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On Statistical Analysis of MOEAs with Multiple Performance Indicators
HaoWang, CarlosIgncioHernándezCastellanos, TomeEftimov....
Published date-12/01/2020
Assessing the empirical performance of Multi-Objective Evolutionary Algorithms (MOEAs) is vital when we extensively test a set of MOEAs and aim to determine a proper ranking thereof. Multiple performance indicators, …
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
WenzhengFeng, JieZhang, YuxiaoDong....
Published date-12/01/2020
DataAugmentation, NodeClassification
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer from the limitations of over-smoothing, …
Almost Surely Stable Deep Dynamics
NathanLawrence, PhilipLoewen, MichaelForbes....
Published date-12/01/2020
We introduce a method for learning provably stable deep neural network based dynamic models from observed data. Specifically, we consider discrete-time stochastic dynamic models, as they are of particular interest …
SRG-Net: Unsupervised Segmentation for Terracotta Warrior Point Cloud with 3D Pointwise CNN methods
YaoHu, GuohuaGeng, KangLi....
Published date-12/01/2020
Clustering
In this paper, we present a seed-region-growing CNN(SRG-Net) for unsupervised part segmentation with 3D point clouds of terracotta warriors. Previous neural network researches in 3D are mainly about supervised classification, …
A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation
RihuanKe, AngelicaAviles-Rivero, SaurabhPandey....
Published date-12/01/2020
SemanticSegmentation, Semi-SupervisedSemanticSegmentation
Semantic segmentation has been widely investigated in the community, in which the state of the art techniques are based on supervised models. Those models have reported unprecedented performance at the …
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms
SaschaSaralajew, LarsHoldijk, ThomasVillmann....
Published date-12/01/2020
Quantization
Methods for adversarial robustness certification aim to provide an upper bound on the test error of a classifier under adversarial manipulation of its input. Current certification methods are computationally expensive …