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@unpublished{faucris.316849127,
abstract = {Neural ordinary differential equations (neural ODEs) have emerged as a natural tool for supervised learning from a control perspective, yet a complete understanding of their optimal architecture remains elusive. In this work, we examine the interplay between their width [katex]p[/katex] and number of layer transitions [katex]L[/katex] (effectively the depth [katex]L+1[/katex]). Specifically, we assess the model expressivity in terms of its capacity to interpolate either a finite dataset [katex]\mathcal{D}[/katex] comprising [katex]N[/katex] pairs of points or two probability measures in [katex]\mathbb{R}^d[/katex] within a Wasserstein error margin [katex]\varepsilon>0[/katex]. Our findings reveal a balancing trade-off between [katex]p[/katex] and [katex]L[/katex], with [katex]L[/katex] scaling as [katex]O(1+N/p)[/katex] for dataset interpolation, and [katex]L=O\left(1+(p\varepsilon^d)^{-1}\right)[/katex] for measure interpolation. In the autonomous case, where [katex]L=0[/katex], a separate study is required, which we undertake focusing on dataset interpolation. We address the relaxed problem of [katex]\varepsilon[/katex]-approximate controllability and establish an error decay of [katex]\varepsilon\sim O(\log(p)p^{-1/d})[/katex]. This decay rate is a consequence of applying a universal approximation theorem to a custom-built Lipschitz vector field that interpolates [katex]\mathcal{D}[/katex]. In the high-dimensional setting, we further demonstrate that [katex]p=O(N)[/katex] neurons are likely sufficient to achieve exact control.},
author = {Alvarez-Lopez, Antonio and Hadj Slimane, Arselane and Zuazua Iriondo, Enrique},
doi = {10.48550/arXiv.2401.09902},
faupublication = {yes},
keywords = {Neural ODEs; Depth; Width; Simultaneous controllability; Transport equation; Wasserstein distance},
note = {https://cris.fau.de/converis/publicweb/Publication/316849127},
peerreviewed = {automatic},
title = {{Interplay} between depth and width for interpolation in neural {ODEs}},
year = {2024}
}