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Bayesian Neural Networks: An Introduction and Survey - 2020

Bayesian Neural Networks: An Introduction And Survey

Research Area:  Machine Learning


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.


Author(s) Name:  Ethan Goan,Clinton Fookes

Journal name:  Case Studies in Applied Bayesian Data Science

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/978-3-030-42553-1_3

Volume Information:  pp 45-87