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[R] Demos and Paper – The Weighted Tsetlin Machine: Compressed Representations with Clause Weighting

[R] Demos and Paper - The Weighted Tsetlin Machine: Compressed Representations with Clause Weighting

The Weighted Tsetlin Machine

The real-valued weighting of clauses allows one clause to replace multiple and supports fine-tuning the impact of each clause. The Weighted Tsetlin Machine (WTM) achieves the same accuracy as the vanilla Tsetlin Machine (TM) on MNIST, IMDb, and Connect-4, requiring only 1/4, 1/3, and 1/50 of the clauses, respectively. With the same number of clauses, the WTM outperforms the TM, obtaining peak test accuracies of respectively 98. 58%, 90.15%, and 87.49%. The demos also include FashionMNIST with weighted convolution: https://github.com/cair/pyTsetlinMachine, https://arxiv.org/abs/1911.12607

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.