Convergence Analysis of an Adaptively Regularized Natural Gradient Method-Reference-Cited by-同舟云学术

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Convergence Analysis of an Adaptively Regularized Natural Gradient Method

Published:2024 Issue: Volume:72 Page:2527-2542
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Wu Jiayuan¹^ORCID,Hu Jiang²^ORCID,Zhang Hongchao³^ORCID,Wen Zaiwen⁴^ORCID

Affiliation:

1. College of Engineering, Peking University, Beijing, China

2. Massachusetts General Hospital and Harvard Medical School, Harvard University, Boston, MA, USA

3. Department of Mathematics, Louisiana State University, Baton Rouge, LA, USA

4. Beijing International Center for Mathematical Research, Center for Machine Learning Research and College of Engineering, Peking University, Beijing, China

Funder

NSF DMS

NSFC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/78/10347386/10527404.pdf?arnumber=10527404

Reference61 articles.

1. New insights and perspectives on the natural gradient method;Martens;J. Mach. Learn. Res.,2020

2. Optimizing neural networks with Kronecker-factored approximate curvature;Martens,2015

3. A Kronecker-factored approximate Fisher matrix for convolution layers;Grosse,2016

4. A Stochastic Trust-Region Framework for Policy Optimization

5. RES: Regularized Stochastic BFGS Algorithm

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