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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.13457 |
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| _version_ | 1866913847772708864 |
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| author | Faraj, Nathan |
| author_facet | Faraj, Nathan |
| contents | This paper introduces a novel method for optimizing learning rates in machine learning. A previously unrecognized proportionality between learning rates and dataset sizes is discovered, providing valuable insights into how dataset scale influences training dynamics. Additionally, a cumulative learning constant is identified, offering a framework for designing and optimizing advanced learning rate schedules. These findings have the potential to enhance training efficiency and performance across a wide range of machine learning applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13457 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tuning Learning Rates with the Cumulative-Learning Constant Faraj, Nathan Machine Learning This paper introduces a novel method for optimizing learning rates in machine learning. A previously unrecognized proportionality between learning rates and dataset sizes is discovered, providing valuable insights into how dataset scale influences training dynamics. Additionally, a cumulative learning constant is identified, offering a framework for designing and optimizing advanced learning rate schedules. These findings have the potential to enhance training efficiency and performance across a wide range of machine learning applications. |
| title | Tuning Learning Rates with the Cumulative-Learning Constant |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.13457 |