Reference
Laplace Redux - Effortless Bayesian Deep Learning, Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig. Advances in Neural Information Processing Systems, 2021(2022)

Abstract

We argue that the Laplace approximation is a simple yet competitive and versatile method for Bayesian deep learning that deserves wider adoption. We provide a plug'n'play library for PyTorch to...

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Scalable Bayesian Deep Learning with Modern Laplace Approximations

Training Bayesian neural networks using the Laplacian method for Gaussian posterior approximation.

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An introduction to Bayesian methods in ML

A two-day workshop introducing Bayesian modelling, using practical examples and probabilistic programming.

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