5.9 Further reading
Weight Uncertainty in Neural Networks, Charles Blundell et al.: This is the paper that introduced BBB, and is one of the key pieces of BDL literature.
Practical Variational Inference for Neural Networks, Alex Graves et al.: An influential paper on the use of variational inference for neural networks, this work introduces a straightforward stochastic variational method that can be applied to a variety of neural network architectures.
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks, José Miguel Hernández-Lobato et al.: Another important work in BDL literature, this work introduced PBP, demonstrating how Bayesian inference can be achieved via more scalable means.
Practical Considerations for Probabilistic Backpropagation, Matt Benatan et al.: In this work, the authors introduce methods for making PBP more practical for real-world applications.
Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning, Matt Benatan...