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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.20195 |
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| _version_ | 1866910766250065920 |
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| author | Kozachinskiy, Alexander |
| author_facet | Kozachinskiy, Alexander |
| contents | In this note, we use the VC dimension technique to prove the first lower bound against one-layer softmax transformers with infinite precision. We do so for two tasks: function composition, considered by Peng, Narayanan, and Papadimitriou, and the SUM$_2$ task, considered by Sanford, Hsu, and Telgarsky. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20195 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lower bounds on transformers with infinite precision Kozachinskiy, Alexander Machine Learning Artificial Intelligence In this note, we use the VC dimension technique to prove the first lower bound against one-layer softmax transformers with infinite precision. We do so for two tasks: function composition, considered by Peng, Narayanan, and Papadimitriou, and the SUM$_2$ task, considered by Sanford, Hsu, and Telgarsky. |
| title | Lower bounds on transformers with infinite precision |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.20195 |