黎曼随机梯度下降下双曲神经网络的收敛性

IF 1.4 4区数学 Q2 MATHEMATICS, APPLIED

Communications on Applied Mathematics and Computation Pub Date : 2023-10-05 DOI:10.1007/s42967-023-00302-9

Wes Whiting, Bao Wang, Jack Xin

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引用次数: 0

摘要

摘要在温和的条件下，证明了一类双曲神经网络回归模型的riemanian梯度下降法在批量梯度下降和随机梯度下降下的收敛性。我们还讨论了亚当算法的黎曼版本。我们展示了这些算法在各种基准上的数值模拟。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Convergence of Hyperbolic Neural Networks Under Riemannian Stochastic Gradient Descent

Abstract We prove, under mild conditions, the convergence of a Riemannian gradient descent method for a hyperbolic neural network regression model, both in batch gradient descent and stochastic gradient descent. We also discuss a Riemannian version of the Adam algorithm. We show numerical simulations of these algorithms on various benchmarks.

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来源期刊

Communications on Applied Mathematics and Computation MATHEMATICS, APPLIED-

CiteScore

2.50

自引率

6.20%

发文量

523