pwc-archive/papers-with-abstracts huggingface dataset:
Cascaded Region-based Densely Connected Network for Event Detection: A Seismic Application
Authors: Yue Wu, Youzuo Lin, Zheng Zhou, David Chas Bolton, Ji Liu, Paul Johnson
Date: 2017-09-12 00:00:00
Tasks: 2D Object Detection, Abnormal Event Detection In Video, Event Detection, object-detection, Object Detection, Time Series, Time Series Analysis

Automatic event detection from time series signals has wide applications,
such as abnormal event detection in video surveillance and event detection in
geophysical data. Traditional detection methods detect events primarily by the
use of similarity and correlation in data. Those methods can be inefficient and
yield low accuracy. In recent years, because of the significantly increased
computational power, machine learning techniques have revolutionized many
science and engineering domains. In this study, we apply a deep-learning-based
method to the detection of events from time series seismic signals. However, a
direct adaptation of the similar ideas from 2D object detection to our problem
faces two challenges. The first challenge is that the duration of earthquake
event varies significantly; The other is that the proposals generated are
temporally correlated. To address these challenges, we propose a novel cascaded
region-based convolutional neural network to capture earthquake events in
different sizes, while incorporating contextual information to enrich features
for each individual proposal. To achieve a better generalization performance,
we use densely connected blocks as the backbone of our network. Because of the
fact that some positive events are not correctly annotated, we further
formulate the detection problem as a learning-from-noise problem. To verify the
performance of our detection methods, we employ our methods to seismic data
generated from a bi-axial "earthquake machine" located at Rock Mechanics
Laboratory, and we acquire labels with the help of experts. Through our
numerical tests, we show that our novel detection techniques yield high
accuracy. Therefore, our novel deep-learning-based detection methods can
potentially be powerful tools for locating events from time series data in
various applications.
VisDA: The Visual Domain Adaptation Challenge
Authors: Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, Kate Saenko
Date: 2017-10-18 00:00:00
Tasks: Domain Adaptation, General Classification, image-classification, Image Classification, Image Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a
large-scale testbed for unsupervised domain adaptation across visual domains.
Unsupervised domain adaptation aims to solve the real-world problem of domain
shift, where machine learning models trained on one domain must be transferred
and adapted to a novel visual domain without additional supervision. The
VisDA2017 challenge is focused on the simulation-to-reality shift and has two
associated tasks: image classification and image segmentation. The goal in both
tracks is to first train a model on simulated, synthetic data in the source
domain and then adapt it to perform well on real image data in the unlabeled
test domain. Our dataset is the largest one to date for cross-domain object
classification, with over 280K images across 12 categories in the combined
training, validation and testing domains. The image segmentation dataset is
also large-scale with over 30K images across 18 categories in the three
domains. We compare VisDA to existing cross-domain adaptation datasets and
provide a baseline performance analysis using various domain adaptation models
that are currently popular in the field.
Image2Mesh: A Learning Framework for Single Image 3D Reconstruction
Authors: Jhony K. Pontes, Chen Kong, Sridha Sridharan, Simon Lucey, Anders Eriksson, Clinton Fookes
Date: 2017-11-29 00:00:00
Tasks: 3D Reconstruction

One challenge that remains open in 3D deep learning is how to efficiently
represent 3D data to feed deep networks. Recent works have relied on volumetric
or point cloud representations, but such approaches suffer from a number of
issues such as computational complexity, unordered data, and lack of finer
geometry. This paper demonstrates that a mesh representation (i.e. vertices and
faces to form polygonal surfaces) is able to capture fine-grained geometry for
3D reconstruction tasks. A mesh however is also unstructured data similar to
point clouds. We address this problem by proposing a learning framework to
infer the parameters of a compact mesh representation rather than learning from
the mesh itself. This compact representation encodes a mesh using free-form
deformation and a sparse linear combination of models allowing us to
reconstruct 3D meshes from single images. In contrast to prior work, we do not
rely on silhouettes and landmarks to perform 3D reconstruction. We evaluate our
method on synthetic and real-world datasets with very promising results. Our
framework efficiently reconstructs 3D objects in a low-dimensional way while
preserving its important geometrical aspects.
An Adaptive Fuzzy-Based System to Simulate, Quantify and Compensate Color Blindness
Authors: Jinmi Lee, Wellington Pinheiro dos Santos
Date: 2017-11-29 00:00:00

