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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Published in | Systems & control letters Vol. 172; p. 105452 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.02.2023
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Subjects | |
Online Access | Get full text |
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