1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2023
DRGCN: Dynamic Evolving Initial Residual for Deep Graph Convolutional Networks
Lei Zhang, Xiaodong Yan, Jianshan He +2
Graph convolutional networks (GCNs) have been proved to be very practical to handle various graph-related tasks. It has attracted considerable research interest to study deep GCNs,…
cs.LG2021★ 1 cited
Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning
Ke Sun, Yingnan Zhao, Enze Shi +4
The remarkable empirical performance of distributional reinforcement learning (RL) has garnered increasing attention to understanding its theoretical advantages over classical RL.…