Jiaxin Shi

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Jun ZhuProfessor of Computer Science, Tsinghua UniversityVerified email at mail.tsinghua.edu.cn
SHENGYANG SUNApplied Scientist, NVIDIAVerified email at nvidia.com
Roger GrosseAssociate Professor, University of TorontoVerified email at cs.toronto.edu
Shixia LiuProfessor, Tsinghua University, IEEE FellowVerified email at tsinghua.edu.cn
Mengchen LiuMicrosoftVerified email at microsoft.com
Chongxuan LiAssociate Professor (tenure track), Renmin University of ChinaVerified email at ruc.edu.cn
Michalis TitsiasDeepMindVerified email at google.com
Yang SongOpenAIVerified email at openai.com
Stefano ErmonStanford UniversityVerified email at cs.stanford.edu
Lester MackeyMicrosoft ResearchVerified email at microsoft.com
Yuhao ZhouPh.D. Student, Tsinghua UniversityVerified email at mails.tsinghua.edu.cn
Guodong ZhangxAIVerified email at x.ai
Chang LiuMicrosoft Research AI for ScienceVerified email at microsoft.com
Yucen LuoMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Zhijie DengAssistant Professor, Shanghai Jiao Tong UniversityVerified email at sjtu.edu.cn
Andriy MnihResearch Scientist at Google DeepMindVerified email at cs.toronto.edu
Jianfei ChenAssociate Professor, Tsinghua UniversityVerified email at mail.tsinghua.edu.cn
Emily B. FoxStanford UniversityVerified email at stanford.edu
Ke Alexander WangCS PhD student, Stanford UniversityVerified email at cs.stanford.edu
Mohammad Emtiyaz KhanCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoVerified email at postman.riken.jp

Jiaxin Shi

Other names石佳欣

Research Scientist, Google DeepMind

Verified email at google.com - Homepage

Machine Learning Artificial Intelligence Statistics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	602	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	412	2020
Functional variational bayesian neural networks S Sun, G Zhang, J Shi*, R Grosse International Conference on Learning Representations, 2019	300	2019
A spectral approach to gradient estimation for implicit distributions J Shi, S Sun, J Zhu International Conference on Machine Learning, 4644-4653, 2018	98	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	94	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	93	2018
Kernel implicit variational inference J Shi, S Sun, J Zhu International Conference on Learning Representations, 2017	62	2017
Sparse orthogonal variational inference for Gaussian processes J Shi, M Titsias, A Mnih International Conference on Artificial Intelligence and Statistics, 1932-1942, 2020	52	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	28	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	23	2018
A finite-particle convergence rate for stein variational gradient descent J Shi, L Mackey Advances in Neural Information Processing Systems 36, 2024	22	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	21	2022
Sampling with mirrored Stein operators J Shi, C Liu, L Mackey International Conference on Learning Representations, 2021	21	2021
Scalable training of inference networks for gaussian-process models J Shi, ME Khan, J Zhu International Conference on Machine Learning, 5758-5768, 2019	20	2019
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	10	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	10	2022
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition S Sun, J Shi, AG Wilson, R Grosse International Conference on Machine Learning, 2021	7	2021

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