pwc-archive/papers-with-abstracts huggingface dataset:
A Noise-Sensitivity-Analysis-Based Test Prioritization Technique for Deep Neural Networks
Authors: Long Zhang, Xuechao Sun, Yong Li, Zhen-Yu Zhang
Date: 2019-01-01 00:00:00
Tasks: Sensitivity

Deep neural networks (DNNs) have been widely used in the fields such as
natural language processing, computer vision and image recognition. But several
studies have been shown that deep neural networks can be easily fooled by
artificial examples with some perturbations, which are widely known as
adversarial examples. Adversarial examples can be used to attack deep neural
networks or to improve the robustness of deep neural networks. A common way of
generating adversarial examples is to first generate some noises and then add
them into original examples. In practice, different examples have different
noise-sensitive. To generate an effective adversarial example, it may be
necessary to add a lot of noise to low noise-sensitive example, which may make
the adversarial example meaningless. In this paper, we propose a
noise-sensitivity-analysis-based test prioritization technique to pick out
examples by their noise sensitivity. We construct an experiment to validate our
approach on four image sets and two DNN models, which shows that examples are
sensitive to noise and our method can effectively pick out examples by their
noise sensitivity.
Baseline Desensitizing In Translation Averaging
Authors: Bingbing Zhuang, Loong-Fah Cheong, Gim Hee Lee
Date: 2019-01-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: Translation

Many existing translation averaging algorithms are either sensitive to
disparate camera baselines and have to rely on extensive preprocessing to
improve the observed Epipolar Geometry graph, or if they are robust against
disparate camera baselines, require complicated optimization to minimize the
highly nonlinear angular error objective. In this paper, we carefully design a
simple yet effective bilinear objective function, introducing a variable to
perform the requisite normalization. The objective function enjoys the
baseline-insensitive property of the angular error and yet is amenable to
simple and efficient optimization by block coordinate descent, with good
empirical performance. A rotation-assisted Iterative Reweighted Least Squares
scheme is further put forth to help deal with outliers. We also contribute
towards a better understanding of the behavior of two recent convex algorithms,
LUD and Shapefit/kick, clarifying the underlying subtle difference that leads
to the performance gap. Finally, we demonstrate that our algorithm achieves
overall superior accuracies in benchmark dataset compared to state-of-theart
methods, and is also several times faster.
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections
Authors: Michael Wojnowicz, Di Zhang, Glenn Chisholm, Xuan Zhao, Matt Wolff
Date: 2019-01-03 00:00:00
Tasks: Dimensionality Reduction, General Classification, Malware Classification

For very large datasets, random projections (RP) have become the tool of
choice for dimensionality reduction. This is due to the computational
complexity of principal component analysis. However, the recent development of
randomized principal component analysis (RPCA) has opened up the possibility of
obtaining approximate principal components on very large datasets. In this
paper, we compare the performance of RPCA and RP in dimensionality reduction
for supervised learning. In Experiment 1, study a malware classification task
on a dataset with over 10 million samples, almost 100,000 features, and over 25
billion non-zero values, with the goal of reducing the dimensionality to a
compressed representation of 5,000 features. In order to apply RPCA to this
dataset, we develop a new algorithm called large sample RPCA (LS-RPCA), which
extends the RPCA algorithm to work on datasets with arbitrarily many samples.
We find that classification performance is much higher when using LS-RPCA for
dimensionality reduction than when using random projections. In particular,
across a range of target dimensionalities, we find that using LS-RPCA reduces
classification error by between 37% and 54%. Experiment 2 generalizes the
phenomenon to multiple datasets, feature representations, and classifiers.
These findings have implications for a large number of research projects in
which random projections were used as a preprocessing step for dimensionality
reduction. As long as accuracy is at a premium and the target dimensionality is
sufficiently less than the numeric rank of the dataset, randomized PCA may be a
superior choice. Moreover, if the dataset has a large number of samples, then
LS-RPCA will provide a method for obtaining the approximate principal
components.
Edge-Semantic Learning Strategy for Layout Estimation in Indoor Environment
Authors: Weidong Zhang, Wei zhang, Jason Gu
Date: 2019-01-03 00:00:00
Tasks: Decoder

