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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2405.04212 |
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| _version_ | 1866909192867020800 |
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| author | Glimsdal, Sondre Østby, Sebastian Brambo, Tobias M. Vinje, Eirik M. |
| author_facet | Glimsdal, Sondre Østby, Sebastian Brambo, Tobias M. Vinje, Eirik M. |
| contents | Green Tsetlin (GT) is a Tsetlin Machine (TM) framework developed to solve real-world problems using TMs. Several frameworks already exist that provide access to TM implementations. However, these either lack features or have a research-first focus. GT is an easy-to-use framework that aims to lower the complexity and provide a production-ready TM implementation that is great for experienced practitioners and beginners. To this end, GT establishes a clear separation between training and inference. A C++ backend with a Python interface provides competitive training and inference performance, with the option of running in pure Python. It also integrates support for critical components such as exporting trained models, hyper-parameter search, and cross-validation out-of-the-box. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04212 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Green Tsetlin Redefining Efficiency in Tsetlin Machine Frameworks Glimsdal, Sondre Østby, Sebastian Brambo, Tobias M. Vinje, Eirik M. Artificial Intelligence Machine Learning Green Tsetlin (GT) is a Tsetlin Machine (TM) framework developed to solve real-world problems using TMs. Several frameworks already exist that provide access to TM implementations. However, these either lack features or have a research-first focus. GT is an easy-to-use framework that aims to lower the complexity and provide a production-ready TM implementation that is great for experienced practitioners and beginners. To this end, GT establishes a clear separation between training and inference. A C++ backend with a Python interface provides competitive training and inference performance, with the option of running in pure Python. It also integrates support for critical components such as exporting trained models, hyper-parameter search, and cross-validation out-of-the-box. |
| title | Green Tsetlin Redefining Efficiency in Tsetlin Machine Frameworks |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.04212 |