pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Wikipedia-based Corpora for Question Answering
Authors: Tomasz Jurczyk, Amit Deshmane, Jinho D. Choi
Date: 2018-01-06 00:00:00
Tasks: Question Answering, Retrieval

This paper gives comprehensive analyses of corpora based on Wikipedia for
several tasks in question answering. Four recent corpora are collected,WikiQA,
SelQA, SQuAD, and InfoQA, and first analyzed intrinsically by contextual
similarities, question types, and answer categories. These corpora are then
analyzed extrinsically by three question answering tasks, answer retrieval,
selection, and triggering. An indexing-based method for the creation of a
silver-standard dataset for answer retrieval using the entire Wikipedia is also
presented. Our analysis shows the uniqueness of these corpora and suggests a
better use of them for statistical question answering learning.
Data Augmentation of Railway Images for Track Inspection
Authors: S. Ritika, Dattaraj Rao
Date: 2018-02-05 00:00:00
Tasks: Data Augmentation

Regular maintenance of all the assets is pivotal for proper functioning of
railway. Manual maintenance can be very cumbersome and leave room for errors.
Track anomalies like vegetation overgrowth, sun kinks affect the track
construct and result in unequal load transfer, imbalanced lateral forces on
tracks which causes further deterioration of tracks and can ultimately result
in derailment of locomotive. Hence there is a need to continuously monitor rail
track health. Track anomalies are rare with the skew as high as one anomaly in
millions of good images. We propose a method to build training data that will
make our algorithms more robust and help us detect real world track issues. The
data augmentation will have a direct effect in making us detect better
anomalies and hence improve time for railroads that is spent in manual
inspection. This paper talks about a real world use case of detecting railway
track defects from a camera mounted on a moving locomotive and tracking their
locations. The camera is engineered to withstand the environment factors on a
moving train and provide a consistent steady image at around 30 frames per
second. An image simulation pipeline of track detection, region of interest
selection, augmenting image for anomalies is implemented. Training images are
simulated for sun kink and vegetation overgrowth. Inception V3 model pretrained
on Imagenet dataset is finetuned for a 2 class classification. For the case of
vegetation overgrowth, the model generalizes well on actual vegetation images,
though it was trained and validated solely on simulated images which might have
different distribution than the actual vegetation. Sun kink classifier can
classify professionally simulated sun kink videos with a precision of 97.5%.
Task-Aware Compressed Sensing with Generative Adversarial Networks
Authors: Maya Kabkab, Pouya Samangouei, Rama Chellappa
Date: 2018-02-05 00:00:00
Tasks: compressed sensing

In recent years, neural network approaches have been widely adopted for
machine learning tasks, with applications in computer vision. More recently,
unsupervised generative models based on neural networks have been successfully
applied to model data distributions via low-dimensional latent spaces. In this
paper, we use Generative Adversarial Networks (GANs) to impose structure in
compressed sensing problems, replacing the usual sparsity constraint. We
propose to train the GANs in a task-aware fashion, specifically for
reconstruction tasks. We also show that it is possible to train our model
without using any (or much) non-compressed data. Finally, we show that the
latent space of the GAN carries discriminative information and can further be
regularized to generate input features for general inference tasks. We
demonstrate the effectiveness of our method on a variety of reconstruction and
classification problems.
Coordinated Exploration in Concurrent Reinforcement Learning
Authors: Maria Dimakopoulou, Benjamin Van Roy
Date: 2018-02-05 00:00:00
Proceeding: ICML 2018 7
Tasks: Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider a team of reinforcement learning agents that concurrently learn
to operate in a common environment. We identify three properties - adaptivity,
commitment, and diversity - which are necessary for efficient coordinated
exploration and demonstrate that straightforward extensions to single-agent
optimistic and posterior sampling approaches fail to satisfy them. As an
alternative, we propose seed sampling, which extends posterior sampling in a
manner that meets these requirements. Simulation results investigate how
per-agent regret decreases as the number of agents grows, establishing
substantial advantages of seed sampling over alternative exploration schemes.
Zero-Shot Kernel Learning
Authors: Hongguang Zhang, Piotr Koniusz
Date: 2018-02-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Attribute, Zero-Shot Learning

