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

• URL: http://arxiv.org/abs/2007.06823v3
• Date: Mon, 3 Jan 2022 08:37:30 GMT
• Title: Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users
• Authors: Laurent Valentin Jospin and Wray Buntine and Farid Boussaid and Hamid Laga and Mohammed Bennamoun
• Abstract summary: 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.
• Score: 27.764388500937983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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