pwc-archive/papers-with-abstracts huggingface dataset:
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Authors: Johannes Gasteiger, Aleksandar Bojchevski, Stephan Günnemann
Date: 2018-10-14 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification, Node Classification, Node Classification on Non-Homophilic (Heterophilic) Graphs

Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, for classifying a node these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood is hard to extend. In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP. Our model's training time is on par or faster and its number of parameters on par or lower than previous models. It leverages a large, adjustable neighborhood for classification and can be easily combined with any neural network. We show that this model outperforms several recently proposed methods for semi-supervised classification in the most thorough study done so far for GCN-like models. Our implementation is available online.
BLEU is Not Suitable for the Evaluation of Text Simplification
Authors: Elior Sulem, Omri Abend, Ari Rappoport
Date: 2018-10-14 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence, Text Generation, Text Simplification

BLEU is widely considered to be an informative metric for text-to-text
generation, including Text Simplification (TS). TS includes both lexical and
structural aspects. In this paper we show that BLEU is not suitable for the
evaluation of sentence splitting, the major structural simplification
operation. We manually compiled a sentence splitting gold standard corpus
containing multiple structural paraphrases, and performed a correlation
analysis with human judgments. We find low or no correlation between BLEU and
the grammaticality and meaning preservation parameters where sentence splitting
is involved. Moreover, BLEU often negatively correlates with simplicity,
essentially penalizing simpler sentences.
The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs
Authors: Boris Ivanovic, Marco Pavone
Date: 2018-10-14 00:00:00
Proceeding: ICCV 2019 10
Tasks: Decision Making, Motion Forecasting, Trajectory Forecasting, Trajectory Modeling, Trajectory Prediction

Developing safe human-robot interaction systems is a necessary step towards the widespread integration of autonomous agents in society. A key component of such systems is the ability to reason about the many potential futures (e.g. trajectories) of other agents in the scene. Towards this end, we present the Trajectron, a graph-structured model that predicts many potential future trajectories of multiple agents simultaneously in both highly dynamic and multimodal scenarios (i.e. where the number of agents in the scene is time-varying and there are many possible highly-distinct futures for each agent). It combines tools from recurrent sequence modeling and variational deep generative modeling to produce a distribution of future trajectories for each agent in a scene. We demonstrate the performance of our model on several datasets, obtaining state-of-the-art results on standard trajectory prediction metrics as well as introducing a new metric for comparing models that output distributions.
Convex Hull Approximation of Nearly Optimal Lasso Solutions
Authors: Satoshi Hara, Takanori Maehara
Date: 2018-10-14 00:00:00
Tasks: Diversity, feature selection

In an ordinary feature selection procedure, a set of important features is
obtained by solving an optimization problem such as the Lasso regression
problem, and we expect that the obtained features explain the data well. In
this study, instead of the single optimal solution, we consider finding a set
of diverse yet nearly optimal solutions. To this end, we formulate the problem
as finding a small number of solutions such that the convex hull of these
solutions approximates the set of nearly optimal solutions. The proposed
algorithm consists of two steps: First, we randomly sample the extreme points
of the set of nearly optimal solutions. Then, we select a small number of
points using a greedy algorithm. The experimental results indicate that the
proposed algorithm can approximate the solution set well. The results also
indicate that we can obtain Lasso solutions with a large diversity.
Lung Structures Enhancement in Chest Radiographs via CT based FCNN Training
Authors: Ophir Gozes, Hayit Greenspan
Date: 2018-10-14 00:00:00
Tasks: Segmentation

