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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!
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