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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2021
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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) |
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Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 |
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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 |
work_keys_str_mv |
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_version_ |
1718418361543557120 |