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Inverse-Free Fast Natural Gradient Descent Method for Deep Learning
March 7, 2024, 5:41 a.m. | Xinwei Ou, Ce Zhu, Xiaolin Huang, Yipeng Liu
cs.LG updates on arXiv.org arxiv.org
Abstract: Second-order methods can converge much faster than first-order methods by incorporating second-order derivates or statistics, but they are far less prevalent in deep learning due to their computational inefficiency. To handle this, many of the existing solutions focus on reducing the size of the matrix to be inverted. However, it is still needed to perform the inverse operator in each iteration. In this paper, we present a fast natural gradient descent (FNGD) method, which only …
abstract arxiv computational converge cs.cv cs.lg deep learning faster focus free gradient matrix natural solutions statistics the matrix type
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