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Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users

Jospin, L.V., Laga, H.ORCID: 0000-0002-4758-7510, Boussaid, F., Buntine, W. and Bennamoun, M. (2022) Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users. IEEE Computational Intelligence Magazine, 17 (2). pp. 29-48.

Link to Published Version: https://doi.org/10.1109/MCI.2022.3155327
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Abstract

Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging problems. However, since deep learning methods operate as black boxes, the uncertainty associated with their predictions is often challenging to quantify. Bayesian statistics offer a formalism to understand and quantify the uncertainty associated with deep neural network predictions. This tutorial provides deep learning practitioners with an overview of the relevant literature and a complete toolset to design, implement, train, use and evaluate Bayesian neural networks, i . e ., stochastic artificial neural networks trained using Bayesian methods.

Item Type: Journal Article
Murdoch Affiliation(s): IT, Media and Communications
Publisher: IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/64680
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