AI RESEARCH
Approximation properties of neural ODEs
arXiv CS.LG
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ArXi:2503.15696v3 Announce Type: replace-cross We study the approximation properties of neural ordinary differential equations (neural ODEs) in the space of continuous functions. Since a neural ODE requires input and output dimensions to be the same, while input and output dimensions of a continuous function are generally different, we need to embed an input into the latent space of the neural ODE, and to project the output of the neural ODE into the output space.