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Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks
Gaotang Li, Marlena Duda, Xiang Zhang +2
Brain graphs, which model the structural and functional relationships between brain regions, are crucial in neuroscientific and clinical applications involving graph classification…
GC-Flow: A Graph-Based Flow Network for Effective Clustering
Tianchun Wang, Farzaneh Mirzazadeh, Xiang Zhang +1
Graph convolutional networks (GCNs) are \emph{discriminative models} that directly model the class posterior for semi-supervised classification of graph data. Whi…
Near-optimality for infinite-horizon restless bandits with many arms
Xiangyu Zhang, Peter I. Frazier
Restless bandits are an important class of problems with applications in recommender systems, active learning, revenue management and other areas. We consider infinite-horizon disc…
EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry
Yingqi Liu, Guangyu Shen, Guanhong Tao +3
Backdoor attack injects malicious behavior to models such that inputs embedded with triggers are misclassified to a target label desired by the attacker. However, natural features…
Backdoor Scanning for Deep Neural Networks through K-Arm Optimization
Guangyu Shen, Yingqi Liu, Guanhong Tao +5
Back-door attack poses a severe threat to deep learning systems. It injects hidden malicious behaviors to a model such that any input stamped with a special pattern can trigger suc…
Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification
Siyuan Cheng, Yingqi Liu, Shiqing Ma +1
Trojan (backdoor) attack is a form of adversarial attack on deep neural networks where the attacker provides victims with a model trained/retrained on malicious data. The backdoor…