Applications of fractional calculus in learned optimization
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916492177571840 |
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| author | Szente, Teodor Alexandru Harrison, James Zanfir, Mihai Sminchisescu, Cristian |
| author_facet | Szente, Teodor Alexandru Harrison, James Zanfir, Mihai Sminchisescu, Cristian |
| contents | Fractional gradient descent has been studied extensively, with a focus on its ability to extend traditional gradient descent methods by incorporating fractional-order derivatives. This approach allows for more flexibility in navigating complex optimization landscapes and offers advantages in certain types of problems, particularly those involving non-linearities and chaotic dynamics. Yet, the challenge of fine-tuning the fractional order parameters remains unsolved. In this work, we demonstrate that it is possible to train a neural network to predict the order of the gradient effectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14855 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Applications of fractional calculus in learned optimization Szente, Teodor Alexandru Harrison, James Zanfir, Mihai Sminchisescu, Cristian Machine Learning Fractional gradient descent has been studied extensively, with a focus on its ability to extend traditional gradient descent methods by incorporating fractional-order derivatives. This approach allows for more flexibility in navigating complex optimization landscapes and offers advantages in certain types of problems, particularly those involving non-linearities and chaotic dynamics. Yet, the challenge of fine-tuning the fractional order parameters remains unsolved. In this work, we demonstrate that it is possible to train a neural network to predict the order of the gradient effectively. |
| title | Applications of fractional calculus in learned optimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.14855 |