Követés
Jiaxin Shi
Jiaxin Shi
További nevek石 佳欣
Research Scientist, Google DeepMind
E-mail megerősítve itt: google.com - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
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
6292016
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
4732019
Functional variational bayesian neural networks
S Sun*, G Zhang*, J Shi*, R Grosse
International Conference on Learning Representations, 2019
3162019
A spectral approach to gradient estimation for implicit distributions
J Shi, S Sun, J Zhu
International Conference on Machine Learning, 4644-4653, 2018
1022018
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
992018
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
982016
Kernel implicit variational inference
J Shi*, S Sun*, J Zhu
International Conference on Learning Representations, 2017
632017
Sparse orthogonal variational inference for Gaussian processes
J Shi, M Titsias, A Mnih
International Conference on Artificial Intelligence and Statistics, 1932-1942, 2020
552020
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
472017
Nonparametric score estimators
Y Zhou, J Shi, J Zhu
International Conference on Machine Learning, 11513-11522, 2020
332020
Sampling with mirrored Stein operators
J Shi, C Liu, L Mackey
International Conference on Learning Representations, 2021
262021
A finite-particle convergence rate for stein variational gradient descent
J Shi, L Mackey
Advances in Neural Information Processing Systems 36, 2023
252023
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
242018
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
232022
Neuralef: Deconstructing kernels by deep neural networks
Z Deng, J Shi, J Zhu
International Conference on Machine Learning, 4976-4992, 2022
222022
Simplified and Generalized Masked Diffusion for Discrete Data
J Shi, K Han, Z Wang, A Doucet, MK Titsias
arXiv preprint arXiv:2406.04329, 2024
212024
Scalable training of inference networks for gaussian-process models
J Shi, ME Khan, J Zhu
International Conference on Machine Learning, 5758-5768, 2019
202019
Sequence modeling with multiresolution convolutional memory
J Shi, KA Wang, E Fox
International Conference on Machine Learning, 31312-31327, 2023
152023
Neural eigenfunctions are structured representation learners
Z Deng, J Shi, H Zhang, P Cui, C Lu, J Zhu
arXiv preprint arXiv:2210.12637, 2022
122022
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
102022
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