35 citations · 137 across the 14 of their papers we have counts for
4 papers · 1 filter
Robust Causal Graph Representation Learning against Confounding Effects
Hang Gao, Jiangmeng Li, Wenwen Qiang +4
The prevailing graph neural network models have achieved significant progress in graph representation learning. However, in this paper, we uncover an ever-overlooked phenomenon: th…
Bootstrapping Informative Graph Augmentation via A Meta Learning Approach
Hang Gao, Jiangmeng Li, Wenwen Qiang +3
Recent works explore learning graph representations in a self-supervised manner. In graph contrastive learning, benchmark methods apply various graph augmentation approaches. Howev…
Using Neural Networks for Programming by Demonstration
Karan K. Budhraja, Hang Gao, Tim Oates
Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents t…
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Hang Gao, Zheng Shou, Alireza Zareian +2
Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data. One natural remedy for this problem is data augmentation, which has been rec…