  This project considers Capsule Networks, a recently introduced machine
learning model that has shown promising results regarding generalization and
preservation of spatial information with few parameters. The Capsule Network's
inner routing procedures thus far proposed, a priori, establish how the routing
relations are modeled, which limits the expressiveness of the underlying model.
In this project, we propose two distinct ways in which the routing procedure
can be learned like any other network parameter.
