Dissecting Neural ODEs

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.

Authors

• Stefano Massaroli
• Michael Poli
• Jinkyoo Park Korea Advanced Institute of Science and Technology
• Atsushi Yamashita
• Hajime Asama

Keywords

No keywords are indexed for this paper.