Hierarchical Bayesian deep learning gains via multilevel particle marginal Metropolis–Hastings; authors address joint inference of neural-network parameters and priors to improve uncertainty quantification
Read the original at arxiv.org→arXiv:2610.19074v1 Announce Type: new Abstract: Reliable uncertainty quantification remains an important challenge in AI. Hierarchical Bayesian methods provide a principled framework for this by representing unknown...
Original headline: "Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology"
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- Oct 15, 04:00 UTC arXiv cs.LG lead source Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology