Gradient directions and relative inexactness in optimization and machine learning
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914852777230336 |
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| author | Vasin, Artem |
| author_facet | Vasin, Artem |
| contents | In this paper, we investigate the influence of noise giving an estimate of the gradient having a acute angle with the original. Noise amplitude has a relative model. The work offers both theoretical calculations and theorems, as well as experimental results. Classic machine learning problems were chosen as experiments -- linear and logistic regression, computer vision and natural language processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00667 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gradient directions and relative inexactness in optimization and machine learning Vasin, Artem Optimization and Control In this paper, we investigate the influence of noise giving an estimate of the gradient having a acute angle with the original. Noise amplitude has a relative model. The work offers both theoretical calculations and theorems, as well as experimental results. Classic machine learning problems were chosen as experiments -- linear and logistic regression, computer vision and natural language processing. |
| title | Gradient directions and relative inexactness in optimization and machine learning |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2407.00667 |