Bayesian Learning for Neural Networks: an algorithmic survey
- URL: http://arxiv.org/abs/2211.11865v2
- Date: Wed, 23 Nov 2022 08:12:48 GMT
- Title: Bayesian Learning for Neural Networks: an algorithmic survey
- Authors: Martin Magris, Alexandros Iosifidis
- Abstract summary: This self-contained survey engages and introduces readers to the principles and algorithms of Bayesian Learning for Neural Networks.
It provides an introduction to the topic from an accessible, practical-algorithmic perspective.
- Score: 95.42181254494287
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The last decade witnessed a growing interest in Bayesian learning. Yet, the
technicality of the topic and the multitude of ingredients involved therein,
besides the complexity of turning theory into practical implementations, limit
the use of the Bayesian learning paradigm, preventing its widespread adoption
across different fields and applications. This self-contained survey engages
and introduces readers to the principles and algorithms of Bayesian Learning
for Neural Networks. It provides an introduction to the topic from an
accessible, practical-algorithmic perspective. Upon providing a general
introduction to Bayesian Neural Networks, we discuss and present both standard
and recent approaches for Bayesian inference, with an emphasis on solutions
relying on Variational Inference and the use of Natural gradients. We also
discuss the use of manifold optimization as a state-of-the-art approach to
Bayesian learning. We examine the characteristic properties of all the
discussed methods, and provide pseudo-codes for their implementation, paying
attention to practical aspects, such as the computation of the gradients
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