In this paper, we address an open problem of zero-shot learning. Its
principle is based on learning a mapping that associates feature vectors
extracted from i.e. images and attribute vectors that describe objects and/or
scenes of interest. In turns, this allows classifying unseen object classes
and/or scenes by matching feature vectors via mapping to a newly defined
attribute vector describing a new class. Due to importance of such a learning
task, there exist many methods that learn semantic, probabilistic, linear or
piece-wise linear mappings. In contrast, we apply well-established kernel
methods to learn a non-linear mapping between the feature and attribute spaces.
We propose an easy learning objective inspired by the Linear Discriminant
Analysis, Kernel-Target Alignment and Kernel Polarization methods that promotes
incoherence. We evaluate performance of our algorithm on the Polynomial as well
as shift-invariant Gaussian and Cauchy kernels. Despite simplicity of our
approach, we obtain state-of-the-art results on several zero-shot learning
datasets and benchmarks including a recent AWA2 dataset.
Scene-centric Joint Parsing of Cross-view Videos
Authors: Hang Qi, Yuanlu Xu, Tao Yuan, Tianfu Wu, Song-Chun Zhu
Date: 2017-09-16 00:00:00
Tasks: Video Understanding

Cross-view video understanding is an important yet under-explored area in
computer vision. In this paper, we introduce a joint parsing framework that
integrates view-centric proposals into scene-centric parse graphs that
represent a coherent scene-centric understanding of cross-view scenes. Our key
observations are that overlapping fields of views embed rich appearance and
geometry correlations and that knowledge fragments corresponding to individual
vision tasks are governed by consistency constraints available in commonsense
knowledge. The proposed joint parsing framework represents such correlations
and constraints explicitly and generates semantic scene-centric parse graphs.
Quantitative experiments show that scene-centric predictions in the parse graph
outperform view-centric predictions.
Dream Formulations and Deep Neural Networks: Humanistic Themes in the Iconology of the Machine-Learned Image
Authors: Emily L. Spratt
Date: 2018-02-05 00:00:00

This paper addresses the interpretability of deep learning-enabled image
recognition processes in computer vision science in relation to theories in art
history and cognitive psychology on the vision-related perceptual capabilities
of humans. Examination of what is determinable about the machine-learned image
in comparison to humanistic theories of visual perception, particularly in
regard to art historian Erwin Panofsky's methodology for image analysis and
psychologist Eleanor Rosch's theory of graded categorization according to
prototypes, finds that there are surprising similarities between the two that
suggest that researchers in the arts and the sciences would have much to
benefit from closer collaborations. Utilizing the examples of Google's
DeepDream and the Machine Learning and Perception Lab at Georgia Tech's
Grad-CAM: Gradient-weighted Class Activation Mapping programs, this study
suggests that a revival of art historical research in iconography and formalism
in the age of AI is essential for shaping the future navigation and
interpretation of all machine-learned images, given the rapid developments in
image recognition technologies.
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks
Authors: Duy H. M. Nguyen, Duy M. Nguyen, Mai T. N. Truong, Thu Nguyen, Khanh T. Tran, Nguyen A. Triet, Pham T. Bao, Binh T. Nguyen
Date: 2018-02-05 00:00:00
Tasks: Gaussian Processes, Skull Stripping

