134 citations · 159 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 3 cited
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks
Wanyu Lin, Hao Lan, Hao Wang +1
This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors. Spe…
cs.CV2021★ 134 cited
TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning
Yang Liu, Keze Wang, Lingbo Liu +2
Video self-supervised learning is a challenging task, which requires significant expressive power from the model to leverage rich spatial-temporal knowledge and generate effective…
cs.LG2021★ 22 cited
Generative Causal Explanations for Graph Neural Networks
Wanyu Lin, Hao Lan, Baochun Li
This paper presents Gem, a model-agnostic approach for providing interpretable explanations for any GNNs on various graph learning tasks. Specifically, we formulate the problem of…