2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2025★ 2 cited
Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
Jiaxing Zhang, Xiaoou Liu, Dongsheng Luo +1
Explaining Graph Neural Networks (GNNs) has garnered significant attention due to the need for interpretability, enabling users to understand the behavior of these black-box models…
cs.LG2024
LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation
Jiaxing Zhang, Jiayi Liu, Dongsheng Luo +2
Recent studies seek to provide Graph Neural Network (GNN) interpretability via multiple unsupervised learning models. Due to the scarcity of datasets, current methods easily suffer…
cs.LG2024
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
Tiejin Chen, Wenwang Huang, Linsey Pang +2
This paper delves into the critical area of deep learning robustness, challenging the conventional belief that classification robustness and explanation robustness in image classif…