| 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 | 420 | 2016 |
| Triple generative adversarial nets C Li, T Xu, J Zhu, B Zhang Advances in neural information processing systems 30, 2017 | 396 | 2017 |
| 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 | 74* | 2017 |
| 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 | 54 | 2017 |
| Learning to generate with memory C Li, J Zhu, B Zhang International conference on machine learning, 1177-1186, 2016 | 38 | 2016 |
| Graphical generative adversarial networks C Li, M Welling, J Zhu, B Zhang Advances in neural information processing systems 31, 2018 | 31 | 2018 |
| 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 | 26 | 2018 |
| Mice: Mixture of contrastive experts for unsupervised image clustering TW Tsai, C Li, J Zhu International Conference on Learning Representations, 2020 | 23 | 2020 |
| Triple generative adversarial networks C Li, K Xu, J Zhu, J Liu, B Zhang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 | 19* | 2021 |
| Implicit normalizing flows C Lu, J Chen, C Li, Q Wang, J Zhu arXiv preprint arXiv:2103.09527, 2021 | 15 | 2021 |
| 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 | 15 | 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 | 15* | 2019 |
| 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 | 14* | 2020 |
| Multi-objects generation with amortized structural regularization T Xu, C Li, J Zhu, B Zhang Advances in Neural Information Processing Systems 32, 2019 | 14 | 2019 |
| Bayesian neural network realization by exploiting inherent stochastic characteristics of analog rram Y Lin, Q Zhang, J Tang, B Gao, C Li, P Yao, Z Liu, J Zhu, J Lu, XS Hu, ... 2019 IEEE International Electron Devices Meeting (IEDM), 14.6. 1-14.6. 4, 2019 | 13 | 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 | 12 | 2020 |
| Learning implicit generative models by teaching density estimators K Xu, C Du, C Li, J Zhu, B Zhang Joint European Conference on Machine Learning and Knowledge Discovery in …, 2020 | 11* | 2020 |
| Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models F Bao, C Li, J Zhu, B Zhang arXiv preprint arXiv:2201.06503, 2022 | 10 | 2022 |
| 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 | 7 | 2020 |
| Countering noisy labels by learning from auxiliary clean labels TW Tsai, C Li, J Zhu arXiv preprint arXiv:1905.13305, 2019 | 6 | 2019 |
Jun ZhuBosch AI Professor, Department of Computer Science, Tsinghua UniversityZweryfikowany adres z mail.tsinghua.edu.cn
