On the benefit of overparameterisation in state reconstruction: An empirical study of the nonlinear case-Reference-Cited by-同舟云学术

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On the benefit of overparameterisation in state reconstruction: An empirical study of the nonlinear case

Published:2024-02-01 Issue: Volume:PartF16814 Page:49-54
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Container-title:2024 Australian & New Zealand Control Conference (ANZCC)
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Author:

Haderlein Jonas F.¹,Peterson Andre D. H.²,Eskikand Parvin Zarei¹,Burkitt Anthony N.¹,Mareels Iven M. Y.³,Grayden David B.⁴

Affiliation:

1. University of Melbourne,Department of Biomedical Engineering,VIC,Australia,3010

2. The University of Melbourne,St Vincent’s Hospital,Department of Medicine

3. Institute for Innovation, Science and Sustainability, Federation University Australia,Mt Helen,Vic,3350

4. Graeme Clark Institute, University of Melbourne,VIC,Australia,3010

Funder

Australian Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10432770/10432788/10432818.pdf?arnumber=10432818

Reference26 articles.

1. Reconciling modern machine-learning practice and the classical bias–variance trade-off

2. Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning;Liu;arXiv,2020

3. Generalisation error in learning with random features and the hidden manifold model;Gerace

4. Surprises in high-dimensional ridgeless least squares interpolation

5. On the Double Descent of Random Features Models Trained with SGD;Liu,2020

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