Chuan-Yi Liu

eConv-TasNet is proposed, an efficient variant of Conv-TasNet that improves both effectiveness and efficiency without relying on resource-intensive modules, and achieves competitive performance compared with state-of-the-art methods while requiring significantly fewer parameters and lower inference cost.

Get robust and interpretable models by combining adversarial training with explanation-guided learning, achieving a whopping +37% boost in adversarial accuracy on OOD data.

Get better few-shot graph learning by explaining your predictions: BAED extracts informative subgraphs to improve accuracy and interpretability.