Bayesian Neural Networks: An Introduction and Survey

Ethan Goan; Clinton Fookes

doi:10.1007/978-3-030-42553-1_3

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arXiv · 2006.12024

Bayesian Neural Networks: An Introduction and Survey

Ethan Goan·Clinton Fookes

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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.

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Ethan Goan, Clinton Fookes. 2020-06-22. Bayesian Neural Networks: An Introduction and Survey. https://doi.org/10.1007/978-3-030-42553-1_3

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