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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.11776 |
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| _version_ | 1866914973811212288 |
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| author | Bersier, Stephane Chen-Lin, Xinyi |
| author_facet | Bersier, Stephane Chen-Lin, Xinyi |
| contents | Despite the wide variety of input types in machine learning, this diversity is often not fully reflected in their representations or model architectures, leading to inefficiencies throughout a model's lifecycle. This paper introduces an algebraic approach to constructing input-encoding architectures that properly account for the data's structure, providing a step toward achieving more typeful machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11776 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Encoding architecture algebra Bersier, Stephane Chen-Lin, Xinyi Machine Learning Artificial Intelligence Programming Languages Software Engineering Despite the wide variety of input types in machine learning, this diversity is often not fully reflected in their representations or model architectures, leading to inefficiencies throughout a model's lifecycle. This paper introduces an algebraic approach to constructing input-encoding architectures that properly account for the data's structure, providing a step toward achieving more typeful machine learning. |
| title | Encoding architecture algebra |
| topic | Machine Learning Artificial Intelligence Programming Languages Software Engineering |
| url | https://arxiv.org/abs/2410.11776 |