A research team from ETH Zürich presents an overview of priors for (deep) Gaussian processes, variational autoencoders and Bayesian neural networks. The researchers propose that well-chosen priors can achieve theoretical and empirical properties such as uncertainty estimation, model selection and optimal decision support; and provide guidance on how to choose them.

Here is a quick read: ETH Zürich Identifies Priors That Boost Bayesian Deep Learning Models.

The paper Priors in Bayesian Deep Learning: A Review is on arXiv.



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