Green Tsetlin Redefining Efficiency in Tsetlin Machine Frameworks
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 5 references that are in the map. First posted on arXiv 7 May 2024.
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References · 5
- 2018The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logiccited by 72
- 2021Coalesced Multi-Output Tsetlin Machines with Clause Sharingcited by 22
- 2023Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Sizecited by 14
- 2020Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Datacited by 6
- 2024The Sparse Tsetlin Machine: Sparse Representation with Active Literalscited by 1
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