directly predicting image
scans, our model predicts a vector momentum sequence associated with a baseline
image. This momentum sequence parameterizes the original image sequence in the
LDDMM framework and lies in the tangent space of the baseline image, which is
Euclidean. A recurrent network with long term-short memory (LSTM) units encodes
the time-varying changes in the vector-momentum sequence, and a convolutional
neural network (CNN) encodes the baseline image of the vector momenta. Features
extracted by the LSTM and CNN are fed into a decoder network to reconstruct the
vector momentum sequence, which is used for the image sequence prediction by
deforming the baseline image with LDDMM shooting. To handle the missing images
at some time points, we adopt a binary mask to ignore their reconstructions in
the loss calculation. We evaluate our model on synthetically generated images
and the brain MRIs from the OASIS dataset. Experimental results demonstrate the
promising predictions of the spatiotemporal changes in both datasets,
irrespective of large or subtle changes in longitudinal image sequences.
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning
Authors: Jian-Xun Wang, Junji Huang, Lian Duan, Heng Xiao
Date: 2018-08-19 00:00:00
Tasks: BIG-bench Machine Learning, Physics-informed machine learning

Modeled Reynolds stress is a major source of model-form uncertainties in
Reynolds-averaged Navier-Stokes (RANS) simulations. Recently, a
physics-informed machine-learning (PIML) approach has been proposed for
reconstructing the discrepancies in RANS-modeled Reynolds stresses. The merits
of the PIML framework has been demonstrated in several canonical incompressible
flows. However, its performance on high-Mach-number flows is still not clear.
In this work we use the PIML approach to predict the discrepancies in RANS
modeled Reynolds stresses in high-Mach-number flat-plate turbulent boundary
layers by using an existing DNS database. Specifically, the discrepancy
function is first constructed using a DNS training flow and then used to
correct RANS-predicted Reynolds stresses under flow conditions different from
the DNS. The machine-learning technique is shown to significantly improve
RANS-modeled turbulent normal stresses, the turbulent kinetic energy, and the
Reynolds-stress anisotropy. Improvements are consistently observed when
different training datasets are used. Moreover, a high-dimensional
visualization technique and distance metrics are used to provide a priori
assessment of prediction confidence based only on RANS simulations. This study
demonstrates that the PIML approach is a computationally affordable technique
for improving the accuracy of RANS-modeled Reynolds stresses for
high-Mach-number turbulent flows when there is a lack of experiments and
high-fidelity simulations.
Improving Breast Cancer Detection using Symmetry Information with Deep Learning
Authors: Yeman Brhane Hagos, Albert Gubern Merida, Jonas Teuwen
Date: 2018-08-17 00:00:00
Tasks: Breast Cancer Detection, Deep Learning, Medical Image Analysis

Convolutional Neural Networks (CNN) have had a huge success in many areas of
computer vision and medical image analysis. However, there is still an immense
potential for performance improvement in mammogram breast cancer detection
Computer-Aided Detection (CAD) systems by integrating all the information that
the radiologist utilizes, such as symmetry and temporal data. In this work, we
proposed a patch based multi-input CNN that learns symmetrical difference to
detect breast masses. The network was trained on a large-scale dataset of 28294
mammogram images. The performance was compared to a baseline architecture
without symmetry context using Area Under the ROC Curve (AUC) and Competition
Performance Metric (CPM). At candidate level, AUC value of 0.933 with 95%
confidence interval of [0.920, 0.954] was obtained when symmetry information is
incorporated in comparison with baseline architecture which yielded AUC value
of 0.929 with [0.919, 0.947] confidence interval. By incorporating symmetrical
information, although there was no a significant candidate level performance
again (p = 0.111), we have found a compelling result at exam level with CPM
value of 0.733 (p = 0.001). We believe that including temporal data, and adding
benign class to the dataset could improve the detection performance.
An elementary introduction to information geometry
Authors: Frank Nielsen
Date: 2018-08-17 00:00:00
Tasks: Survey

In this survey, we describe the fundamental differential-geometric structures of information manifolds, state the fundamental theorem of information geometry, and illustrate some use cases of these information manifolds in information sciences. The exposition is self-contained by concisely introducing the necessary concepts of differential geometry, but proofs are omitted for brevity.
Probabilistic Model of Object Detection Based on Convolutional Neural Network
Authors: Fang-Qi Li, Xu-Die Ren, Hao-Nan Guo
Date: 2018-08-16 00:00:00
Tasks: object-detection, Object Detection

The combination of a CNN detector and a search framework forms the basis for
local object/pattern detection. To handle the waste of regional information and
the defective compromise between efficiency and accuracy, this paper proposes a
probabilistic model with a powerful search framework. By mapping an image into
a probabilistic distribution of objects, this new model gives more informative
outputs with less computation. The setting and analytic traits are elaborated
in this paper, followed by a series of experiments carried out on FDDB, which
show that the proposed model is sound, efficient and analytic.
Dual approach for object tracking based on optical flow and swarm intelligence
Authors: Rajesh Misra, Kumar S. Ray
Date: 2018-08-15 00:00:00
Tasks: Object, Object Tracking, Optical Flow Estimation

