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arXiv · 2007.06823

Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users

Laurent Valentin Jospin·Wray Buntine·Farid Boussaid·Hamid Laga·Mohammed Bennamoun
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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 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.

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BibTeXRIS

Laurent Valentin Jospin, Wray Buntine, Farid Boussaid, Hamid Laga, Mohammed Bennamoun. 2020-07-14. Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users. https://doi.org/10.1109/mci.2022.3155327

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