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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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The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning
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Variational Bayesian dropout: pitfalls and fixesPreprint
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Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate ShiftPreprint
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Bayesian Inference over the Stiefel Manifold via the Givens Representation
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Seedat, N., & Kanan, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00104 · Cited by 21
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“BNNs/BDL are robust to overfitting problem and can be trained on both small and big datasets [27].”
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Improving the open cluster census
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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