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Oral
Dissecting Neural ODEs
Stefano Massaroli · Michael Poli · Jinkyoo Park · Atsushi Yamashita · Hajime Asama

Tue Dec 08 06:30 AM -- 06:45 AM (PST) @ Orals & Spotlights: Dynamical Sys/Density/Sparsity

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.

#### Author Information

##### Michael Poli (KAIST)

My work spans topics in deep learning, dynamical systems, variational inference and numerical methods. I am broadly interested in ensuring the successes achieved by deep learning methods in computer vision and natural language are extended to other engineering domains.