Cited by 879Gaussian Processes and Bayesian InferenceAdversarial Robustness in Machine LearningNeural Networks and Applications

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

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Jospin, Laurent Valentin, Laga, Hamid, Boussaid, Farid, Buntine, Wray, Bennamoun, Mohammed·2022·IEEE Computational Intelligence Magazine

DOI 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.

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Authors: Jospin, L. V., Laga, H., Boussaid, F., Buntine, W., & Bennamoun, M.
Affiliations: The University of Western Australia (AU)
Murdoch University (AU)
University of Western Australia, Australia
ORCID: Jospin, Laurent Valentin
Laga, Hamid
Buntine, Wray
Bennamoun, Mohammed
Venue: IEEE Computational Intelligence Magazine
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022-05
Volume: 17
Issue: 2
Pages: 29-48
ISSN: 1556-603X (ISSN-L)
1556-6048
DOI: 10.1109/mci.2022.3155327
OpenAlex: W3043426275
Cited by: 879
References: 141
Citations by year: 2026: 171 · 2025: 217 · 2024: 192 · 2023: 170 · 2022: 80
FWCI: 54.00
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Computational Intelligence Magazine (publishedVersion)
arXiv (Cornell University) (submittedVersion)
UWA Profiles and Research Repository (UWA) (submittedVersion, other-oa)
Murdoch University Research Portal (submittedVersion)
arXiv (Cornell University)
Funders: Australian Research Council (DP150100294, DP150104251, DP210101682)
Air Force Research Laboratory (FA8750-19-2-0501)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords: Bayesian neural networks, uncertainty quantification, forecast uncertainty, black-box models, Bayesian inference, deep learning, stochastic neural networks

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