Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models-Reference-Cited by-同舟云学术

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Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models

Published:2024-12 Issue: Volume:432 Page:117359
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Anantha Padmanabha Govinda,Fuhg Jan Niklas,Safta Cosmin,Jones Reese E.^ORCID,Bouklas Nikolaos^ORCID

Publisher

Elsevier BV

Reference56 articles.

1. Monte Carlo implementation;Neal,1996

2. Advances in variational inference;Zhang;IEEE Trans. Pattern Anal. Mach. Intell.,2018

3. Stein variational gradient descent: A general purpose bayesian inference algorithm;Liu;Adv. Neural Inf. Process. Syst.,2016

4. A stochastic stein variational Newton method;Leviyev,2022

5. Projected stein variational Newton: A fast and scalable Bayesian inference method in high dimensions;Chen;Adv. Neural Inf. Process. Syst.,2019

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