Adaptive multiple optimal learning factors for neural network training
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913384972156928 |
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| author | Challagundla, Jeshwanth |
| author_facet | Challagundla, Jeshwanth |
| contents | This thesis presents a novel approach to neural network training that addresses the challenge of determining the optimal number of learning factors. The proposed Adaptive Multiple Optimal Learning Factors (AMOLF) algorithm dynamically adjusts the number of learning factors based on the error change per multiply, leading to improved training efficiency and accuracy. The thesis also introduces techniques for grouping weights based on the curvature of the objective function and for compressing large Hessian matrices. Experimental results demonstrate the superior performance of AMOLF compared to existing methods like OWO-MOLF and Levenberg-Marquardt. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06583 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive multiple optimal learning factors for neural network training Challagundla, Jeshwanth Machine Learning Artificial Intelligence This thesis presents a novel approach to neural network training that addresses the challenge of determining the optimal number of learning factors. The proposed Adaptive Multiple Optimal Learning Factors (AMOLF) algorithm dynamically adjusts the number of learning factors based on the error change per multiply, leading to improved training efficiency and accuracy. The thesis also introduces techniques for grouping weights based on the curvature of the objective function and for compressing large Hessian matrices. Experimental results demonstrate the superior performance of AMOLF compared to existing methods like OWO-MOLF and Levenberg-Marquardt. |
| title | Adaptive multiple optimal learning factors for neural network training |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.06583 |