Insights from workshop on Bayesian deep learning at neurips 21 - DataScienceCentral.com

Until now, neural networks have been predominantly relying on backpropagation and gradient descent as the inference engine in order to learn a neural network's parameters. This is primarily because closed-form Bayesian inference for neural networks has been considered to be intractable. This short paper outlines a new analytical method for performing tractable approximate Gaussian inference (TAGI) in Bayesian neural networks.