Brain extraction (skull stripping) is a challenging problem in neuroimaging. It is due to the variability in conditions from data acquisition or abnormalities in images, making brain morphology and intensity characteristics changeable and complicated. In this paper, we propose an algorithm for skull stripping in Magnetic Resonance Imaging (MRI) scans, namely ASMCNN, by combining the Active Shape Model (ASM) and Convolutional Neural Network (CNN) for taking full of their advantages to achieve remarkable results. Instead of working with 3D structures, we process 2D image sequences in the sagittal plane. First, we divide images into different groups such that, in each group, shapes and structures of brain boundaries have similar appearances. Second, a modified version of ASM is used to detect brain boundaries by utilizing prior knowledge of each group. Finally, CNN and post-processing methods, including Conditional Random Field (CRF), Gaussian processes, and several special rules are applied to refine the segmentation contours. Experimental results show that our proposed method outperforms current state-of-the-art algorithms by a significant margin in all experiments.
ClassSim: Similarity between Classes Defined by Misclassification Ratios of Trained Classifiers
Authors: Kazuma Arino, Yohei Kikuta
Date: 2018-02-05 00:00:00
Tasks: Classification, General Classification

Deep neural networks (DNNs) have achieved exceptional performances in many
tasks, particularly, in supervised classification tasks. However, achievements
with supervised classification tasks are based on large datasets with
well-separated classes. Typically, real-world applications involve wild
datasets that include similar classes; thus, evaluating similarities between
classes and understanding relations among classes are important. To address
this issue, a similarity metric, ClassSim, based on the misclassification
ratios of trained DNNs is proposed herein. We conducted image recognition
experiments to demonstrate that the proposed method provides better
similarities compared with existing methods and is useful for classification
problems. Source code including all experimental results is available at
https://github.com/karino2/ClassSim/.
Parsimonious Network based on Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis
Authors: Wahyu Caesarendra, Mahardhika Pratama, Tegoeh Tjahjowidodo, Kiet Tieud, Buyung Kosasih
Date: 2018-02-05 00:00:00
Tasks: Prognosis, Time Series, Time Series Analysis

In recent years, the utilization of rotating parts, e.g. bearings and gears,
has been continuously supporting the manufacturing line to produce consistent
output quality. Due to their critical role, the breakdown of these components
might significantly impact the production rate. A proper condition based
monitoring (CBM) is among a few ways to maintain and monitor the rotating
systems. Prognosis, as one of the major tasks in CBM that predicts and
estimates the remaining useful life of the machine, has attracted significant
interest in decades. This paper presents a literature review on prognosis
approaches from published papers in the last decade. The prognostic approaches
are described comprehensively to provide a better idea on how to select an
appropriate prognosis method for specific needs. An advanced predictive
analytics, namely Parsimonious Network Based on Fuzzy Inference System
(PANFIS), was proposed and tested into the low speed slew bearing data. PANFIS
differs itself from conventional prognostic approaches in which it supports for
online lifelong prognostics without the requirement of retraining or
reconfiguration phase. The method is applied to normal-to-failure bearing
vibration data collected for 139 days and to predict the time-domain features
of vibration slew bearing signals. The performance of the proposed method is
compared to some established methods such as ANFIS, eTS, and Simp_eTS. From the
results, it is suggested that PANFIS offers outstanding performance compared to
those of other methods.
A Multiresolution Convolutional Neural Network with Partial Label Training for Annotating Reflectance Confocal Microscopy Images of Skin
Authors: Alican Bozkurt, Kivanc Kose, Christi Alessi-Fox, Melissa Gill, Dana H. Brooks, Jennifer G. Dy, Milind Rajadhyaksha
Date: 2018-02-05 00:00:00
Tasks: Diagnostic, Specificity

