  Bayesian interpretations of neural network have a long history, dating back
to early work in the 1990's and have recently regained attention because of
their desirable properties like uncertainty estimation, model robustness and
regularisation. We want to discuss here the application of Bayesian models to
knowledge sharing between neural networks. Knowledge sharing comes in different
facets, such as transfer learning, model distillation and shared embeddings.
All of these tasks have in common that learned "features" ought to be shared
across different networks. Theoretically rooted in the concepts of Bayesian
neural networks this work has widespread application to general deep learning.
