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Graph Stochastic Neural Networks for Semi-supervised Learning
HaiboWang, ChuanZhou, XinChen....
Published date-12/01/2020
NodeClassification, VariationalInference
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However, most existing models learn a deterministic classification function, which lack sufficient flexibility to …
Domain Generalization via Entropy Regularization
ShanshanZhao, MingmingGong, TongliangLiu....
Published date-12/01/2020
DomainGeneralization
Domain generalization aims to learn from multiple source domains a predictive model that can generalize to unseen target domains. One essential problem in domain generalization is to learn discriminative domain-invariant …
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 …
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks
HongweiJin, ZhanShi, VenkataJayaShankarAshishPeruri....
Published date-12/01/2020
GraphClassification
Graph convolution networks (GCNs) have become effective models for graph classification. Similar to many deep networks, GCNs are vulnerable to adversarial attacks on graph topology and node attributes. Recently, a …
Fair Multiple Decision Making Through Soft Interventions
YaoweiHu, YongkaiWu, LuZhang....
Published date-12/01/2020
DecisionMaking, fairness
Previous research in fair classification mostly focuses on a single decision model. In reality, there usually exist multiple decision models within a system and all of which may contain a …
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
Wen-DaJin, JunXu, Ming-MingCheng....
Published date-12/01/2020
SaliencyDetection
Intra-saliency and inter-saliency cues have been extensively studied for co-saliency detection (Co-SOD). Model-based methods produce coarse Co-SOD results due to hand-crafted intra- and inter-saliency features. Current data-driven models exploit inter-saliency …