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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.24878 |
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| _version_ | 1866918470680051712 |
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| author | Hu, Jerry Yao-Chieh Lu, Mingcheng Lee, Yi-Chen Liu, Han |
| author_facet | Hu, Jerry Yao-Chieh Lu, Mingcheng Lee, Yi-Chen Liu, Han |
| contents | We provide a systematic recipe for translating ReLU approximation results to softmax attention mechanism. This recipe covers many common approximation targets. Importantly, it yields target-specific, economic resource bounds beyond universal approximation statements. We showcase the recipe on multiplication, reciprocal computation, and min/max primitives. These results provide new analytical tools for analyzing softmax transformer models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24878 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Transformer Approximations from ReLUs Hu, Jerry Yao-Chieh Lu, Mingcheng Lee, Yi-Chen Liu, Han Machine Learning Artificial Intelligence We provide a systematic recipe for translating ReLU approximation results to softmax attention mechanism. This recipe covers many common approximation targets. Importantly, it yields target-specific, economic resource bounds beyond universal approximation statements. We showcase the recipe on multiplication, reciprocal computation, and min/max primitives. These results provide new analytical tools for analyzing softmax transformer models. |
| title | Transformer Approximations from ReLUs |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.24878 |