.png){kind=icon}
.png){kind=icon}
Return
Hands-On Bayesian Neural Networks-A Tutorial for Deep Learning Users
.png){kind=icon}
OA
AI
DOI：10.1109/MCI.2022.3155327.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Deep learning
Training data
Uncertainty
Design methodology
Computational modeling
Stochastic processes
Bayes methods
Neural networks
Journal
IF:
11.2
Papers:
606
Citations:
3.1K