Stebėti
Jiaxin Shi
Jiaxin Shi
Kiti vardai石 佳欣
Research Scientist, Google DeepMind
Patvirtintas el. paštas google.com - Pagrindinis puslapis
Pavadinimas
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
5202016
Functional variational bayesian neural networks
S Sun*, G Zhang*, J Shi*, R Grosse
International Conference on Learning Representations, 2019
2392019
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
2162020
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
872016
A spectral approach to gradient estimation for implicit distributions
J Shi, S Sun, J Zhu
International Conference on Machine Learning, 4644-4653, 2018
842018
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
742018
Kernel implicit variational inference
J Shi*, S Sun*, J Zhu
International Conference on Learning Representations, 2017
582017
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
462017
Sparse orthogonal variational inference for Gaussian processes
J Shi, M Titsias, A Mnih
International Conference on Artificial Intelligence and Statistics, 1932-1942, 2020
422020
Nonparametric score estimators
Y Zhou, J Shi, J Zhu
International Conference on Machine Learning, 11513-11522, 2020
222020
Scalable training of inference networks for gaussian-process models
J Shi, ME Khan, J Zhu
International Conference on Machine Learning, 5758-5768, 2019
192019
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
192018
Sampling with Mirrored Stein Operators
J Shi, C Liu, L Mackey
International Conference on Learning Representations, 2021
142021
Neuralef: Deconstructing kernels by deep neural networks
Z Deng, J Shi, J Zhu
International Conference on Machine Learning, 4976-4992, 2022
102022
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
92022
Neural Eigenfunctions Are Structured Representation Learners
Z Deng*, J Shi*, H Zhang, P Cui, C Lu, J Zhu
arXiv preprint arXiv:2210.12637, 2022
62022
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent
J Shi, L Mackey
arXiv preprint arXiv:2211.09721, 2022
52022
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
S Sun, J Shi, AG Wilson, R Grosse
International Conference on Machine Learning, 2021
52021
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
42022
Neural Networks as Inter-Domain Inducing Points
S Sun*, J Shi*, RB Grosse
Third Symposium on Advances in Approximate Bayesian Inference, 2020
42020
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