Jiaxin Shi

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Jun ZhuProfessor of Computer Science, Tsinghua UniversityPatvirtintas el. paštas mail.tsinghua.edu.cn
SHENGYANG SUNResearch Scientist, NVIDIAPatvirtintas el. paštas nvidia.com
Roger GrosseAssociate Professor, University of TorontoPatvirtintas el. paštas cs.toronto.edu
Michalis TitsiasDeepMindPatvirtintas el. paštas google.com
Shixia LiuProfessor, Tsinghua University, IEEE FellowPatvirtintas el. paštas tsinghua.edu.cn
Mengchen LiuMicrosoftPatvirtintas el. paštas microsoft.com
Chongxuan LiAssociate Professor (tenure track), Renmin University of ChinaPatvirtintas el. paštas ruc.edu.cn
Lester MackeyMicrosoft ResearchPatvirtintas el. paštas microsoft.com
Yang SongOpenAIPatvirtintas el. paštas openai.com
Stefano ErmonStanford UniversityPatvirtintas el. paštas cs.stanford.edu
Yuhao ZhouTsinghua UniversityPatvirtintas el. paštas mails.tsinghua.edu.cn
Guodong ZhangxAIPatvirtintas el. paštas x.ai
Chang LiuMicrosoft Research AI for SciencePatvirtintas el. paštas microsoft.com
Yucen LuoMax Planck Institute for Intelligent SystemsPatvirtintas el. paštas tuebingen.mpg.de
Emily B. FoxStanford UniversityPatvirtintas el. paštas stanford.edu
Ke Alexander WangCS PhD student, Stanford UniversityPatvirtintas el. paštas cs.stanford.edu
Zhijie DengAssistant Professor, Shanghai Jiao Tong UniversityPatvirtintas el. paštas sjtu.edu.cn
Andriy MnihResearch Scientist at Google DeepMindPatvirtintas el. paštas cs.toronto.edu
Jianfei ChenAssociate Professor, Tsinghua UniversityPatvirtintas el. paštas mail.tsinghua.edu.cn
Arnaud DoucetGoogle DeepMindPatvirtintas el. paštas google.com

Stebėti

Jiaxin Shi

Kiti vardai石佳欣

Research Scientist, Google DeepMind

Patvirtintas el. paštas google.com - Pagrindinis puslapis

Machine Learning Artificial Intelligence Statistics


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	622	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, 2019	484	2019
Functional variational bayesian neural networks S Sun, G Zhang, J Shi*, R Grosse International Conference on Learning Representations, 2019	318	2019
A spectral approach to gradient estimation for implicit distributions J Shi, S Sun, J Zhu International Conference on Machine Learning, 4644-4653, 2018	104	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	99	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	97	2016
Kernel implicit variational inference J Shi, S Sun, J Zhu International Conference on Learning Representations, 2017	65	2017
Sparse orthogonal variational inference for Gaussian processes J Shi, M Titsias, A Mnih International Conference on Artificial Intelligence and Statistics, 1932-1942, 2020	57	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	46	2017
Nonparametric score estimators Y Zhou, J Shi, J Zhu International Conference on Machine Learning, 11513-11522, 2020	34	2020
Simplified and generalized masked diffusion for discrete data J Shi, K Han, Z Wang, A Doucet, M Titsias Advances in Neural Information Processing Systems 37, 103131-103167, 2024	32	2024
A finite-particle convergence rate for stein variational gradient descent J Shi, L Mackey Advances in Neural Information Processing Systems 36, 2023	26	2023
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	25	2022
Sampling with mirrored Stein operators J Shi, C Liu, L Mackey International Conference on Learning Representations, 2021	25	2021
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	24	2018
Neuralef: Deconstructing kernels by deep neural networks Z Deng, J Shi, J Zhu International Conference on Machine Learning, 4976-4992, 2022	23	2022
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
Sequence modeling with multiresolution convolutional memory J Shi, KA Wang, E Fox International Conference on Machine Learning, 31312-31327, 2023	16	2023
Neural eigenfunctions are structured representation learners Z Deng, J Shi, H Zhang, P Cui, C Lu, J Zhu arXiv preprint arXiv:2210.12637, 2022	12	2022
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	11	2022

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