| 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 | 577 | 2016 |
| Sliced score matching: A scalable approach to density and score estimation Y Song, S Garg, J Shi, S Ermon Uncertainty in Artificial Intelligence, 574-584, 2020 | 368 | 2020 |
| Functional variational bayesian neural networks S Sun*, G Zhang*, J Shi*, R Grosse International Conference on Learning Representations, 2019 | 289 | 2019 |
| A spectral approach to gradient estimation for implicit distributions J Shi, S Sun, J Zhu International Conference on Machine Learning, 4644-4653, 2018 | 92 | 2018 |
| Plenopatch: Patch-based plenoptic image manipulation FL Zhang, J Wang, E Shechtman, ZY Zhou, JX Shi, SM Hu IEEE transactions on visualization and computer graphics 23 (5), 1561-1573, 2016 | 92 | 2016 |
| Message passing Stein variational gradient descent J Zhuo, C Liu, J Shi, J Zhu, N Chen, B Zhang International Conference on Machine Learning, 6018-6027, 2018 | 87 | 2018 |
| Kernel implicit variational inference J Shi*, S Sun*, J Zhu International Conference on Learning Representations, 2017 | 60 | 2017 |
| Sparse orthogonal variational inference for Gaussian processes J Shi, M Titsias, A Mnih International Conference on Artificial Intelligence and Statistics, 1932-1942, 2020 | 51 | 2020 |
| ZhuSuan: A library for Bayesian deep learning J Shi, J Chen, J Zhu, S Sun, Y Luo, Y Gu, Y Zhou arXiv preprint arXiv:1709.05870, 2017 | 47 | 2017 |
| Nonparametric score estimators Y Zhou, J Shi, J Zhu International Conference on Machine Learning, 11513-11522, 2020 | 26 | 2020 |
| Semi-crowdsourced clustering with deep generative models Y Luo, T Tian, J Shi, J Zhu, B Zhang Advances in Neural Information Processing Systems 31, 2018 | 22 | 2018 |
| Sampling with mirrored Stein operators J Shi, C Liu, L Mackey International Conference on Learning Representations, 2021 | 20 | 2021 |
| Scalable training of inference networks for gaussian-process models J Shi, ME Khan, J Zhu International Conference on Machine Learning, 5758-5768, 2019 | 19 | 2019 |
| A finite-particle convergence rate for stein variational gradient descent J Shi, L Mackey Advances in Neural Information Processing Systems 36, 2024 | 18 | 2024 |
| Gradient estimation with discrete Stein operators J Shi, Y Zhou, J Hwang, M Titsias, L Mackey Advances in Neural Information Processing Systems 35, 25829-25841, 2022 | 17 | 2022 |
| Neuralef: Deconstructing kernels by deep neural networks Z Deng, J Shi, J Zhu International Conference on Machine Learning, 4976-4992, 2022 | 17 | 2022 |
| Neural eigenfunctions are structured representation learners Z Deng, J Shi, H Zhang, P Cui, C Lu, J Zhu arXiv preprint arXiv:2210.12637, 2022 | 11 | 2022 |
| Sequence modeling with multiresolution convolutional memory J Shi, KA Wang, E Fox International Conference on Machine Learning, 31312-31327, 2023 | 8 | 2023 |
| Double control variates for gradient estimation in discrete latent variable models M Titsias, J Shi International Conference on Artificial Intelligence and Statistics, 6134-6151, 2022 | 8 | 2022 |
| Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition S Sun, J Shi, AG Wilson, R Grosse International Conference on Machine Learning, 2021 | 5 | 2021 |
Jun ZhuProfessor of Computer Science, Tsinghua University在 mail.tsinghua.edu.cn 的电子邮件经过验证