The abundance of overlapping anatomical structures appearing in chest
radiographs can reduce the performance of lung pathology detection by automated
algorithms (CAD) as well as the human reader. In this paper, we present a deep
learning based image processing technique for enhancing the contrast of soft
lung structures in chest radiographs using Fully Convolutional Neural Networks
(FCNN). Two 2D FCNN architectures were trained to accomplish the task: The
first performs 2D lung segmentation which is used for normalization of the lung
area. The second FCNN is trained to extract lung structures. To create the
training images, we employed Simulated X-Ray or Digitally Reconstructed
Radiographs (DRR) derived from 516 scans belonging to the LIDC-IDRI dataset. By
first segmenting the lungs in the CT domain, we are able to create a dataset of
2D lung masks to be used for training the segmentation FCNN. For training the
extraction FCNN, we create DRR images of only voxels belonging to the 3D lung
segmentation which we call "Lung X-ray" and use them as target images. Once the
lung structures are extracted, the original image can be enhanced by fusing the
original input x-ray and the synthesized "Lung X-ray". We show that our
enhancement technique is applicable to real x-ray data, and display our results
on the recently released NIH Chest X-Ray-14 dataset. We see promising results
when training a DenseNet-121 based architecture to work directly on the lung
enhanced X-ray images.
Theoretical Guarantees of Transfer Learning
Authors: Zirui Wang
Date: 2018-10-14 00:00:00
Tasks: Transfer Learning

Transfer learning has been proven effective when within-target labeled data
is scarce. A lot of works have developed successful algorithms and empirically
observed positive transfer effect that improves target generalization error
using source knowledge. However, theoretical analysis of transfer learning is
more challenging due to the nature of the problem and thus is less studied. In
this report, we do a survey of theoretical works in transfer learning and
summarize key theoretical guarantees that prove the effectiveness of transfer
learning. The theoretical bounds are derived using model complexity and
learning algorithm stability. As we should see, these works exhibit a trade-off
between tight bounds and restrictive assumptions. Moreover, we also prove a new
generalization bound for the multi-source transfer learning problem using the
VC-theory, which is more informative than the one proved in previous work.
Finding Similar Medical Questions from Question Answering Websites
Authors: Yaliang Li, Liuyi Yao, Nan Du, Jing Gao, Qi Li, Chuishi Meng, Chenwei Zhang, Wei Fan
Date: 2018-10-14 00:00:00
Tasks: Diversity, Medical Question Answering, Question Answering, Retrieval

The past few years have witnessed the flourishing of crowdsourced medical
question answering (Q&A) websites. Patients who have medical information
demands tend to post questions about their health conditions on these
crowdsourced Q&A websites and get answers from other users. However, we observe
that a large portion of new medical questions cannot be answered in time or
receive only few answers from these websites. On the other hand, we notice that
solved questions have great potential to solve this challenge. Motivated by
these, we propose an end-to-end system that can automatically find similar
questions for unsolved medical questions. By learning the vector presentation
of unsolved questions and their candidate similar questions, the proposed
system outputs similar questions according to the similarity between vector
representations. Through the vector representation, the similar questions are
found at the question level, and the diversity of medical questions expression
issue can be addressed. Further, we handle two more important issues, i.e.,
training data generation issue and efficiency issue, associated with the LSTM
training procedure and the retrieval of candidate similar questions. The
effectiveness of the proposed system is validated on a large-scale real-world
dataset collected from a crowdsourced maternal-infant Q&A website.
Learning to Sketch with Deep Q Networks and Demonstrated Strokes
Authors: Tao Zhou, Chen Fang, Zhaowen Wang, Jimei Yang, Byungmoon Kim, Zhili Chen, Jonathan Brandt, Demetri Terzopoulos
Date: 2018-10-14 00:00:00
Tasks: Q-Learning

