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On Signal-to-Noise Ratio Issues in Variational Inference for Deep
  Gaussian Processes
v1v2 (latest)

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes

International Conference on Machine Learning (ICML), 2020
1 November 2020
Tim G. J. Rudner
Oscar Key
Y. Gal
Tom Rainforth
ArXiv (abs)PDFHTML

Papers citing "On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes"

3 / 3 papers shown
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance SamplingConference on Uncertainty in Artificial Intelligence (UAI), 2024
Jian Xu
Shian Du
Junmei Yang
Qianli Ma
Delu Zeng
John Paisley
BDL
561
3
0
13 Aug 2024
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian ProcessesIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023
Jian Xu
Shian Du
Junmei Yang
Qianli Ma
Delu Zeng
BDL
377
6
0
22 Sep 2023
Linear Convergence of Black-Box Variational Inference: Should We Stick
  the Landing?
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Kyurae Kim
Yian Ma
Jacob R. Gardner
562
10
0
27 Jul 2023
1
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