Visual cognition of the indoor environment can benefit from the spatial
layout estimation, which is to represent an indoor scene with a 2D box on a
monocular image. In this paper, we propose to fully exploit the edge and
semantic information of a room image for layout estimation. More specifically,
we present an encoder-decoder network with shared encoder and two separate
decoders, which are composed of multiple deconvolution (transposed convolution)
layers, to jointly learn the edge maps and semantic labels of a room image. We
combine these two network predictions in a scoring function to evaluate the
quality of the layouts, which are generated by ray sampling and from a
predefined layout pool. Guided by the scoring function, we apply a novel
refinement strategy to further optimize the layout hypotheses. Experimental
results show that the proposed network can yield accurate estimates of edge
maps and semantic labels. By fully utilizing the two different types of labels,
the proposed method achieves state-of-the-art layout estimation performance on
benchmark datasets.
Volumetric Convolution: Automatic Representation Learning in Unit Ball
Authors: Sameera Ramasinghe, Salman Khan, Nick Barnes
Date: 2019-01-03 00:00:00
Proceeding: ICLR 2019 5
Tasks: 3D Object Recognition, Object Recognition, Representation Learning

Convolution is an efficient technique to obtain abstract feature
representations using hierarchical layers in deep networks. Although performing
convolution in Euclidean geometries is fairly straightforward, its extension to
other topological spaces---such as a sphere ($\mathbb{S}^2$) or a unit ball
($\mathbb{B}^3$)---entails unique challenges. In this work, we propose a novel
`\emph{volumetric convolution}' operation that can effectively convolve
arbitrary functions in $\mathbb{B}^3$. We develop a theoretical framework for
\emph{volumetric convolution} based on Zernike polynomials and efficiently
implement it as a differentiable and an easily pluggable layer for deep
networks. Furthermore, our formulation leads to derivation of a novel formula
to measure the symmetry of a function in $\mathbb{B}^3$ around an arbitrary
axis, that is useful in 3D shape analysis tasks. We demonstrate the efficacy of
proposed volumetric convolution operation on a possible use-case i.e., 3D
object recognition task.
Structure learning via unstructured kernel-based M-regression
Authors: Xin He, Yeheng Ge, Xingdong Feng
Date: 2019-01-03 00:00:00
Tasks: quantile regression, regression, Sparse Learning, valid

In statistical learning, identifying underlying structures of true target functions based on observed data plays a crucial role to facilitate subsequent modeling and analysis. Unlike most of those existing methods that focus on some specific settings under certain model assumptions, this paper proposes a general and novel framework for recovering true structures of target functions by using unstructured M-regression in a reproducing kernel Hilbert space (RKHS). The proposed framework is inspired by the fact that gradient functions can be employed as a valid tool to learn underlying structures, including sparse learning, interaction selection and model identification, and it is easy to implement by taking advantage of the nice properties of the RKHS. More importantly, it admits a wide range of loss functions, and thus includes many commonly used methods, such as mean regression, quantile regression, likelihood-based classification, and margin-based classification, which is also computationally efficient by solving convex optimization tasks. The asymptotic results of the proposed framework are established within a rich family of loss functions without any explicit model specifications. The superior performance of the proposed framework is also demonstrated by a variety of simulated examples and a real case study.
Adversarial Learning of a Sampler Based on an Unnormalized Distribution
Authors: Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin
Date: 2019-01-03 00:00:00
Tasks: Form, Q-Learning

We investigate adversarial learning in the case when only an unnormalized
form of the density can be accessed, rather than samples. With insights so
garnered, adversarial learning is extended to the case for which one has access
to an unnormalized form u(x) of the target density function, but no samples.
Further, new concepts in GAN regularization are developed, based on learning
from samples or from u(x). The proposed method is compared to alternative
approaches, with encouraging results demonstrated across a range of
applications, including deep soft Q-learning.
Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
Authors: Victor Zhong, Caiming Xiong, Nitish Shirish Keskar, Richard Socher
Date: 2019-01-03 00:00:00
Proceeding: ICLR 2019 5
Tasks: Question Answering

End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurrences across all of the documents with the query. We design these modules using hierarchies of coattention and self-attention, which learn to emphasize different parts of the input. On the Qangaroo WikiHop multi-evidence question answering task, the CFC obtains a new state-of-the-art result of 70.6% on the blind test set, outperforming the previous best by 3% accuracy despite not using pretrained contextual encoders.
Weights Adaptation Optimization of Heterogeneous Epidemic Spreading Networks: A Constrained Cooperative Coevolution Strategy
Authors: Yun Feng, Bing-Chuan Wang
Date: 2019-01-03 00:00:00
Tasks: Evolutionary Algorithms