Doodling is a useful and common intelligent skill that people can learn and
master. In this work, we propose a two-stage learning framework to teach a
machine to doodle in a simulated painting environment via Stroke Demonstration
and deep Q-learning (SDQ). The developed system, Doodle-SDQ, generates a
sequence of pen actions to reproduce a reference drawing and mimics the
behavior of human painters. In the first stage, it learns to draw simple
strokes by imitating in supervised fashion from a set of strokeaction pairs
collected from artist paintings. In the second stage, it is challenged to draw
real and more complex doodles without ground truth actions; thus, it is trained
with Qlearning. Our experiments confirm that (1) doodling can be learned
without direct stepby- step action supervision and (2) pretraining with stroke
demonstration via supervised learning is important to improve performance. We
further show that Doodle-SDQ is effective at producing plausible drawings in
different media types, including sketch and watercolor.
Perceptual Image Quality Assessment through Spectral Analysis of Error Representations
Authors: Dogancan Temel, Ghassan AlRegib
Date: 2018-10-14 00:00:00
Tasks: Image Quality Assessment

In this paper, we analyze the statistics of error signals to assess the
perceived quality of images. Specifically, we focus on the magnitude spectrum
of error images obtained from the difference of reference and distorted images.
Analyzing spectral statistics over grayscale images partially models
interference in spatial harmonic distortion exhibited by the visual system but
it overlooks color information, selective and hierarchical nature of visual
system. To overcome these shortcomings, we introduce an image quality
assessment algorithm based on the Spectral Understanding of Multi-scale and
Multi-channel Error Representations, denoted as SUMMER. We validate the quality
assessment performance over 3 databases with around 30 distortion types. These
distortion types are grouped into 7 main categories as compression artifact,
image noise, color artifact, communication error, blur, global and local
distortions. In total, we benchmark the performance of 17 algorithms along with
the proposed algorithm using 5 performance metrics that measure linearity,
monotonicity, accuracy, and consistency. In addition to experiments with
standard performance metrics, we analyze the distribution of objective and
subjective scores with histogram difference metrics and scatter plots.
Moreover, we analyze the classification performance of quality assessment
algorithms along with their statistical significance tests. Based on our
experiments, SUMMER significantly outperforms majority of the compared methods
in all benchmark categories
Top-K Influential Nodes in Social Networks: A Game Perspective
Authors: Yu Zhang, Yan Zhang
Date: 2018-10-14 00:00:00
Tasks: Community Detection, Marketing

Influence maximization, the fundamental of viral marketing, aims to find top-$K$ seed nodes maximizing influence spread under certain spreading models. In this paper, we study influence maximization from a game perspective. We propose a Coordination Game model, in which every individuals make their decisions based on the benefit of coordination with their network neighbors, to study information propagation. Our model serves as the generalization of some existing models, such as Majority Vote model and Linear Threshold model. Under the generalized model, we study the hardness of influence maximization and the approximation guarantee of the greedy algorithm. We also combine several strategies to accelerate the algorithm. Experimental results show that after the acceleration, our algorithm significantly outperforms other heuristics, and it is three orders of magnitude faster than the original greedy method.
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario
Authors: Yixiong Liang, Zhihong Tang, Meng Yan, Jialin Chen, Qing Liu, Yao Xiang
Date: 2018-10-14 00:00:00
Tasks: Cell Segmentation, Few-Shot Learning, object-detection, Object Detection

Automated detection of cervical cancer cells or cell clumps has the potential to significantly reduce error rate and increase productivity in cervical cancer screening. However, most traditional methods rely on the success of accurate cell segmentation and discriminative hand-crafted features extraction. Recently there are emerging deep learning-based methods which train convolutional neural networks (CNN) to classify image patches, but they are computationally expensive. In this paper we propose an efficient CNN-based object detection methods for cervical cancer cells/clumps detection. Specifically, we utilize the state-of-the-art two-stage object detection method, the Faster-RCNN with Feature Pyramid Network (FPN) as the baseline and propose a novel comparison detector to deal with the limited data problem. The key idea is that classify the proposals by comparing with the reference samples of each category in object detection. In addition, we propose to learn the reference samples of the background from data instead of manually choosing them by some heuristic rules. Experimental results show that the proposed Comparison Detector yields significant improvement on the small dataset, achieving a mean Average Precision (mAP) of 26.3% and an Average Recall (AR) of 35.7%, both improving about 20 points compared to the baseline. Moreover, Comparison Detector improved AR by 4.6 points and achieved marginally better performance in terms of mAP compared with baseline model when training on the medium dataset. Our method is promising for the development of automation-assisted cervical cancer screening systems. Code is available at https://github.com/kuku-sichuan/ComparisonDetector.
Varifocal-Net: A Chromosome Classification Approach using Deep Convolutional Networks
Authors: Yulei Qin, Juan Wen, Hao Zheng, Xiaolin Huang, Jie Yang, Ning Song, Yue-Min Zhu, Lingqian Wu, Guang-Zhong Yang
Date: 2018-10-13 00:00:00
Tasks: Classification, General Classification, Multi-Task Learning, Weakly-supervised Learning

