Title: Hands-on Bayesian Neural Networks — a Tutorial for Deep Learning Users
Authors: Laurent Valentin Jospin, Wray Buntine, Farid Boussaid, Hamid Laga, Mohammed Bennamoun
Published: 14th July 2020 (Tuesday) @ 05:21:27
Link: http://arxiv.org/abs/2007.06823v3
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.