| 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 | 256 | 2016 |
| Triple generative adversarial nets LI Chongxuan, T Xu, J Zhu, B Zhang Advances in neural information processing systems, 4088-4098, 2017 | 234 | 2017 |
| Max-margin deep generative models C Li, J Zhu, T Shi, B Zhang Advances in neural information processing systems, 1837-1845, 2015 | 29 | 2015 |
| 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 | 25 | 2017 |
| Learning to generate with memory C Li, J Zhu, B Zhang International Conference on Machine Learning, 1177-1186, 2016 | 23 | 2016 |
| 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 | 17 | 2017 |
| Graphical generative adversarial networks LI Chongxuan, M Welling, J Zhu, B Zhang Advances in neural information processing systems, 6069-6080, 2018 | 15 | 2018 |
| Collaborative filtering with user-item co-autoregressive models C Du, C Li, Y Zheng, J Zhu, B Zhang arXiv preprint arXiv:1612.07146, 2016 | 11 | 2016 |
| 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 | 5* | 2019 |
| Learning implicit generative models by teaching explicit ones C Du, K Xu, C Li, J Zhu, B Zhang arXiv preprint arXiv:1807.03870, 2018 | 4 | 2018 |
| Multi-objects generation with amortized structural regularization T Xu, LI Chongxuan, J Zhu, B Zhang Advances in Neural Information Processing Systems, 6619-6629, 2019 | 3 | 2019 |
| Countering noisy labels by learning from auxiliary clean labels TW Tsai, C Li, J Zhu arXiv preprint arXiv:1905.13305, 2019 | 2 | 2019 |
| Population matching discrepancy and applications in deep learning J Chen, LI Chongxuan, Y Ru, J Zhu Advances in Neural Information Processing Systems, 6262-6272, 2017 | 2 | 2017 |
| Triple generative adversarial networks C Li, K Xu, J Liu, J Zhu, B Zhang arXiv preprint arXiv:1912.09784, 2019 | 1 | 2019 |
| Understanding and Stabilizing GANs' Training Dynamics with Control Theory K Xu, C Li, H Wei, J Zhu, B Zhang arXiv preprint arXiv:1909.13188, 2019 | 1 | 2019 |
| Efficient Learning of Generative Models via Finite-Difference Score Matching T Pang, K Xu, C Li, Y Song, S Ermon, J Zhu arXiv preprint arXiv:2007.03317, 2020 | | 2020 |
| Learning Implicit Generative Models By Teaching Density Estimators K Xu, C Du, C Li, J Zhu, B Zhang arXiv preprint arXiv:1807.03870, 2018 | | 2018 |
| Model-based Credit Assignment for Model-free Deep Reinforcement Learning D Yan, J Weng, S Huang, C Li, Y Zhou, H Su, J Zhu | | |
| Understanding and Stabilizing GANs’ Training Dynamics using Control Theory K Xu, C Li, J Zhu, B Zhang | | |
| Interactive Demo: A Visual Analysis System for Analyzing Deep Convolutional Neural Networks M Liu, T EDU, Z Li, C Li, COMJ Zhu, S Liu, EDU CN | | |
Jun ZhuProfessor of Computer Science, Tsinghua UniversityVerified email at mail.tsinghua.edu.cn
