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...

Full description

Saved in:
Bibliographic Details
Published inSystems & control letters Vol. 172; p. 105452
Main Authors Esteve-Yagüe, Carlos, Geshkovski, Borjan
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.02.2023
Subjects
Online AccessGet full text

Cover

Loading…