When are Neural ODE Solutions Proper ODEs?

Katharina Ott
Katharina Ott
Prateek Katiyar
Prateek Katiyar
Michael Tiemann
Michael Tiemann
Cited by: 0|Views9

Abstract:

A key appeal of the recently proposed Neural Ordinary Differential Equation(ODE) framework is that it seems to provide a continuous-time extension of discrete residual neural networks. As we show herein, though, trained Neural ODE models actually depend on the specific numerical method used during training. If the trained model is suppo...More

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