An Efficient Fisher Matrix Approximation Method for Large-Scale Neural Network Optimization

Author:

Yang Minghan1,Xu Dong2,Cui Qiwen3,Wen Zaiwen4ORCID,Xu Pengxiang5

Affiliation:

1. DAMO Academy, Alibaba Group, China

2. Beijing International Center for Mathematical Research, Peking University, China

3. Paul G. Allen School of Computer Science & Engineering, University of Washington, USA

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

5. Peng Cheng Laboratory, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Reference33 articles.

1. Topmoumoute online natural gradient algorithm;roux;Proc Adv Neural Inf Process Syst,2008

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

3. Optimizing neural networks with kronecker-factored approximate curvature;martens;Proc Int Conf Mach Learn,2015

4. Logarithmic regret algorithms for online convex optimization

5. Quantum chemistry structures and properties of 134 kilo molecules;ramakrishnan;Data Science Journal,2014

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1. Riemannian Natural Gradient Methods;SIAM Journal on Scientific Computing;2024-01-22

2. Convergence Analysis of an Adaptively Regularized Natural Gradient Method;IEEE Transactions on Signal Processing;2024

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