Chromosome classification is critical for karyotyping in abnormality
diagnosis. To expedite the diagnosis, we present a novel method named
Varifocal-Net for simultaneous classification of chromosome's type and polarity
using deep convolutional networks. The approach consists of one global-scale
network (G-Net) and one local-scale network (L-Net). It follows three stages.
The first stage is to learn both global and local features. We extract global
features and detect finer local regions via the G-Net. By proposing a varifocal
mechanism, we zoom into local parts and extract local features via the L-Net.
Residual learning and multi-task learning strategies are utilized to promote
high-level feature extraction. The detection of discriminative local parts is
fulfilled by a localization subnet of the G-Net, whose training process
involves both supervised and weakly-supervised learning. The second stage is to
build two multi-layer perceptron classifiers that exploit features of both two
scales to boost classification performance. The third stage is to introduce a
dispatch strategy of assigning each chromosome to a type within each patient
case, by utilizing the domain knowledge of karyotyping. Evaluation results from
1909 karyotyping cases showed that the proposed Varifocal-Net achieved the
highest accuracy per patient case (%) 99.2 for both type and polarity tasks. It
outperformed state-of-the-art methods, demonstrating the effectiveness of our
varifocal mechanism, multi-scale feature ensemble, and dispatch strategy. The
proposed method has been applied to assist practical karyotype diagnosis.
A System for Massively Parallel Hyperparameter Tuning
Authors: Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar
Date: 2018-10-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: BIG-bench Machine Learning, Distributed Computing, Hyperparameter Optimization

Modern learning models are characterized by large hyperparameter spaces and long training times. These properties, coupled with the rise of parallel computing and the growing demand to productionize machine learning workloads, motivate the need to develop mature hyperparameter optimization functionality in distributed computing settings. We address this challenge by first introducing a simple and robust hyperparameter optimization algorithm called ASHA, which exploits parallelism and aggressive early-stopping to tackle large-scale hyperparameter optimization problems. Our extensive empirical results show that ASHA outperforms existing state-of-the-art hyperparameter optimization methods; scales linearly with the number of workers in distributed settings; and is suitable for massive parallelism, as demonstrated on a task with 500 workers. We then describe several design decisions we encountered, along with our associated solutions, when integrating ASHA in Determined AI's end-to-end production-quality machine learning system that offers hyperparameter tuning as a service.
Porosity Amount Estimation in Stones Based on Combination of One Dimensional Local Binary Patterns and Image Normalization Technique
Authors: Shervan Fekri-Ershad
Date: 2018-10-13 00:00:00
Tasks: Defect Detection, Texture Classification

