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arXiv · 2002.08071

Dissecting Neural ODEs

Stefano Massaroli·Michael Poli·Jinkyoo Park·Atsushi Yamashita·Hajime Asama
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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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Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, Hajime Asama. 2021-01-11. Dissecting Neural ODEs. https://arxiv.org/abs/2002.08071

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