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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.16334 |
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| _version_ | 1866918366986371072 |
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| author | Strunk, Alexander Assam, Roland |
| author_facet | Strunk, Alexander Assam, Roland |
| contents | This paper introduces Higher Gauge Flow Models, a novel class of Generative Flow Models. Building upon ordinary Gauge Flow Models (arXiv:2507.13414), these Higher Gauge Flow Models leverage an L$_{\infty}$-algebra, effectively extending the Lie Algebra. This expansion allows for the integration of the higher geometry and higher symmetries associated with higher groups into the framework of Generative Flow Models. Experimental evaluation on a Gaussian Mixture Model dataset revealed substantial performance improvements compared to traditional Flow Models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16334 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Higher Gauge Flow Models Strunk, Alexander Assam, Roland Artificial Intelligence Machine Learning Differential Geometry This paper introduces Higher Gauge Flow Models, a novel class of Generative Flow Models. Building upon ordinary Gauge Flow Models (arXiv:2507.13414), these Higher Gauge Flow Models leverage an L$_{\infty}$-algebra, effectively extending the Lie Algebra. This expansion allows for the integration of the higher geometry and higher symmetries associated with higher groups into the framework of Generative Flow Models. Experimental evaluation on a Gaussian Mixture Model dataset revealed substantial performance improvements compared to traditional Flow Models. |
| title | Higher Gauge Flow Models |
| topic | Artificial Intelligence Machine Learning Differential Geometry |
| url | https://arxiv.org/abs/2507.16334 |