Layer wise Scaled Gaussian Priors for Markov Chain Monte Carlo Sampled deep Bayesian neural networks

Previous work has demonstrated that initialization is very important for both fitting a neural network by gradient descent methods, as well as for Variational inference of Bayesian neural networks. In this work we investigate how Layer wise Scaled Gaussian Priors perform with Markov Chain Monte Carl...

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Autori principali:	Devesh Jawla, John Kelleher
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Frontiers Media S.A. 2025-04-01
Serie:	Frontiers in Artificial Intelligence
Soggetti:	Bayesian neural network (BNN) Markov Chain Monte Carlo (MCMC) deep learning artificial intelligence neural network Bayesian inference (BI)
Accesso online:	https://www.frontiersin.org/articles/10.3389/frai.2025.1444891/full
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Layer wise Scaled Gaussian Priors for Markov Chain Monte Carlo Sampled deep Bayesian neural networks

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