In Computer Vision,object tracking is a very old and complex problem.Though there are several existing algorithms for object tracking, still there are several challenges remain to be solved. For instance, variation of illumination of light, noise, occlusion, sudden start and stop of moving object, shading etc,make the object tracking a complex problem not only for dynamic background but also for static background. In this paper we propose a dual approach for object tracking based on optical flow and swarm Intelligence.The optical flow based KLT(Kanade-Lucas-Tomasi) tracker, tracks the dominant points of the target object from first frame to last frame of a video sequence;whereas swarm Intelligence based PSO (Particle Swarm Optimization) tracker simultaneously tracks the boundary information of the target object from second frame to last frame of the same video sequence.This dual function of tracking makes the trackers very much robust with respect to the above stated problems. The flexibility of our approach is that it can be successfully applicable in variable background as well as static background.We compare the performance of the proposed dual tracking algorithm with several benchmark datasets and obtain very competitive results in general and in most of the cases we obtained superior results using dual tracking algorithm. We also compare the performance of the proposed dual tracker with some existing PSO based algorithms for tracking and achieved better results.
Binary Image Features Proposed to Empower Computer Vision
Authors: Soumi Ray, Vinod Kumar
Date: 2018-08-14 00:00:00

This literature has proposed three fast and easy computable image features to
improve computer vision by offering more human-like vision power. These
features are not based on image pixels absolute or relative intensity; neither
based on shape or colour. So, no complex pixel by pixel calculation is
required. For human eyes, pixel by pixel calculation is like seeing an image
with maximum zoom which is done only when a higher level of details is
required. Normally, first we look at an image to get an overall idea about it
to know whether it deserves further investigation or not. This capacity of
getting an idea at a glance is analysed and three basic features are proposed
to empower computer vision. Potential of proposed features is tested and
established through different medical dataset. Achieved accuracy in
classification demonstrates possibilities and potential of the use of the
proposed features in image processing.
Pyramidal Recurrent Unit for Language Modeling
Authors: Sachin Mehta, Rik Koncel-Kedziorski, Mohammad Rastegari, Hannaneh Hajishirzi
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Modeling, Language Modelling

LSTMs are powerful tools for modeling contextual information, as evidenced by
their success at the task of language modeling. However, modeling contexts in
very high dimensional space can lead to poor generalizability. We introduce the
Pyramidal Recurrent Unit (PRU), which enables learning representations in high
dimensional space with more generalization power and fewer parameters. PRUs
replace the linear transformation in LSTMs with more sophisticated interactions
including pyramidal and grouped linear transformations. This architecture gives
strong results on word-level language modeling while reducing the number of
parameters significantly. In particular, PRU improves the perplexity of a
recent state-of-the-art language model Merity et al. (2018) by up to 1.3 points
while learning 15-20% fewer parameters. For similar number of model parameters,
PRU outperforms all previous RNN models that exploit different gating
mechanisms and transformations. We provide a detailed examination of the PRU
and its behavior on the language modeling tasks. Our code is open-source and
available at https://sacmehta.github.io/PRU/
Convolutional Neural Networks with Recurrent Neural Filters
Authors: Yi Yang
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence, Sentiment Analysis

We introduce a class of convolutional neural networks (CNNs) that utilize
recurrent neural networks (RNNs) as convolution filters. A convolution filter
is typically implemented as a linear affine transformation followed by a
non-linear function, which fails to account for language compositionality. As a
result, it limits the use of high-order filters that are often warranted for
natural language processing tasks. In this work, we model convolution filters
with RNNs that naturally capture compositionality and long-term dependencies in
language. We show that simple CNN architectures equipped with recurrent neural
filters (RNFs) achieve results that are on par with the best published ones on
the Stanford Sentiment Treebank and two answer sentence selection datasets.
Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning
Authors: Shang-Yu Su, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Task-Completion Dialogue Policy Learning

This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving
the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed
framework that extends the Dyna-Q algorithm to integrate planning for
task-completion dialogue policy learning. To obviate DDQ's high dependency on
the quality of simulated experiences, we incorporate an RNN-based discriminator
in D3Q to differentiate simulated experience from real user experience in order
to control the quality of training data. Experiments show that D3Q
significantly outperforms DDQ by controlling the quality of simulated
experience used for planning. The effectiveness and robustness of D3Q is
further demonstrated in a domain extension setting, where the agent's
capability of adapting to a changing environment is tested.
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse
Authors: Manaal Faruqui, Ellie Pavlick, Ian Tenney, Dipanjan Das
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Representation Learning, Sentence

We release a corpus of 43 million atomic edits across 8 languages. These
edits are mined from Wikipedia edit history and consist of instances in which a
human editor has inserted a single contiguous phrase into, or deleted a single
contiguous phrase from, an existing sentence. We use the collected data to show
that the language generated during editing differs from the language that we
observe in standard corpora, and that models trained on edits encode different
aspects of semantics and discourse than models trained on raw, unstructured
text. We release the full corpus as a resource to aid ongoing research in
semantics, discourse, and representation learning.