422 citations · 529 across the 9 of their papers we have counts for
5 papers · 1 filter
Probabilistic Categorical Adversarial Attack & Adversarial Training
Han Xu, Pengfei He, Jie Ren +4
The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples wi…
Learning Representations for Hyper-Relational Knowledge Graphs
Harry Shomer, Wei Jin, Juanhui Li +2
Knowledge graphs (KGs) have gained prominence for their ability to learn representations for uni-relational facts. Recently, research has focused on modeling hyper-relational facts…
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily
Han Xu, Yaxin Li, Xiaorui Liu +2
Meta learning algorithms have been widely applied in many tasks for efficient learning, such as few-shot image classification and fast reinforcement learning. During meta training,…
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
Wei Jin, Yaxin Li, Han Xu +4
Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the i…
Attacking Graph Convolutional Networks via Rewiring
Yao Ma, Suhang Wang, Tyler Derr +2
Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification. Recent researches show that graph neural…