116 citations · 225 across the 27 of their papers we have counts for
4 papers · 1 filter
Controlled Dynamics Attractor Transformer
Cheng Zhang, Minnan Luo, Zesheng Yang +3
Transformer architectures have dramatically advanced representation learning and inference in deep models through self-attention mechanisms. In parallel,associative memory (AM) fra…
GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search
Xinshun Feng, Herun Wan, Shangbin Feng +4
Current Graph Neural Networks (GNNs) suffer from the over-smoothing problem, which results in indistinguishable node representations and low model performance with more GNN layers.…
Towards Explanation for Unsupervised Graph-Level Representation Learning
Qinghua Zheng, Jihong Wang, Minnan Luo +4
Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the inpu…
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective
Jihong Wang, Minnan Luo, Jundong Li +3
Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rende…