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K-FAC is a successful tractable implementation of Natural Gradient for Deep Learning, which nevertheless suffers from the requirement to compute the inverse of the Kronecker factors (through an eigen-decomposition).
Amari, S. I. Natural gradient works efficiently in learning, Neural Computation, 10(20), pp. 251-276 (1998)
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Halko N.; Martinsson P.G.; Tropp J. A. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions (2011)
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Martens, J. New insights and perspectives on the natural gradient method, arXiv:1412.1193 (2020)
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Cited alongside, same era.
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Mazeika M. The Singular Value Decomposition and Low Rank Approximation
2021
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Tang, Z.; Jiang, F.; Gong, M.; Li, H.; Wu, Y.; Yu, F.; Wang, Z.; Wang, M. SKFAC: Training Neural Networks with Faster Kronecker-Factored Approximate Curvature, IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2021)
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2021
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Puiu, C. O. Rethinking Exponential Averaging of the Fisher, arXiv:2204.04718 (2022)
2022
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