7.5 Further reading
Practical Considerations for Probabilistic Backpropagation, Matt Benatan et al.: In this paper, the authors explore methods to get the most out of PBP, demonstrating how different early stopping approaches can be used to improve training, exploring the tradeoffs associated with mini-batching, and more
Modeling aleatoric and epistemic uncertainty using TensorFlow and TensorFlow Probability, Alexander Molak: In this Jupyter notebook, the author shows how to model aleatoric and epistemic uncertainty on regression toy data
Weight Uncertainty in Neural Networks, Charles Blundell et al.: In this paper, the authors introduce BBB, which we use in the regression case study and is one of the key pieces of BDL literature
Deep Deterministic Uncertainty: A Simple Baseline, Jishnu Mukhoti et al.: In this work, the authors describe several experiments related to the different types of uncertainty and introduce the AmbiguousMNIST dataset that we used in the last case study
Uncertainty...