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About Priors in Bayesian Learning
This document reviews different prior choices for Bayesian deep learning models, which is an important but often overlooked component of Bayesian inference. It discusses how uninformative priors are commonly used in Bayesian deep learning due to being an easy default, but that this can be problematic as prior misspecification undermines the desirable theoretical properties of Bayesian methods and hurts empirical performance. The document then provides an overview of alternative informative prior distributions that have been proposed for Gaussian processes, variational autoencoders, and Bayesian neural networks, arguing they are worth considering over standard uninformative priors. It concludes by outlining methods for learning priors from data.
Detailed Information
| Author: | ['Inigo Arregui'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 26 |
| Language: | English |
| Format: | |
| Price: | FREE |
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