Bayesian neural networks without Bayes' rule

Introduction Deep neural networks (DNNs) are great at fitting data, but terrible at predicting when they don&rsquo;t know the right answer. Tackling this latter challenge (often called uncertainty quantification) is at the core of various problems in AI/ML. For example, reducing hallucinations in large language models, 1 or the classical balancing of exploration versus exploitation in…