Dissecting Neural ODEs

Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, Hajime Asama

Oral presentation: Orals & Spotlights Track 06: Dynamical Sys/Density/Sparsity
on 2020-12-08T06:30:00-08:00 - 2020-12-08T06:45:00-08:00
Poster Session 2 (more posters)
on 2020-12-08T09:00:00-08:00 - 2020-12-08T11:00:00-08:00
GatherTown: Learning theory ( Town D1 - Spot A2 )
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Abstract: 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.

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