Partial Answer of How Transformers Learn Automata
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910937073582080 |
|---|---|
| author | Zhang, Tiantian |
| author_facet | Zhang, Tiantian |
| contents | We introduce a novel framework for simulating finite automata using representation-theoretic semidirect products and Fourier modules, achieving more efficient Transformer-based implementations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20395 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Partial Answer of How Transformers Learn Automata Zhang, Tiantian Formal Languages and Automata Theory Machine Learning We introduce a novel framework for simulating finite automata using representation-theoretic semidirect products and Fourier modules, achieving more efficient Transformer-based implementations. |
| title | Partial Answer of How Transformers Learn Automata |
| topic | Formal Languages and Automata Theory Machine Learning |
| url | https://arxiv.org/abs/2504.20395 |