In this paper, the dynamic constrained optimization problem of weights adaptation for heterogeneous epidemic spreading networks is investigated. Due to the powerful ability of searching global optimum, evolutionary algorithms are employed as the optimizers. One major difficulty is that the dimension of the problem is increasing exponentially with the network size and most existing evolutionary algorithms cannot achieve satisfiable performance on large-scale optimization problems. To address this issue, a novel constrained cooperative coevolution ($C^3$) strategy, which can separate the original large-scale problem into different subcomponents, is employed to achieve the trade-off between the constraint and objective function.
A Remote Sensing Image Dataset for Cloud Removal
Authors: Daoyu Lin, Guangluan Xu, Xiaoke Wang, Yang Wang, Xian Sun, Kun fu
Date: 2019-01-03 00:00:00
Tasks: Change Detection, Cloud Removal, Deep Learning, Scene Classification

Cloud-based overlays are often present in optical remote sensing images, thus
limiting the application of acquired data. Removing clouds is an indispensable
pre-processing step in remote sensing image analysis. Deep learning has
achieved great success in the field of remote sensing in recent years,
including scene classification and change detection. However, deep learning is
rarely applied in remote sensing image removal clouds. The reason is the lack
of data sets for training neural networks. In order to solve this problem, this
paper first proposed the Remote sensing Image Cloud rEmoving dataset (RICE).
The proposed dataset consists of two parts: RICE1 contains 500 pairs of images,
each pair has images with cloud and cloudless size of 512*512; RICE2 contains
450 sets of images, each set contains three 512*512 size images. ,
respectively, the reference picture without clouds, the picture of the cloud
and the mask of its cloud. The dataset is freely available at
\url{https://github.com/BUPTLdy/RICE_DATASET}.
A Comprehensive Survey on Graph Neural Networks
Authors: Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu
Date: 2019-01-03 00:00:00
Tasks: BIG-bench Machine Learning, image-classification, Image Classification, Natural Language Understanding, Speech Recognition, Survey

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, we propose potential research directions in this rapidly growing field.
Rethinking on Multi-Stage Networks for Human Pose Estimation
Authors: Wenbo Li, Zhicheng Wang, Binyi Yin, Qixiang Peng, Yuming Du, Tianzi Xiao, Gang Yu, Hongtao Lu, Yichen Wei, Jian Sun
Date: 2019-01-01 00:00:00
Tasks: Keypoint Detection, Pose Estimation

Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods. While multi-stage methods are seemingly more suited for the task, their performance in current practice is not as good as single-stage methods. This work studies this issue. We argue that the current multi-stage methods' unsatisfactory performance comes from the insufficiency in various design choices. We propose several improvements, including the single-stage module design, cross stage feature aggregation, and coarse-to-fine supervision. The resulting method establishes the new state-of-the-art on both MS COCO and MPII Human Pose dataset, justifying the effectiveness of a multi-stage architecture. The source code is publicly available for further research.
The Challenges in Specifying and Explaining Synthesized Implementations of Reactive Systems
Authors: Hadas Kress-Gazit, Hazem Torfah
Date: 2019-01-03 00:00:00

In formal synthesis of reactive systems an implementation of a system is
automatically constructed from its formal specification. The great advantage of
synthesis is that the resulting implementation is correct by construction;
therefore there is no need for manual programming and tedious debugging tasks.
Developers remain, nevertheless, hesitant to using automatic synthesis tools
and still favor manually writing code. A common argument against synthesis is
that the resulting implementation does not always give a clear picture on what
decisions were made during the synthesis process. The outcome of synthesis
tools is mostly unreadable and hinders the developer from understanding the
functionality of the resulting implementation. Many attempts have been made in
the last years to make the synthesis process more transparent to users. Either
by structuring the outcome of synthesis tools or by providing additional
automated support to help users with the specification process. In this paper
we discuss the challenges in writing specifications for reactive systems and
give a survey on what tools have been developed to guide users in specifying
reactive systems and understanding the outcome of synthesis tools.
Towards a Framework Combining Machine Ethics and Machine Explainability
Authors: Kevin Baum, Holger Hermanns, Timo Speith
Date: 2019-01-03 00:00:00
Tasks: Decision Making, Ethics

