The DOI matches its Crossref record; 135 of the 141 references it cites were found in a registry or an index.
Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
Open accessAbstract
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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“BNNs can be designed in different ways regarding the selection of stochastic components, distribution approximation, and inference approaches [74].”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [74]
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Cited by
Sorted by citations
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 3,111
“BNNs/BDL are robust to overfitting problem and can be trained on both small and big datasets [27].”
MethodUncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
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Metadata
- 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
- Venue
- IEEE Computational Intelligence Magazine
- Published
- 2022-05
- Volume
- 17
- Issue
- 2
- Pages
- 29-48
- ISSN
- 1556-603X (ISSN-L)
- 1556-6048
- 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
- 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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