  Transfer learning considers a learning process where a new task is solved by
transferring relevant knowledge from known solutions to related tasks. While
this has been studied experimentally, there lacks a foundational description of
the transfer learning problem that exposes what related tasks are, and how they
can be exploited. In this work, we present a definition for relatedness between
tasks and identify foliations as a mathematical framework to represent such
relationships.
