| 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 | 102 | 2020 |
| 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 | 68 | 2019 |
| Interpretable complex-valued neural networks for privacy protection L Xiang, H Ma, H Zhang, Y Zhang, J Ren, Q Zhang arXiv preprint arXiv:1901.09546, 2019 | 44 | 2019 |
| A unified game-theoretic interpretation of adversarial robustness J Ren, D Zhang, Y Wang, L Chen, Z Zhou, Y Chen, X Cheng, X Wang, ... arXiv preprint arXiv:2103.07364, 2021 | 30 | 2021 |
| Defining and quantifying the emergence of sparse concepts in dnns J Ren, M Li, Q Chen, H Deng, Q Zhang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 29 | 2023 |
| Can we faithfully represent masked states to compute shapley values on a dnn? J Ren, Z Zhou, Q Chen, Q Zhang arXiv preprint arXiv:2105.10719, 2021 | 25 | 2021 |
| Towards a unified game-theoretic view of adversarial perturbations and robustness J Ren, D Zhang, Y Wang, L Chen, Z Zhou, Y Chen, X Cheng, X Wang, ... Advances in Neural Information Processing Systems 34, 3797-3810, 2021 | 21 | 2021 |
| Interpreting and disentangling feature components of various complexity from DNNs J Ren, M Li, Z Liu, Q Zhang International Conference on Machine Learning, 8971-8981, 2021 | 21 | 2021 |
| Identifying semantic induction heads to understand in-context learning J Ren, Q Guo, H Yan, D Liu, Q Zhang, X Qiu, D Lin arXiv preprint arXiv:2402.13055, 2024 | 20 | 2024 |
| Mining interpretable AOG representations from convolutional networks via active question answering Q Zhang, J Ren, G Huang, R Cao, YN Wu, SC Zhu IEEE transactions on pattern analysis and machine intelligence 43 (11), 3949 …, 2020 | 18 | 2020 |
| Proving common mechanisms shared by twelve methods of boosting adversarial transferability Q Zhang, X Wang, J Ren, X Cheng, S Lin, Y Wang, X Zhu arXiv preprint arXiv:2207.11694, 2022 | 11 | 2022 |
| Towards axiomatic, hierarchical, and symbolic explanation for deep models J Ren, M Li, Q Chen, H Deng, Q Zhang | 11 | 2021 |
| Trap of feature diversity in the learning of mlps D Liu, S Wang, J Ren, K Wang, S Yin, H Deng, Q Zhang arXiv preprint arXiv:2112.00980, 2021 | 7 | 2021 |
| Game-theoretic understanding of adversarially learned features J Ren, D Zhang, Y Wang, L Chen, Z Zhou, X Cheng, X Wang, Y Chen, ... arXiv preprint arXiv:2103.07364, 2021 | 6 | 2021 |
| Interpretability of neural networks based on game-theoretic interactions H Zhou, J Ren, H Deng, X Cheng, J Zhang, Q Zhang Machine Intelligence Research 21 (4), 718-739, 2024 | 4 | 2024 |
| Towards theoretical analysis of transformation complexity of ReLU DNNs J Ren, M Li, M Zhou, SH Chan, Q Zhang International Conference on Machine Learning, 18537-18558, 2022 | 3 | 2022 |
| Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI Q Zhang, T Han, L Fan, Z Zhu, H Su, YN Wu, J Ren, H Zhang arXiv preprint arXiv:2107.08821, 2021 | | 2021 |
| Towards a Game-Theoretic View of Baseline Values in the Shapley Value J Ren, Z Zhou, Q Chen, Q Zhang | | |
| Visualizing the Emergence of Primitive Interactions During the Training of DNNs J Ren, X Zheng, J Liu, Q Zhang | | |