bde: A Python Package for Bayesian Deep Ensembles via MILE
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Abstract (arXiv)
bde is a user-friendly Python package for Bayesian Deep Ensembles with a particular focus on tabular data. Built on an efficient JAX implementation of the sampling-based inference method Microcanonical Langevin Ensembles (MILE), it provides scikit-learn compatible estimators for fast training, efficient Markov Chain Monte Carlo sampling, and uncertainty quantification in both regression and classification tasks.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 13 May 2026.
Builds on
8 of 16 references.
References · 8
- 2021Laplace Redux -- Effortless Bayesian Deep Learningcited by 365
- 2019Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyrocited by 191
- 2024Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AIcited by 62
- 2024BlackJAX: Composable Bayesian inference in JAXcited by 26
- 2024Scalable Bayesian Learning with posteriorscited by 13
- 2025Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networkscited by 10
- 2023Fluctuation without dissipation: Microcanonical Langevin Monte Carlocited by 7
- 2026Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise?cited by 7
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Citing papers per year: 2026: 1.
1 paper.