Metric entropy limits on recurrent neural network learning of linear dynamical systems
BibTeX Reference
Warning: This BibTeX entry is automatically generated from the database. As there is no one-to-one match between the database fields and the BibTeX format, there may be errors in the entry. Regard the BibTeX feature as experimental for now. Use with care and correct errors and omissions manually.Please, do provide feedback about this feature to Michael Lerjen.
@article{ACHA-2021,
author = {Hutter, Clemens and Gül, Recep and Bölcskei, Helmut},
title = {Metric entropy limits on recurrent neural network learning of linear dynamical systems},
journal = {Applied and Computational Harmonic Analysis},
volume = 59,
pages = {198--223},
month = jul,
year = 2022,
keywords = {Recurrent neural networks, linear dynamical systems, metric entropy, Hardy spaces, universal approximation, system identification},
url = {http://www.nari.ee.ethz.ch/pubs/p/ACHA-2021}
}
LaTeX Reference
\bibitem{ACHA-2021} C. Hutter, R. Gül, and H. Bölcskei, ``Metric entropy limits on recurrent neural network learning of linear dynamical systems,'' \emph{Applied and Computational Harmonic Analysis}, Vol. 59, pp. 198-223, July 2022, (\emph{invited paper}).
HTML Reference
C. Hutter, R. Gül, and H. Bölcskei, "Metric entropy limits on recurrent neural network learning of linear dynamical systems," Applied and Computational Harmonic Analysis, Vol. 59, pp. 198-223, July 2022, (invited paper).