18 citations · 22 across the 5 of their papers we have counts for
5 papers
Can LLM Assist in the Evaluation of the Quality of Machine Learning Explanations?
Bo Wang, Yiqiao Li, Jianlong Zhou +1
EXplainable machine learning (XML) has recently emerged to address the mystery mechanisms of machine learning (ML) systems by interpreting their 'black box' results. Despite the de…
ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks
Yiqiao Li, Jianlong Zhou, Yifei Dong +2
Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery. To address this challenge and ena…
Explaining Imitation Learning through Frames
Boyuan Zheng, Jianlong Zhou, Chunjie Liu +2
As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerabl…
GANExplainer: GAN-based Graph Neural Networks Explainer
Yiqiao Li, Jianlong Zhou, Boyuan Zheng +1
With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification…
A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics
Yiqiao Li, Jianlong Zhou, Sunny Verma +1
Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data. At the same time, the predictions made by these models are often hard to…