| Explaining deep neural network models with adversarial gradient integration D Pan, X Li, D Zhu Thirtieth International Joint Conference on Artificial Intelligence (IJCAI), 2021 | 69 | 2021 |
| Explainable recommendation via interpretable feature mapping and evaluation of explainability D Pan, X Li, X Li, D Zhu Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI), 2020 | 57 | 2020 |
| Attcat: Explaining transformers via attentive class activation tokens Y Qiang, D Pan, C Li, X Li, R Jang, D Zhu Advances in neural information processing systems 35, 5052-5064, 2022 | 50 | 2022 |
| Defending against adversarial attacks on medical imaging AI system, classification or detection? X Li, D Pan, D Zhu 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 1677-1681, 2021 | 41 | 2021 |
| On the learning property of logistic and softmax losses for deep neural networks X Li, X Li, D Pan, D Zhu Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4739-4746, 2020 | 28 | 2020 |
| Improving adversarial robustness via probabilistically compact loss with logit constraints X Li, X Li, D Pan, D Zhu Proceedings of the AAAI conference on artificial intelligence 35 (10), 8482-8490, 2021 | 24 | 2021 |
| Learning compact features via in-training representation alignment X Li, X Li, D Pan, Y Qiang, D Zhu Proceedings of the AAAI Conference on Artificial Intelligence 37 (7), 8675-8683, 2023 | 7* | 2023 |
| Negative flux aggregation to estimate feature attributions X Li, D Pan, C Li, Y Qiang, D Zhu arXiv preprint arXiv:2301.06989, 2023 | 7 | 2023 |
| Fast Explainability via Feasible Concept Sets Generator D Pan, N Moniz, N Chawla arXiv preprint arXiv:2405.18664, 2024 | | 2024 |
| Interpretable Machine Learning and Applications D Pan Wayne State University, 2022 | | 2022 |