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

Learning to compute inner consensus: A novel approach to modeling agreement between Capsules