| Towards better analysis of deep convolutional neural networks M Liu, J Shi, Z Li, C Li, J Zhu, S Liu IEEE transactions on visualization and computer graphics 23 (1), 91-100, 2016 | 520 | 2016 |
| Triple generative adversarial nets C Li, T Xu, J Zhu, B Zhang Advances in neural information processing systems 30, 2017 | 493 | 2017 |
| Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps C Lu, Y Zhou, F Bao, J Chen, C Li, J Zhu NeurIPS 2022, 2022 | 233 | 2022 |
| Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models F Bao, C Li, J Zhu, B Zhang International Conference on Learning Representations, 2022 | 135 | 2022 |
| Learning to write stylized chinese characters by reading a handful of examples D Sun, T Ren, C Li, H Su, J Zhu arXiv preprint arXiv:1712.06424, 2017 | 69 | 2017 |
| Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations M Zhao, F Bao, C Li, J Zhu NeurIPS 2022, 2022 | 61 | 2022 |
| Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models C Lu, Y Zhou, F Bao, J Chen, C Li, J Zhu arXiv preprint arXiv:2211.01095, 2022 | 60 | 2022 |
| Max-margin deep generative models for (semi-) supervised learning C Li, J Zhu, B Zhang IEEE transactions on pattern analysis and machine intelligence 40 (11), 2762 …, 2017 | 48 | 2017 |
| Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning L Wang, K Yang, C Li, L Hong, Z Li, J Zhu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 46 | 2021 |
| Memory replay with data compression for continual learning L Wang, X Zhang, K Yang, L Yu, C Li, L Hong, S Zhang, Z Li, Y Zhong, ... ICLR 2022, 2022 | 43 | 2022 |
| Learning to generate with memory C Li, J Zhu, B Zhang International conference on machine learning, 1177-1186, 2016 | 42 | 2016 |
| Graphical generative adversarial networks C Li, M Welling, J Zhu, B Zhang Advances in neural information processing systems 31, 2018 | 41 | 2018 |
| Mice: Mixture of contrastive experts for unsupervised image clustering TW Tsai, C Li, J Zhu International conference on learning representations, 2020 | 39 | 2020 |
| Estimating the optimal covariance with imperfect mean in diffusion probabilistic models F Bao, C Li, J Sun, J Zhu, B Zhang arXiv preprint arXiv:2206.07309, 2022 | 37 | 2022 |
| Collaborative filtering with user-item co-autoregressive models C Du, C Li, Y Zheng, J Zhu, B Zhang Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 36 | 2018 |
| Max-margin deep generative models C Li, J Zhu, T Shi, B Zhang Advances in neural information processing systems 28, 2015 | 36 | 2015 |
| All are worth words: A vit backbone for diffusion models F Bao, S Nie, K Xue, Y Cao, C Li, H Su, J Zhu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 32* | 2023 |
| 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 | 31 | 2020 |
| Implicit normalizing flows C Lu, J Chen, C Li, Q Wang, J Zhu International Conference on Learning Representations, 2021 | 30 | 2021 |
| Understanding and stabilizing GANs’ training dynamics using control theory K Xu, C Li, J Zhu, B Zhang International Conference on Machine Learning, 10566-10575, 2020 | 30* | 2020 |
Jun ZhuProfessor of Computer Science, Tsinghua UniversityVerified email at mail.tsinghua.edu.cn
