  Continuous deep learning architectures have recently re-emerged as Neural
Ordinary Differential Equations (Neural ODEs). This infinite-depth approach
theoretically bridges the gap between deep learning and dynamical systems,
offering a novel perspective. However, deciphering the inner working of these
models is still an open challenge, as most applications apply them as generic
black-box modules. In this work we "open the box", further developing the
continuous-depth formulation with the aim of clarifying the influence of
several design choices on the underlying dynamics.
