Sampling-based inference for Bayesian neural networks has achieved computational parity with optimization-based methods and should be prioritized to deliver better uncertainty quantification and model insights.
bde: A Python Package for Bayesian Deep Ensembles via MILE
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abstract
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.
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cs.LG 1years
2026 1verdicts
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Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning
Sampling-based inference for Bayesian neural networks has achieved computational parity with optimization-based methods and should be prioritized to deliver better uncertainty quantification and model insights.