| Triple generative adversarial nets C Li, T Xu, J Zhu, B Zhang Advances in neural information processing systems 30, 2017 | 569 | 2017 |
| Improving adversarial robustness via promoting ensemble diversity T Pang, K Xu, C Du, N Chen, J Zhu International Conference on Machine Learning, 4970-4979, 2019 | 516 | 2019 |
| Rethinking softmax cross-entropy loss for adversarial robustness T Pang, K Xu, Y Dong, C Du, N Chen, J Zhu arXiv preprint arXiv:1905.10626, 2019 | 208 | 2019 |
| Boosting adversarial training with hypersphere embedding T Pang, X Yang, Y Dong, K Xu, J Zhu, H Su Advances in Neural Information Processing Systems 33, 7779-7792, 2020 | 167 | 2020 |
| Mixup inference: Better exploiting mixup to defend adversarial attacks T Pang, K Xu, J Zhu arXiv preprint arXiv:1909.11515, 2019 | 131 | 2019 |
| Efficient learning of generative models via finite-difference score matching T Pang, K Xu, C Li, Y Song, S Ermon, J Zhu Advances in Neural Information Processing Systems 33, 19175-19188, 2020 | 59 | 2020 |
| Triple generative adversarial networks C Li, K Xu, J Zhu, J Liu, B Zhang IEEE transactions on pattern analysis and machine intelligence 44 (12), 9629 …, 2021 | 49 | 2021 |
| Understanding and stabilizing gans' training dynamics with control theory K Xu, C Li, J Zhu, B Zhang arXiv preprint arXiv:1909.13188, 2019 | 44* | 2019 |
| Neuron segmentation based on CNN with semi-supervised regularization K Xu, H Su, J Zhu, JS Guan, B Zhang Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 32 | 2016 |
| Multi-objects generation with amortized structural regularization T Xu, C Li, J Zhu, B Zhang Advances in Neural Information Processing Systems 32, 2019 | 22 | 2019 |
| Bi-level score matching for learning energy-based latent variable models F Bao, C Li, K Xu, H Su, J Zhu, B Zhang Advances in Neural Information Processing Systems 33, 18110-18122, 2020 | 17 | 2020 |
| The Youtube-8M kaggle competition: challenges and methods H Zou, K Xu, J Li, J Zhu arXiv preprint arXiv:1706.09274, 2017 | 17 | 2017 |
| Learning Implicit Generative Models By Teaching Density Estimators K Xu, C Du, C Li, J Zhu, B Zhang arXiv preprint arXiv:1807.03870, 2018 | 15* | 2018 |
| Rethinking and reweighting the univariate losses for multi-label ranking: Consistency and generalization G Wu, C Li, K Xu, J Zhu Advances in Neural Information Processing Systems 34, 14332-14344, 2021 | 13 | 2021 |
| To relieve your headache of training an mrf, take advil C Li, C Du, K Xu, M Welling, J Zhu, B Zhang arXiv preprint arXiv:1901.08400, 2019 | 13 | 2019 |
| Variational (gradient) estimate of the score function in energy-based latent variable models F Bao, K Xu, C Li, L Hong, J Zhu, B Zhang International Conference on Machine Learning, 651-661, 2021 | 11 | 2021 |
| Deep structured generative models K Xu, H Liang, J Zhu, H Su, B Zhang arXiv preprint arXiv:1807.03877, 2018 | 10 | 2018 |
| Method and Apparatus for Neural Network Based on Energy-Based Latent Variable Models J Zhu, F Bao, C Li, K Xu, H Su, S Lu US Patent App. 18/248,917, 2023 | | 2023 |
Jun ZhuProfessor of Computer Science, Tsinghua UniversityDirección de correo verificada de mail.tsinghua.edu.cn