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Hands-On Bayesian Neural Networks-A Tutorial for Deep Learning Users
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DOI：10.1109/MCI.2022.3155327.png)
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摘要
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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.
Keyword:
Deep learning
Training data
Uncertainty
Design methodology
Computational modeling
Stochastic processes
Bayes methods
Neural networks
期刊
IF:
11.2
论文数:
620
被引数:
3.1K
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