We describe a new multiresolution "nested encoder-decoder" convolutional
network architecture and use it to annotate morphological patterns in
reflectance confocal microscopy (RCM) images of human skin for aiding cancer
diagnosis. Skin cancers are the most common types of cancers, melanoma being
the deadliest among them. RCM is an effective, non-invasive pre-screening tool
for skin cancer diagnosis, with the required cellular resolution. However,
images are complex, low-contrast, and highly variable, so that clinicians
require months to years of expert-level training to be able to make accurate
assessments. In this paper, we address classifying 4 key clinically important
structural/textural patterns in RCM images. The occurrence and morphology of
these patterns are used by clinicians for diagnosis of melanomas. The large
size of RCM images, the large variance of pattern size, the large-scale range
over which patterns appear, the class imbalance in collected images, and the
lack of fully-labeled images all make this a challenging problem to address,
even with automated machine learning tools. We designed a novel nested U-net
architecture to cope with these challenges, and a selective loss function to
handle partial labeling. Trained and tested on 56 melanoma-suspicious,
partially labeled, 12k x 12k pixel images, our network automatically annotated
diagnostic patterns with high sensitivity and specificity, providing consistent
labels for unlabeled sections of the test images. Providing such annotation
will aid clinicians in achieving diagnostic accuracy, and perhaps more
important, dramatically facilitate clinical training, thus enabling much more
rapid adoption of RCM into widespread clinical use process. In addition, our
adaptation of U-net architecture provides an intrinsically multiresolution deep
network that may be useful in other challenging biomedical image analysis
applications.
Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models
Authors: Yifan Peng, Anthony Rios, Ramakanth Kavuluru, Zhiyong Lu
Date: 2018-02-05 00:00:00
Tasks: Relation, Relation Extraction

Text mining the relations between chemicals and proteins is an increasingly
important task. The CHEMPROT track at BioCreative VI aims to promote the
development and evaluation of systems that can automatically detect the
chemical-protein relations in running text (PubMed abstracts). This manuscript
describes our submission, which is an ensemble of three systems, including a
Support Vector Machine, a Convolutional Neural Network, and a Recurrent Neural
Network. Their output is combined using a decision based on majority voting or
stacking. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall
for an f-score of 0.6410, demonstrating the effectiveness of machine
learning-based approaches for automatic relation extraction from biomedical
literature. Our submission achieved the highest performance in the task during
the 2017 challenge.
Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces
Authors: Rakesh Katuwal, P. N. Suganthan
Date: 2018-02-05 00:00:00
Tasks: General Classification, Multi-class Classification

Both neural networks and decision trees are popular machine learning methods
and are widely used to solve problems from diverse domains. These two
classifiers are commonly used base classifiers in an ensemble framework. In
this paper, we first present a new variant of oblique decision tree based on a
linear classifier, then construct an ensemble classifier based on the fusion of
a fast neural network, random vector functional link network and oblique
decision trees. Random Vector Functional Link Network has an elegant closed
form solution with extremely short training time. The neural network partitions
each training bag (obtained using bagging) at the root level into C subsets
where C is the number of classes in the dataset and subsequently, C oblique
decision trees are trained on such partitions. The proposed method provides a
rich insight into the data by grouping the confusing or hard to classify
samples for each class and thus, provides an opportunity to employ fine-grained
classification rule over the data. The performance of the ensemble classifier
is evaluated on several multi-class datasets where it demonstrates a superior
performance compared to other state-of- the-art classifiers.
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees
Authors: AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang
Date: 2018-02-05 00:00:00
Tasks: Causal Inference

A directed acyclic graph (DAG) is the most common graphical model for
representing causal relationships among a set of variables. When restricted to
using only observational data, the structure of the ground truth DAG is
identifiable only up to Markov equivalence, based on conditional independence
relations among the variables. Therefore, the number of DAGs equivalent to the
ground truth DAG is an indicator of the causal complexity of the underlying
structure--roughly speaking, it shows how many interventions or how much
additional information is further needed to recover the underlying DAG. In this
paper, we propose a new technique for counting the number of DAGs in a Markov
equivalence class. Our approach is based on the clique tree representation of
chordal graphs. We show that in the case of bounded degree graphs, the proposed
algorithm is polynomial time. We further demonstrate that this technique can be
utilized for uniform sampling from a Markov equivalence class, which provides a
stochastic way to enumerate DAGs in the equivalence class and may be needed for
finding the best DAG or for causal inference given the equivalence class as
input. We also extend our counting and sampling method to the case where prior
knowledge about the underlying DAG is available, and present applications of
this extension in causal experiment design and estimating the causal effect of
joint interventions.
Face Destylization
Authors: Fatemeh Shiri, Xin Yu, Fatih Porikli, Piotr Koniusz
Date: 2018-02-05 00:00:00
Tasks: Style Transfer

