Sparsity in long-time control of neural ODEs

We consider the neural ODE and optimal control perspective of supervised learning, with ℓ1-control penalties, where rather than only minimizing a final cost (the empirical risk) for the state, we integrate this cost over the entire time horizon. We prove that any optimal control (for this cost) vani...

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Bibliographic Details

Published in:	Systems & control letters Vol. 172; p. 105452
Main Authors:	Esteve-Yagüe, Carlos, Geshkovski, Borjan
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.02.2023
Subjects:	Learning Neural ODEs Optimal Control Sparsity Neural ODEs Learning Sparsity Optimal Control
ISSN:	0167-6911, 1872-7956
Online Access:	Get full text
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