# Latent Derivative Bayesian Last Layer Networks

###### tags: `papers`

MAIN IDEAS:
- Approximate inference techniques for weight space priors of BNN’s suffer from several drawbacks
- The ‘Bayesian last layer’ (BLL) is an alternative BNN approach that learns the feature space for an exact Bayesian linear model with explicit predictive distributions.
- Its predictions outside of the data distribution (OOD) are typically overconfident
- Overcome this weakness by introducing a functional prior on the model’s derivatives
- This method enhances the BLL to Gaussian process-like performance on tasks where calibrated uncertainty is critical: OOD regression, Bayesian optimization and active learning