Numerous style transfer methods which produce artistic styles of portraits
have been proposed to date. However, the inverse problem of converting the
stylized portraits back into realistic faces is yet to be investigated
thoroughly. Reverting an artistic portrait to its original photo-realistic face
image has potential to facilitate human perception and identity analysis. In
this paper, we propose a novel Face Destylization Neural Network (FDNN) to
restore the latent photo-realistic faces from the stylized ones. We develop a
Style Removal Network composed of convolutional, fully-connected and
deconvolutional layers. The convolutional layers are designed to extract facial
components from stylized face images. Consecutively, the fully-connected layer
transfers the extracted feature maps of stylized images into the corresponding
feature maps of real faces and the deconvolutional layers generate real faces
from the transferred feature maps. To enforce the destylized faces to be
similar to authentic face images, we employ a discriminative network, which
consists of convolutional and fully connected layers. We demonstrate the
effectiveness of our network by conducting experiments on an extensive set of
synthetic images. Furthermore, we illustrate our network can recover faces from
stylized portraits and real paintings for which the stylized data was
unavailable during the training phase.
Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques
Authors: Xi Chen, Xiao Wang, Jianhua Xuan
Date: 2018-02-05 00:00:00
Tasks: Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking

It is an important task to reliably detect and track multiple moving objects
for video surveillance and monitoring. However, when occlusion occurs in
nonlinear motion scenarios, many existing methods often fail to continuously
track multiple moving objects of interest. In this paper we propose an
effective approach for detection and tracking of multiple moving objects with
occlusion. Moving targets are initially detected using a simple yet efficient
block matching technique, providing rough location information for multiple
object tracking. More accurate location information is then estimated for each
moving object by a nonlinear tracking algorithm. Considering the ambiguity
caused by the occlusion among multiple moving objects, we apply an unscented
Kalman filtering (UKF) technique for reliable object detection and tracking.
Different from conventional Kalman filtering (KF), which cannot achieve the
optimal estimation in nonlinear tracking scenarios, UKF can be used to track
both linear and nonlinear motions due to the unscented transform. Further, it
estimates the velocity information for each object to assist to the object
detection algorithm, effectively delineating multiple moving objects of
occlusion. The experimental results demonstrate that the proposed method can
correctly detect and track multiple moving objects with nonlinear motion
patterns and occlusions.
Learning Deep Matrix Representations
Authors: Kien Do, Truyen Tran, Svetha Venkatesh
Date: 2017-03-04 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Face Reconstruction, General Classification, Node Classification

We present a new distributed representation in deep neural nets wherein the
information is represented in native form as a matrix. This differs from
current neural architectures that rely on vector representations. We consider
matrices as central to the architecture and they compose the input, hidden and
output layers. The model representation is more compact and elegant -- the
number of parameters grows only with the largest dimension of the incoming
layer rather than the number of hidden units. We derive several new deep
networks: (i) feed-forward nets that map an input matrix into an output matrix,
(ii) recurrent nets which map a sequence of input matrices into a sequence of
output matrices. We also reinterpret existing models for (iii) memory-augmented
networks and (iv) graphs using matrix notations. For graphs we demonstrate how
the new notations lead to simple but effective extensions with multiple
attentions. Extensive experiments on handwritten digits recognition, face
reconstruction, sequence to sequence learning, EEG classification, and
graph-based node classification demonstrate the efficacy and compactness of the
matrix architectures.