225 citations · 432 across the 20 of their papers we have counts for
6 papers · 1 filter
Structure-Aware DropEdge Towards Deep Graph Convolutional Networks
Jiaqi Han, Wenbing Huang, Yu Rong +3
It has been discovered that Graph Convolutional Networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why…
Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning
Xiangzhe Kong, Wenbing Huang, Yang Liu
Many processes in biology and drug discovery involve various 3D interactions between molecules, such as protein and protein, protein and small molecule, etc. Given that different m…
Subequivariant Graph Reinforcement Learning in 3D Environments
Runfa Chen, Jiaqi Han, Fuchun Sun +1
Learning a shared policy that guides the locomotion of different agents is of core interest in Reinforcement Learning (RL), which leads to the study of morphology-agnostic RL. Howe…
When to Update Your Model: Constrained Model-based Reinforcement Learning
Tianying Ji, Yu Luo, Fuchun Sun +3
Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization a…
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training
Xueyi Liu, Yu Rong, Tingyang Xu +3
Graph instance contrastive learning has been proved as an effective task for Graph Neural Network (GNN) pre-training. However, one key issue may seriously impede the representative…
Equivariant Graph Mechanics Networks with Constraints
Wenbing Huang, Jiaqi Han, Yu Rong +3
Learning to reason about relations and dynamics over multiple interacting objects is a challenging topic in machine learning. The challenges mainly stem from that the interacting s…