3 papers
cs.LG2026
BAED: a New Paradigm for Few-shot Graph Learning with Explanation in the Loop
Chao Chen, Xujia Li, Dongsheng Hong +4
The challenges of training and inference in few-shot environments persist in the area of graph representation learning. The quality and quantity of labels are often insufficient du…
cs.LG2026
Explanation-Guided Adversarial Training for Robust and Interpretable Models
Chao Chen, Yanhui Chen, Shanshan Lin +4
Deep neural networks (DNNs) have achieved remarkable performance in many tasks, yet they often behave as opaque black boxes. Explanation-guided learning (EGL) methods steer DNNs us…
cs.CV2025
From Attribution to Action: Jointly ALIGNing Predictions and Explanations
Dongsheng Hong, Chao Chen, Yanhui Chen +3
Explanation-guided learning (EGL) has shown promise in aligning model predictions with interpretable reasoning, particularly in computer vision tasks. However, most approaches rely…