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The original was posted on /r/machinelearning by /u/olegranmo on 2024-10-19 09:48:27+00:00.

Hi all! I just completed the first deep Tsetlin Machine - a Graph Tsetlin Machine that can learn and reason multimodally across graphs. After introducing the Tsetlin machine in 2018, I expected to figure out how to make a deep one quickly. Took me six years! Sharing the project:

Features:

• Processes directed and labeled multigraphs
• Vector symbolic node properties and edge types
• Nested (deep) clauses
• Arbitrarily sized inputs
• Incorporates Vanilla, Multiclass, Convolutional, and Coalesced Tsetlin Machines
• Rewritten faster CUDA kernels

Roadmap:

• Rewrite graphs.py in C or numba for much faster construction of graphs
• Add autoencoder
• Add regression
• Add multi-output
• Graph initialization with adjacency matrix

Happy to receive feedback on the next steps of development!