mlsanityData to 7 Oct 2026

Approximate blocked Gibbs sampling for Bayesian neural networks

arXiv 2208.11389 ↗First posted 24 Aug 2022stat.ML cs.LG stat.COcited by 3

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Abstract (arXiv)

In this work, minibatch MCMC sampling for feedforward neural networks is made more feasible. To this end, it is proposed to sample subgroups of parameters via a blocked Gibbs sampling scheme. By partitioning the parameter space, sampling is possible irrespective of layer width. It is also possible to alleviate vanishing acceptance rates for increasing depth by reducing the proposal variance in deeper layers. Increasing the length of a non-convergent chain increases the predictive accuracy in classification tasks, so avoiding vanishing acceptance rates and consequently enabling longer chain runs have practical benefits. Moreover, non-convergent chain realizations aid in the quantification of predictive uncertainty. An open problem is how to perform minibatch MCMC sampling for feedforward neural networks in the presence of augmented data.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 44 references that are in the map. First posted on arXiv 24 Aug 2022.

Builds on

14 of 44 references.

References · 14

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  10. 2019Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splittingcited by 22
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2 more
  1. 2021Structured Stochastic Gradient MCMCcited by 12
  2. 2013Reversible Jump MCMC Simulated Annealing for Neural Networkscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2024Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AIcited by 60
  2. 2026On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriorscited by 2
  3. 2026Guiding Posterior Exploration with Optimizer-Derived Geometrycited by 0