About 8% of the male population of the world are affected by a determined
type of color vision disturbance, which varies from the partial to complete
reduction of the ability to distinguish certain colors. A considerable amount
of color blind people are able to live all life long without knowing they have
color vision disabilities and abnormalities. Nowadays the evolution of
information technology and computer science, specifically image processing
techniques and computer graphics, can be fundamental to aid at the development
of adaptive color blindness correction tools. This paper presents a software
tool based on Fuzzy Logic to evaluate the type and the degree of color
blindness a person suffer from. In order to model several degrees of color
blindness, herein this work we modified the classical linear transform-based
simulation method by the use of fuzzy parameters. We also proposed four new
methods to correct color blindness based on a fuzzy approach: Methods A and B,
with and without histogram equalization. All the methods are based on
combinations of linear transforms and histogram operations. In order to
evaluate the results we implemented a web-based survey to get the best results
according to optimize to distinguish different elements in an image. Results
obtained from 40 volunteers proved that the Method B with histogram
equalization got the best results for about 47% of volunteers.
Topological Recurrent Neural Network for Diffusion Prediction
Authors: Jia Wang, Vincent W. Zheng, Zemin Liu, Kevin Chen-Chuan Chang
Date: 2017-11-28 00:00:00
Tasks: Prediction, Representation Learning

In this paper, we study the problem of using representation learning to
assist information diffusion prediction on graphs. In particular, we aim at
estimating the probability of an inactive node to be activated next in a
cascade. Despite the success of recent deep learning methods for diffusion, we
find that they often underexplore the cascade structure. We consider a cascade
as not merely a sequence of nodes ordered by their activation time stamps;
instead, it has a richer structure indicating the diffusion process over the
data graph. As a result, we introduce a new data model, namely diffusion
topologies, to fully describe the cascade structure. We find it challenging to
model diffusion topologies, which are dynamic directed acyclic graphs (DAGs),
with the existing neural networks. Therefore, we propose a novel topological
recurrent neural network, namely Topo-LSTM, for modeling dynamic DAGs. We
customize Topo-LSTM for the diffusion prediction task, and show it improves the
state-of-the-art baselines, by 20.1%--56.6% (MAP) relatively, across multiple
real-world data sets. Our code and data sets are available online at
https://github.com/vwz/topolstm.
One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay
Authors: Jake Bruce, Niko Suenderhauf, Piotr Mirowski, Raia Hadsell, Michael Milford
Date: 2017-11-28 00:00:00
Tasks: Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation

Recently, model-free reinforcement learning algorithms have been shown to
solve challenging problems by learning from extensive interaction with the
environment. A significant issue with transferring this success to the robotics
domain is that interaction with the real world is costly, but training on
limited experience is prone to overfitting. We present a method for learning to
navigate, to a fixed goal and in a known environment, on a mobile robot. The
robot leverages an interactive world model built from a single traversal of the
environment, a pre-trained visual feature encoder, and stochastic environmental
augmentation, to demonstrate successful zero-shot transfer under real-world
environmental variations without fine-tuning.
Semi-supervised learning of hierarchical representations of molecules using neural message passing
Authors: Hai Nguyen, Shin-ichi Maeda, Kenta Oono
Date: 2017-11-28 00:00:00

With the rapid increase of compound databases available in medicinal and
material science, there is a growing need for learning representations of
molecules in a semi-supervised manner. In this paper, we propose an
unsupervised hierarchical feature extraction algorithm for molecules (or more
generally, graph-structured objects with fixed number of types of nodes and
edges), which is applicable to both unsupervised and semi-supervised tasks. Our
method extends recently proposed Paragraph Vector algorithm and incorporates
neural message passing to obtain hierarchical representations of subgraphs. We
applied our method to an unsupervised task and demonstrated that it outperforms
existing proposed methods in several benchmark datasets. We also experimentally
showed that semi-supervised tasks enhanced predictive performance compared with
supervised ones with labeled molecules only.
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training
Authors: Faisal Mahmood, Richard Chen, Nicholas J. Durr
Date: 2017-11-17 00:00:00
Tasks: Depth Estimation, Domain Adaptation

To realize the full potential of deep learning for medical imaging, large
annotated datasets are required for training. Such datasets are difficult to
acquire because labeled medical images are not usually available due to privacy
issues, lack of experts available for annotation, underrepresentation of rare
conditions and poor standardization. Lack of annotated data has been addressed
in conventional vision applications using synthetic images refined via
unsupervised adversarial training to look like real images. However, this
approach is difficult to extend to general medical imaging because of the
complex and diverse set of features found in real human tissues. We propose an
alternative framework that uses a reverse flow, where adversarial training is
used to make real medical images more like synthetic images, and hypothesize
that clinically-relevant features can be preserved via self-regularization.
These domain-adapted images can then be accurately interpreted by networks
trained on large datasets of synthetic medical images. We test this approach
for the notoriously difficult task of depth-estimation from endoscopy. We train
a depth estimator on a large dataset of synthetic images generated using an
accurate forward model of an endoscope and an anatomically-realistic colon.
This network predicts significantly better depths when using synthetic-like
domain-adapted images compared to the real images, confirming that the
clinically-relevant features of depth are preserved.
SyGuS-Comp 2017: Results and Analysis
Authors: Rajeev Alur, Dana Fisman, Rishabh Singh, Armando Solar-Lezama
Date: 2017-11-29 00:00:00

Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an
implementation f that meets both a semantic constraint given by a logical
formula phi in a background theory T, and a syntactic constraint given by a
grammar G, which specifies the allowed set of candidate implementations. Such a
synthesis problem can be formally defined in SyGuS-IF, a language that is built
on top of SMT-LIB.
  The Syntax-Guided Synthesis Competition (SyGuS-Comp) is an effort to
facilitate, bring together and accelerate research and development of efficient
solvers for SyGuS by providing a platform for evaluating different synthesis
techniques on a comprehensive set of benchmarks. In this year's competition six
new solvers competed on over 1500 benchmarks. This paper presents and analyses
the results of SyGuS-Comp'17.
Ego-Surfing: Person Localization in First-Person Videos Using Ego-Motion Signatures
Authors: Ryo Yonetani, Kris M. Kitani, Yoichi Sato
Date: 2016-06-15 00:00:00
Tasks: Clustering, Retrieval, Video Retrieval

We envision a future time when wearable cameras are worn by the masses and
recording first-person point-of-view videos of everyday life. While these
cameras can enable new assistive technologies and novel research challenges,
they also raise serious privacy concerns. For example, first-person videos
passively recorded by wearable cameras will necessarily include anyone who
comes into the view of a camera -- with or without consent. Motivated by these
benefits and risks, we developed a self-search technique tailored to
first-person videos. The key observation of our work is that the egocentric
head motion of a target person (ie, the self) is observed both in the
point-of-view video of the target and observer. The motion correlation between
the target person's video and the observer's video can then be used to identify
instances of the self uniquely. We incorporate this feature into the proposed
approach that computes the motion correlation over densely-sampled trajectories
to search for a target individual in observer videos. Our approach
significantly improves self-search performance over several well-known face
detectors and recognizers. Furthermore, we show how our approach can enable
several practical applications such as privacy filtering, target video
retrieval, and social group clustering.
Rotation Averaging and Strong Duality
Authors: Anders Eriksson, Carl Olsson, Fredrik Kahl, Tat-Jun Chin
Date: 2017-05-03 00:00:00
Proceeding: CVPR 2018 6

In this paper we explore the role of duality principles within the problem of
rotation averaging, a fundamental task in a wide range of computer vision
applications. In its conventional form, rotation averaging is stated as a
minimization over multiple rotation constraints. As these constraints are
non-convex, this problem is generally considered challenging to solve globally.
We show how to circumvent this difficulty through the use of Lagrangian
duality. While such an approach is well-known it is normally not guaranteed to
provide a tight relaxation. Based on spectral graph theory, we analytically
prove that in many cases there is no duality gap unless the noise levels are
severe. This allows us to obtain certifiably global solutions to a class of
important non-convex problems in polynomial time.
  We also propose an efficient, scalable algorithm that out-performs general
purpose numerical solvers and is able to handle the large problem instances
commonly occurring in structure from motion settings. The potential of this
proposed method is demonstrated on a number of different problems, consisting
of both synthetic and real-world data.
TensorFlow Distributions
Authors: Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A. Saurous
Date: 2017-11-28 00:00:00
Tasks: Deep Learning, Probabilistic Programming

The TensorFlow Distributions library implements a vision of probability
theory adapted to the modern deep-learning paradigm of end-to-end
differentiable computation. Building on two basic abstractions, it offers
flexible building blocks for probabilistic computation. Distributions provide
fast, numerically stable methods for generating samples and computing
statistics, e.g., log density. Bijectors provide composable volume-tracking
transformations with automatic caching. Together these enable modular
construction of high dimensional distributions and transformations not possible
with previous libraries (e.g., pixelCNNs, autoregressive flows, and reversible
residual networks). They are the workhorse behind deep probabilistic
programming systems like Edward and empower fast black-box inference in
probabilistic models built on deep-network components. TensorFlow Distributions
has proven an important part of the TensorFlow toolkit within Google and in the
broader deep learning community.
Synthetic Data for Neural Machine Translation of Spoken-Dialects
Authors: Hany Hassan, Mostafa ElAraby, Ahmed Tawfik
Date: 2017-07-01 00:00:00
Proceeding: IWSLT 2017 12
Tasks: Machine Translation, Translation

