Anderson-type acceleration method for Deep Neural Network optimization
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917136453074944 |
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| author | Ito, Kazufumi Xue, Tiancheng |
| author_facet | Ito, Kazufumi Xue, Tiancheng |
| contents | In this paper we consider the neural network optimization. We develop Anderson-type acceleration method for the stochastic gradient decent method and it improves the network permanence very much. We demonstrate the applicability of the method for Deep Neural Network (DNN) and Convolution Neural Network (CNN). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Anderson-type acceleration method for Deep Neural Network optimization Ito, Kazufumi Xue, Tiancheng Numerical Analysis Optimization and Control In this paper we consider the neural network optimization. We develop Anderson-type acceleration method for the stochastic gradient decent method and it improves the network permanence very much. We demonstrate the applicability of the method for Deep Neural Network (DNN) and Convolution Neural Network (CNN). |
| title | Anderson-type acceleration method for Deep Neural Network optimization |
| topic | Numerical Analysis Optimization and Control |
| url | https://arxiv.org/abs/2510.20254 |