Dissecting Neural ODEs

Continuous deep learning architectures have recently re-emerged as Neural Ordinary Differential Equations (Neural ODEs). 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.