We find ourselves surrounded by a rapidly increasing number of autonomous and
semi-autonomous systems. Two grand challenges arise from this development:
Machine Ethics and Machine Explainability. Machine Ethics, on the one hand, is
concerned with behavioral constraints for systems, so that morally acceptable,
restricted behavior results; Machine Explainability, on the other hand, enables
systems to explain their actions and argue for their decisions, so that human
users can understand and justifiably trust them.
  In this paper, we try to motivate and work towards a framework combining
Machine Ethics and Machine Explainability. Starting from a toy example, we
detect various desiderata of such a framework and argue why they should and how
they could be incorporated in autonomous systems. Our main idea is to apply a
framework of formal argumentation theory both, for decision-making under
ethical constraints and for the task of generating useful explanations given
only limited knowledge of the world. The result of our deliberations can be
described as a first version of an ethically motivated, principle-governed
framework combining Machine Ethics and Machine Explainability
Prediction of multi-dimensional spatial variation data via Bayesian tensor completion
Authors: Jiali Luan, Zheng Zhang
Date: 2019-01-03 00:00:00

This paper presents a multi-dimensional computational method to predict the
spatial variation data inside and across multiple dies of a wafer. This
technique is based on tensor computation. A tensor is a high-dimensional
generalization of a matrix or a vector. By exploiting the hidden low-rank
property of a high-dimensional data array, the large amount of unknown
variation testing data may be predicted from a few random measurement samples.
The tensor rank, which decides the complexity of a tensor representation, is
decided by an available variational Bayesian approach. Our approach is
validated by a practical chip testing data set, and it can be easily
generalized to characterize the process variations of multiple wafers. Our
approach is more efficient than the previous virtual probe techniques in terms
of memory and computational cost when handling high-dimensional chip testing
data.
An Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy
Authors: Xinwu Yang, Guizeng You, Chong Zhao, Mengfei Dou, Xinian Guo
Date: 2019-01-03 00:00:00
Tasks: Clustering, Evolutionary Algorithms

Two important characteristics of multi-objective evolutionary algorithms are
distribution and convergency. As a classic multi-objective genetic algorithm,
NSGA-II is widely used in multi-objective optimization fields. However, in
NSGA-II, the random population initialization and the strategy of population
maintenance based on distance cannot maintain the distribution or convergency
of the population well. To dispose these two deficiencies, this paper proposes
an improved algorithm, OTNSGA-II II, which has a better performance on
distribution and convergency. The new algorithm adopts orthogonal experiment,
which selects individuals in manner of a new discontinuing non-dominated
sorting and crowding distance, to produce the initial population. And a new
pruning strategy based on clustering is proposed to self-adaptively prunes
individuals with similar features and poor performance in non-dominated sorting
and crowding distance, or to individuals are far away from the Pareto Front
according to the degree of intra-class aggregation of clustering results. The
new pruning strategy makes population to converge to the Pareto Front more
easily and maintain the distribution of population. OTNSGA-II and NSGA-II are
compared on various types of test functions to verify the improvement of
OTNSGA-II in terms of distribution and convergency.
Event detection in Twitter: A keyword volume approach
Authors: Ahmad Hany Hossny, Lewis Mitchell
Date: 2019-01-03 00:00:00
Tasks: Binary Classification, Event Detection, Transliteration

Event detection using social media streams needs a set of informative
features with strong signals that need minimal preprocessing and are highly
associated with events of interest. Identifying these informative features as
keywords from Twitter is challenging, as people use informal language to
express their thoughts and feelings. This informality includes acronyms,
misspelled words, synonyms, transliteration and ambiguous terms. In this paper,
we propose an efficient method to select the keywords frequently used in
Twitter that are mostly associated with events of interest such as protests.
The volume of these keywords is tracked in real time to identify the events of
interest in a binary classification scheme. We use keywords within word-pairs
to capture the context. The proposed method is to binarize vectors of daily
counts for each word-pair by applying a spike detection temporal filter, then
use the Jaccard metric to measure the similarity of the binary vector for each
word-pair with the binary vector describing event occurrence. The top n
word-pairs are used as features to classify any day to be an event or non-event
day. The selected features are tested using multiple classifiers such as Naive
Bayes, SVM, Logistic Regression, KNN and decision trees. They all produced AUC
ROC scores up to 0.91 and F1 scores up to 0.79. The experiment is performed
using the English language in multiple cities such as Melbourne, Sydney and
Brisbane as well as the Indonesian language in Jakarta. The two experiments,
comprising different languages and locations, yielded similar results.