A p proxim ate A lgorith m s for N eural-B ayesian A pproaches.
A b stract We describe two specific examples of neural-Bayesian approaches for complex mod­ eling tasks: survival analysis and multitask learning. In both cases, we can come up with reasonable priors on the parameters of the neural network. As a result, the Bayesian approaches improve their (maximum likelihood) frequentist counter­ parts dramatically. By illustrating their application on the models under study, we review and compare algorithms that can be used for Bayesian inference: Laplace approximation, variational algorithms, Monte Carlo sampling, and empirical Bayes.