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Dissecting Neural ODEs

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arxiv 2002.08071 v4 pith:YCEMF3RL submitted 2020-02-19 cs.LG cs.NEstat.ML

Stefano Massaroli , Michael Poli , Jinkyoo Park , Atsushi Yamashita , Hajime Asama This is my paper · ORCID
classification cs.LGcs.NEstat.ML
keywords neuraldeeplearningodesopenapplicationsapplyapproach
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Generalization Bound for a General Class of Neural Ordinary Differential Equations

    cs.LG 2025-08 reject novelty 6.0 of 10

    Claims a first generalization bound for nonlinear neural ODEs, but bounds the complexity of time trajectories rather than input-output maps, leaving the main theorem unproven.

  2. Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling

    cs.LG 2025-07 conditional novelty 4.0 of 10

    Legendre-polynomial weight parameterization lowers training cost and improves stability in continuous-time network surrogates, but the reported accuracy advantage conflicts with the paper's own error table.

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