  Dataflow matrix machines are a powerful generalization of recurrent neural
networks. They work with multiple types of arbitrary linear streams, multiple
types of powerful neurons, and allow to incorporate higher-order constructions.
We expect them to be useful in machine learning and probabilistic programming,
and in the synthesis of dynamic systems and of deterministic and probabilistic
programs.