In this paper, we introduce a novel approach to generate synthetic data for
training Neural Machine Translation systems. The proposed approach transforms a
given parallel corpus between a written language and a target language to a
parallel corpus between a spoken dialect variant and the target language. Our
approach is language independent and can be used to generate data for any
variant of the source language such as slang or spoken dialect or even for a
different language that is closely related to the source language.
  The proposed approach is based on local embedding projection of distributed
representations which utilizes monolingual embeddings to transform parallel
data across language variants. We report experimental results on Levantine to
English translation using Neural Machine Translation. We show that the
generated data can improve a very large scale system by more than 2.8 Bleu
points using synthetic spoken data which shows that it can be used to provide a
reliable translation system for a spoken dialect that does not have sufficient
parallel data.
Intent-Aware Contextual Recommendation System
Authors: Biswarup Bhattacharya, Iftikhar Burhanuddin, Abhilasha Sancheti, Kushal Satya
Date: 2017-11-28 00:00:00
Tasks: Recommendation Systems

Recommender systems take inputs from user history, use an internal ranking
algorithm to generate results and possibly optimize this ranking based on
feedback. However, often the recommender system is unaware of the actual intent
of the user and simply provides recommendations dynamically without properly
understanding the thought process of the user. An intelligent recommender
system is not only useful for the user but also for businesses which want to
learn the tendencies of their users. Finding out tendencies or intents of a
user is a difficult problem to solve.
  Keeping this in mind, we sought out to create an intelligent system which
will keep track of the user's activity on a web-application as well as
determine the intent of the user in each session. We devised a way to encode
the user's activity through the sessions. Then, we have represented the
information seen by the user in a high dimensional format which is reduced to
lower dimensions using tensor factorization techniques. The aspect of intent
awareness (or scoring) is dealt with at this stage. Finally, combining the user
activity data with the contextual information gives the recommendation score.
The final recommendations are then ranked using filtering and collaborative
recommendation techniques to show the top-k recommendations to the user. A
provision for feedback is also envisioned in the current system which informs
the model to update the various weights in the recommender system. Our overall
model aims to combine both frequency-based and context-based recommendation
systems and quantify the intent of a user to provide better recommendations.
  We ran experiments on real-world timestamped user activity data, in the
setting of recommending reports to the users of a business analytics tool and
the results are better than the baselines. We also tuned certain aspects of our
model to arrive at optimized results.
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ
Authors: Zhe Zhu, Michael Harowicz, Jun Zhang, Ashirbani Saha, Lars J. Grimm, E. Shelley Hwang, Maciej A. Mazurowski
Date: 2017-11-28 00:00:00
Tasks: Transfer Learning

Purpose: To determine whether deep learning-based algorithms applied to
breast MR images can aid in the prediction of occult invasive disease following
the di- agnosis of ductal carcinoma in situ (DCIS) by core needle biopsy.
Material and Methods: In this institutional review board-approved study, we
analyzed dynamic contrast-enhanced fat-saturated T1-weighted MRI sequences of
131 patients at our institution with a core needle biopsy-confirmed diagnosis
of DCIS. The patients had no preoperative therapy before breast MRI and no
prior history of breast cancer. We explored two different deep learning
approaches to predict whether there was a hidden (occult) invasive component in
the analyzed tumors that was ultimately detected at surgical excision. In the
first approach, we adopted the transfer learning strategy, in which a network
pre-trained on a large dataset of natural images is fine-tuned with our DCIS
images. Specifically, we used the GoogleNet model pre-trained on the ImageNet
dataset. In the second approach, we used a pre-trained network to extract deep
features, and a support vector machine (SVM) that utilizes these features to
predict the upstaging of the DCIS. We used 10-fold cross validation and the
area under the ROC curve (AUC) to estimate the performance of the predictive
models. Results: The best classification performance was obtained using the
deep features approach with GoogleNet model pre-trained on ImageNet as the
feature extractor and a polynomial kernel SVM used as the classifier (AUC =
0.70, 95% CI: 0.58- 0.79). For the transfer learning based approach, the
highest AUC obtained was 0.53 (95% CI: 0.41-0.62). Conclusion: Convolutional
neural networks could potentially be used to identify occult invasive disease
in patients diagnosed with DCIS at the initial core needle biopsy.
Image Registration Techniques: A Survey
Authors: Sayan Nag
Date: 2017-11-28 00:00:00
Tasks: Image Registration, Survey

Image Registration is the process of aligning two or more images of the same
scene with reference to a particular image. The images are captured from
various sensors at different times and at multiple view-points. Thus to get a
better picture of any change of a scene or object over a considerable period of
time image registration is important. Image registration finds application in
medical sciences, remote sensing and in computer vision. This paper presents a
detailed review of several approaches which are classified accordingly along
with their contributions and drawbacks. The main steps of an image registration
procedure are also discussed. Different performance measures are presented that
determine the registration quality and accuracy. The scope for the future
research are presented as well.