Essays and technical articles, alongside fragments of ideas, reading lists, and interesting finds.
Bayesian Neural Networks offer a principled way to account for uncertainty in neural models. This survey explores generalization and double descent, common posterior approximations, and an application to simulation-based inference in cosmology.
