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Xuechen Li
Xuechen Li
PhD Student, Stanford University
Подтвержден адрес электронной почты в домене stanford.edu - Главная страница
Название
Процитировано
Процитировано
Год
Isolating sources of disentanglement in variational autoencoders
RTQ Chen, X Li, RB Grosse, DK Duvenaud
Advances in neural information processing systems 31, 2018
7372018
On the opportunities and risks of foundation models
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2021
2042021
Inference Suboptimality in Variational Autoencoders
C Cremer, X Li, D Duvenaud
International Conference on Machine Learning, 2018
1972018
Scalable gradients for stochastic differential equations
X Li, TKL Wong, RTQ Chen, D Duvenaud
International Conference on Artificial Intelligence and Statistics, 3870-3882, 2020
1342020
Stochastic runge-kutta accelerates langevin monte carlo and beyond
X Li, Y Wu, L Mackey, MA Erdogdu
Advances in neural information processing systems 32, 2019
372019
Neural sdes as infinite-dimensional gans
P Kidger, J Foster, X Li, TJ Lyons
International Conference on Machine Learning, 5453-5463, 2021
21*2021
When Does Preconditioning Help or Hurt Generalization?
S Amari, J Ba, R Grosse, X Li, A Nitanda, T Suzuki, D Wu, J Xu
arXiv preprint arXiv:2006.10732, 2020
162020
Scalable gradients and variational inference for stochastic differential equations
X Li, TKL Wong, RTQ Chen, DK Duvenaud
Symposium on Advances in Approximate Bayesian Inference, 1-28, 2020
152020
Large language models can be strong differentially private learners
X Li, F Tramer, P Liang, T Hashimoto
arXiv preprint arXiv:2110.05679, 2021
142021
Infinitely deep bayesian neural networks with stochastic differential equations
W Xu, RTQ Chen, X Li, D Duvenaud
International Conference on Artificial Intelligence and Statistics, 721-738, 2022
102022
Efficient and accurate gradients for neural sdes
P Kidger, J Foster, XC Li, T Lyons
Advances in Neural Information Processing Systems 34, 2021
42021
Learning to Extend Program Graphs to Work-in-Progress Code
X Li, CJ Maddison, D Tarlow
arXiv preprint arXiv:2105.14038, 2021
2021
The idemetric property: when most distances are (almost) the same
G Barmpalias, N Huang, A Lewis-Pye, A Li, X Li, Y Pan, T Roughgarden
Proceedings of the Royal Society A, 2019
2019
Isolating Sources of Disentanglement in VAEs
RTQ Chen, X Li, R Grosse, D Duvenaud
Proceedings of the 32nd International Conference on Neural Information …, 2019
2019
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