[2006.12024] Bayesian Neural Networks: An Introduction and Survey

Statistics > Machine Learning arXiv:2006.12024 (stat) [Submitted on 22 Jun 2020 (v1), last revised 6 Apr 2026 (this version, v2)] Title:Bayesian Neural Networks: An Introduction and Survey Authors:Ethan Goan, Clinton Fookes View a PDF of the paper titled Bayesian Neural Networks: An Introduction and Survey, by Ethan Goan and Clinton Fookes View PDF HTML (experimental) Abstract:Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language processing. Despite their success, they are often implemented in a frequentist scheme, meaning they are unable to reason about uncertainty in their predictions. This article introduces Bayesian Neural Networks (BNNs) and the seminal research regarding their implementation. Different approximate inference methods are compared, and used to highlight where future research can improve on current methods. Comments: Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2006.12024 [stat.ML]   (or arXiv:2006.12024v2 [stat.ML] for this version)   https://doi.org/10.48550/arXiv.2006.12024 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Case Studies in Applied Bayesian Data Science: CIRM Jean-Morlet Chair, Fall 2018, 1, (2020) 45-87 Related DOI: https://doi.org/10.1007/978-3-030-42553-1_3 Focus to learn more DOI(s) linking to related reso...