IEEE Computational Intelligence Magazine - May 2022 - 29
Michael Margaliot
Tel Aviv University, Israel
Hands-On Bayesian Neural
Networks-A Tutorial
for Deep Learning Users
Laurent Valentin Jospin
University of Western Australia, AUSTRALIA
Hamid Laga
Murdoch University, AUSTRALIA
Farid Boussaid
University of Western Australia, AUSTRALIA
Wray Buntine
Monash University, AUSTRALIA
Mohammed Bennamoun
University of Western Australia, AUSTRALIA
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.
I. Introduction
D
Digital Object Identifier 10.1109/MCI.2022.3155327
Date of current version: 12 April 2022
1556-603X/22©2022IEEE
eep learning has led to a revolution in machine learning, providing
solutions to tackle problems that were traditionally difficult to solve.
However, deep learning models are prone to overfitting, which
adversely affects their generalization capabilities [1]. They also tend
Corresponding author: Laurent Valentin Jospin (e-mail: laurent.jospin@research.uwa.edu.au).
MAY 2022 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 29
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IEEE Computational Intelligence Magazine - May 2022
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