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

L.V. Jospin; H. Laga; F. Boussaid; W. Buntine; M. Bennamoun

doi:10.1109/MCI.2022.3155327

Back

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

Journal article

Peer reviewed

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

L.V. Jospin, H. Laga, F. Boussaid, W. Buntine and M. Bennamoun

IEEE Computational Intelligence Magazine, Vol.17(2), pp.29-48

2022

DOI: https://doi.org/10.1109/MCI.2022.3155327

Files and links (1)

url

Link to Published Version *Subscription may be requiredView

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.

Details

Title: Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
Authors/Creators: L.V. Jospin (Author/Creator) - The University of Western Australia
H. Laga (Author/Creator) - The University of Western Australia
F. Boussaid (Author/Creator) - The University of Western Australia
W. Buntine (Author/Creator) - Murdoch University
M. Bennamoun (Author/Creator) - Monash University
Publication Details: IEEE Computational Intelligence Magazine, Vol.17(2), pp.29-48
Publisher: IEEE
Identifiers: 991005544705807891
Murdoch Affiliation: School of Information Technology
Language: English
Resource Type: Journal article

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

Source: InCites

Metrics

130 Record Views

504 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Highly Cited Paper

Collaboration types: Domestic collaboration
Citation topics: 4 Electrical Engineering, Electronics & Computer Science; 4.17 Computer Vision & Graphics; 4.17.128 Deep Visual Recognition
Web Of Science research areas: Computer Science, Artificial Intelligence
ESI research areas: Computer Science