Jiaxin Shi

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Jun ZhuProfessor of Computer Science, Tsinghua UniversityVerified email at mail.tsinghua.edu.cn
SHENGYANG SUNPhD, University of TorontoVerified email at cs.toronto.edu
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, Renmin University of ChinaVerified email at ruc.edu.cn
Yuhao ZhouPh.D. Student, Tsinghua UniversityVerified email at mails.tsinghua.edu.cn
Guodong ZhangUniversity of TorontoVerified email at cs.toronto.edu
Michalis TitsiasDeepMindVerified email at google.com
Yang SongOpenAIVerified email at cs.stanford.edu
Chang LiuMicrosoft Research AI4ScienceVerified email at microsoft.com
Yucen LuoMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Lester MackeyMicrosoft ResearchVerified email at microsoft.com
Jianfei ChenAssistant Professor, Tsinghua UniversityVerified email at mail.tsinghua.edu.cn
Andriy MnihResearch Scientist at Google DeepMindVerified email at cs.toronto.edu
Zhijie DengAssistant Professor, Shanghai Jiao Tong UniversityVerified email at sjtu.edu.cn
Mohammad Emtiyaz KhanCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoVerified email at postman.riken.jp
Andrew Gordon WilsonNew York UniversityVerified email at cims.nyu.edu
Emily B. FoxStanford UniversityVerified email at stanford.edu
Ke Alexander WangCS PhD student, Stanford UniversityVerified email at cs.stanford.edu

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	521	2016
Functional variational bayesian neural networks S Sun, G Zhang, J Shi*, R Grosse International Conference on Learning Representations, 2019	239	2019
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	217	2020
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	87	2016
A spectral approach to gradient estimation for implicit distributions J Shi, S Sun, J Zhu International Conference on Machine Learning, 4644-4653, 2018	84	2018
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	74	2018
Kernel implicit variational inference J Shi, S Sun, J Zhu International Conference on Learning Representations, 2017	58	2017
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	46	2017
Sparse orthogonal variational inference for Gaussian processes J Shi, M Titsias, A Mnih International Conference on Artificial Intelligence and Statistics, 1932-1942, 2020	43	2020
Nonparametric score estimators Y Zhou, J Shi, J Zhu International Conference on Machine Learning, 11513-11522, 2020	22	2020
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
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	19	2018
Sampling with Mirrored Stein Operators J Shi, C Liu, L Mackey International Conference on Learning Representations, 2021	15	2021
Neuralef: Deconstructing kernels by deep neural networks Z Deng, J Shi, J Zhu International Conference on Machine Learning, 4976-4992, 2022	10	2022
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	9	2022
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent J Shi, L Mackey arXiv preprint arXiv:2211.09721, 2022	6	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	6	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
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	4	2022
Neural Networks as Inter-Domain Inducing Points S Sun, J Shi, RB Grosse Third Symposium on Advances in Approximate Bayesian Inference, 2020	4	2020

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