| Visual interpretability for deep learning: a survey Q Zhang, SC Zhu Frontiers of Information Technology & Electronic Engineering 19 (1), 27-39, 2018 | 1005 | 2018 |
| Interpretable convolutional neural networks Q Zhang, YN Wu, SC Zhu Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 937 | 2018 |
| Interpreting cnns via decision trees Q Zhang, Y Yang, H Ma, YN Wu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 390 | 2019 |
| Interpreting CNN knowledge via an explanatory graph Q Zhang, R Cao, F Shi, YN Wu, SC Zhu Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 267 | 2018 |
| Prediction of human emergency behavior and their mobility following large-scale disaster X Song, Q Zhang, Y Sekimoto, R Shibasaki Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 211 | 2014 |
| Explaining knowledge distillation by quantifying the knowledge X Cheng, Z Rao, Y Chen, Q Zhang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 136 | 2020 |
| Modeling and probabilistic reasoning of population evacuation during large-scale disaster X Song, Q Zhang, Y Sekimoto, T Horanont, S Ueyama, R Shibasaki Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 129 | 2013 |
| Towards a deep and unified understanding of deep neural models in nlp C Guan, X Wang, Q Zhang, R Chen, D He, X Xie International conference on machine learning, 2454-2463, 2019 | 120 | 2019 |
| Examining CNN representations with respect to dataset bias Q Zhang, W Wang, SC Zhu Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 114 | 2018 |
| A unified approach to interpreting and boosting adversarial transferability X Wang, J Ren, S Lin, X Zhu, Y Wang, Q Zhang arXiv preprint arXiv:2010.04055, 2020 | 89 | 2020 |
| Growing interpretable part graphs on convnets via multi-shot learning Q Zhang, R Cao, YN Wu, SC Zhu Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 80 | 2017 |
| Prediction and simulation of human mobility following natural disasters X Song, Q Zhang, Y Sekimoto, R Shibasaki, NJ Yuan, X Xie ACM Transactions on Intelligent Systems and Technology (TIST) 8 (2), 1-23, 2016 | 73 | 2016 |
| Rasat: Integrating relational structures into pretrained seq2seq model for text-to-sql J Qi, J Tang, Z He, X Wan, Y Cheng, C Zhou, X Wang, Q Zhang, Z Lin arXiv preprint arXiv:2205.06983, 2022 | 72 | 2022 |
| Interpreting multivariate shapley interactions in dnns H Zhang, Y Xie, L Zheng, D Zhang, Q Zhang Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 10877 …, 2021 | 63 | 2021 |
| Explaining neural networks semantically and quantitatively R Chen, H Chen, J Ren, G Huang, Q Zhang Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 63 | 2019 |
| Interpretable CNNs for object classification Q Zhang, X Wang, YN Wu, H Zhou, SC Zhu IEEE transactions on pattern analysis and machine intelligence 43 (10), 3416 …, 2020 | 59 | 2020 |
| 3d-rotation-equivariant quaternion neural networks W Shen, B Zhang, S Huang, Z Wei, Q Zhang Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 58 | 2020 |
| A simulator of human emergency mobility following disasters: Knowledge transfer from big disaster data X Song, Q Zhang, Y Sekimoto, R Shibasaki, NJ Yuan, X Xie Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 56 | 2015 |
| Discovering and explaining the representation bottleneck of dnns H Deng, Q Ren, H Zhang, Q Zhang arXiv preprint arXiv:2111.06236, 2021 | 55 | 2021 |
| Interpreting and boosting dropout from a game-theoretic view H Zhang, S Li, Y Ma, M Li, Y Xie, Q Zhang arXiv preprint arXiv:2009.11729, 2020 | 48 | 2020 |
