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- W4386735345 abstract "Deep learning has made significant progress in artificial intelligence, providing new solutions to a variety of previously challenging problems. However, standard deep learning algorithms only offer point estimates of the model, which fail to express the model uncertainty, leading to overconfident decisions. To address these issues, the Bayesian approach has been adopted in deep learning, which provides a probabilistic interpretation of deep learning models. However, inferring the Bayesian posterior is frequently challenging. Therefore, approximations of the true posterior with another simple approximate distribution are often employed; this technique is known as the variational inference method. This paper presents an overview of Bayesian Neural Networks (BNNs) using current variational inference methods and attempts to explore the tools necessary to create, apply, train, and evaluate neural networks in a Bayesian framework." @default.
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- W4386735345 date "2023-01-01" @default.
- W4386735345 modified "2023-09-27" @default.
- W4386735345 title "A Review of Variational Inference for Bayesian Neural Network" @default.
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- W4386735345 doi "https://doi.org/10.1007/978-3-031-43520-1_20" @default.
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