42 citations · 57 across the 8 of their papers we have counts for
4 papers · 1 filter
Learning How to Propagate Messages in Graph Neural Networks
Teng Xiao, Zhengyu Chen, Donglin Wang +1
This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs). One of the challenges for graph neural networks is that of defining the…
Decoupled Self-supervised Learning for Non-Homophilous Graphs
Teng Xiao, Zhengyu Chen, Zhimeng Guo +2
This paper studies the problem of conducting self-supervised learning for node representation learning on graphs. Most existing self-supervised learning methods assume the graph is…
Adaptive Adversarial Training for Meta Reinforcement Learning
Shiqi Chen, Zhengyu Chen, Donglin Wang
Meta Reinforcement Learning (MRL) enables an agent to learn from a limited number of past trajectories and extrapolate to a new task. In this paper, we attempt to improve the robus…
Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning
Jinxin Liu, Donglin Wang, Qiangxing Tian +1
It is of significance for an agent to learn a widely applicable and general-purpose policy that can achieve diverse goals including images and text descriptions. Considering such p…