An Adaptive Deep Learning Optimization Method Based on Radius of Curvature

An adaptive clamping method (SGD-MS) based on the radius of curvature is designed to alleviate the local optimal oscillation problem in deep neural network, which combines the radius of curvature of the objective function and the gradient descent of the optimizer. The radius of curvature is consider...

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Autores principales: Jiahui Zhang, Xinhao Yang, Ke Zhang, Chenrui Wen
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Lenguaje:EN
Publicado: Hindawi Limited 2021
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Acceso en línea:https://doaj.org/article/acab22f5e532433c807e6c5f9bb9d3fc
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spelling oai:doaj.org-article:acab22f5e532433c807e6c5f9bb9d3fc2021-11-22T01:10:33ZAn Adaptive Deep Learning Optimization Method Based on Radius of Curvature1687-527310.1155/2021/9882068https://doaj.org/article/acab22f5e532433c807e6c5f9bb9d3fc2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9882068https://doaj.org/toc/1687-5273An adaptive clamping method (SGD-MS) based on the radius of curvature is designed to alleviate the local optimal oscillation problem in deep neural network, which combines the radius of curvature of the objective function and the gradient descent of the optimizer. The radius of curvature is considered as the threshold to separate the momentum term or the future gradient moving average term adaptively. In addition, on this basis, we propose an accelerated version (SGD-MA), which further improves the convergence speed by using the method of aggregated momentum. Experimental results on several datasets show that the proposed methods effectively alleviate the local optimal oscillation problem and greatly improve the convergence speed and accuracy. A novel parameter updating algorithm is also provided in this paper for deep neural network.Jiahui ZhangXinhao YangKe ZhangChenrui WenHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution DOAJ
collection DOAJ
language EN
topic Computer applications to medicine. Medical informatics
R858-859.7
Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
spellingShingle Computer applications to medicine. Medical informatics
R858-859.7
Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
Jiahui Zhang
Xinhao Yang
Ke Zhang
Chenrui Wen
An Adaptive Deep Learning Optimization Method Based on Radius of Curvature
description An adaptive clamping method (SGD-MS) based on the radius of curvature is designed to alleviate the local optimal oscillation problem in deep neural network, which combines the radius of curvature of the objective function and the gradient descent of the optimizer. The radius of curvature is considered as the threshold to separate the momentum term or the future gradient moving average term adaptively. In addition, on this basis, we propose an accelerated version (SGD-MA), which further improves the convergence speed by using the method of aggregated momentum. Experimental results on several datasets show that the proposed methods effectively alleviate the local optimal oscillation problem and greatly improve the convergence speed and accuracy. A novel parameter updating algorithm is also provided in this paper for deep neural network.
format article
author Jiahui Zhang
Xinhao Yang
Ke Zhang
Chenrui Wen
author_facet Jiahui Zhang
Xinhao Yang
Ke Zhang
Chenrui Wen
author_sort Jiahui Zhang
title An Adaptive Deep Learning Optimization Method Based on Radius of Curvature
title_short An Adaptive Deep Learning Optimization Method Based on Radius of Curvature
title_full An Adaptive Deep Learning Optimization Method Based on Radius of Curvature
title_fullStr An Adaptive Deep Learning Optimization Method Based on Radius of Curvature
title_full_unstemmed An Adaptive Deep Learning Optimization Method Based on Radius of Curvature
title_sort adaptive deep learning optimization method based on radius of curvature
publisher Hindawi Limited
publishDate 2021
url https://doaj.org/article/acab22f5e532433c807e6c5f9bb9d3fc
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