Since now, many approaches has been proposed for surface defect detection
based on image texture analysis techniques. One of the efficient texture
analysis operations is local binary patterns which provides good accuracy.
Two Can Play That Game: An Adversarial Evaluation of a Cyber-alert Inspection System
Authors: Ankit Shah, Arunesh Sinha, Rajesh Ganesan, Sushil Jajodia, Hasan Cam
Date: 2018-10-13 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Cyber-security is an important societal concern. Cyber-attacks have increased
in numbers as well as in the extent of damage caused in every attack. Large
organizations operate a Cyber Security Operation Center (CSOC), which form the
first line of cyber-defense. The inspection of cyber-alerts is a critical part
of CSOC operations. A recent work, in collaboration with Army Research Lab, USA
proposed a reinforcement learning (RL) based approach to prevent the
cyber-alert queue length from growing large and overwhelming the defender.
Given the potential deployment of this approach to CSOCs run by US defense
agencies, we perform a red team (adversarial) evaluation of this approach.
Further, with the recent attacks on learning systems, it is even more important
to test the limits of this RL approach. Towards that end, we learn an
adversarial alert generation policy that is a best response to the defender
inspection policy. Surprisingly, we find the defender policy to be quite robust
to the best response of the attacker. In order to explain this observation, we
extend the earlier RL model to a game model and show that there exists defender
policies that can be robust against any adversarial policy. We also derive a
competitive baseline from the game theory model and compare it to the RL
approach. However, we go further to exploit assumptions made in the MDP in the
RL model and discover an attacker policy that overwhelms the defender. We use a
double oracle approach to retrain the defender with episodes from this
discovered attacker policy. This made the defender robust to the discovered
attacker policy and no further harmful attacker policies were discovered.
Overall, the adversarial RL and double oracle approach in RL are general
techniques that are applicable to other RL usage in adversarial environments.
No-reference Image Denoising Quality Assessment
Authors: Si Lu
Date: 2018-10-13 00:00:00
Tasks: Denoising, Image Denoising

A wide variety of image denoising methods are available now. However, the
performance of a denoising algorithm often depends on individual input noisy
images as well as its parameter setting. In this paper, we present a
no-reference image denoising quality assessment method that can be used to
select for an input noisy image the right denoising algorithm with the optimal
parameter setting. This is a challenging task as no ground truth is available.
This paper presents a data-driven approach to learn to predict image denoising
quality. Our method is based on the observation that while individual existing
quality metrics and denoising models alone cannot robustly rank denoising
results, they often complement each other. We accordingly design denoising
quality features based on these existing metrics and models and then use Random
Forests Regression to aggregate them into a more powerful unified metric. Our
experiments on images with various types and levels of noise show that our
no-reference denoising quality assessment method significantly outperforms the
state-of-the-art quality metrics. This paper also provides a method that
leverages our quality assessment method to automatically tune the parameter
settings of a denoising algorithm for an input noisy image to produce an
optimal denoising result.
Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility
Authors: Joseph Y. Halpern, Max Kleiman-Weiner
Date: 2018-10-13 00:00:00

We provide formal definitions of degree of blameworthiness and intention
relative to an epistemic state (a probability over causal models and a utility
function on outcomes). These, together with a definition of actual causality,
provide the key ingredients for moral responsibility judgments. We show that
these definitions give insight into commonsense intuitions in a variety of
puzzling cases from the literature.
Embedded deep learning in ophthalmology: Making ophthalmic imaging smarter
Authors: Petteri Teikari, Raymond P. Najjar, Leopold Schmetterer, Dan Milea
Date: 2018-10-13 00:00:00
Tasks: Deep Learning, Prognosis

Deep learning has recently gained high interest in ophthalmology, due to its
ability to detect clinically significant features for diagnosis and prognosis.
Despite these significant advances, little is known about the ability of
various deep learning systems to be embedded within ophthalmic imaging devices,
allowing automated image acquisition. In this work, we will review the existing
and future directions for "active acquisition" embedded deep learning, leading
to as high quality images with little intervention by the human operator. In
clinical practice, the improved image quality should translate into more robust
deep learning-based clinical diagnostics. Embedded deep learning will be
enabled by the constantly improving hardware performance with low cost. We will
briefly review possible computation methods in larger clinical systems.
Briefly, they can be included in a three-layer framework composed of edge, fog
and cloud layers, the former being performed at a device-level. Improved edge
layer performance via "active acquisition" serves as an automatic data curation
operator translating to better quality data in electronic health records
(EHRs), as well as on the cloud layer, for improved deep learning-based
clinical data mining.