Hierarchical Bayesian Neural Networks with Informative Priors

Imagine you have a machine learning (ML) problem but only small data (gasp, yes, this does exist). This often happens when your data set is nested -- you might have many data points, but only few per category. For example, in ad-tech you may want predict how likely a user will buy a certain product. There could be thousands of products but you only have a small number of measurements (e.g. Likely, there will be similarities between each product category, but they will all have individual differences too.