GraphTM uses message passing on graphs to build nested deep clauses, achieving 3.86% higher accuracy than convolutional TM on CIFAR-10 and competitive results on action tracking, recommendations, and genome sequences.
Coalesced multi-output tsetlin machines with clause sharing
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2verdicts
UNVERDICTED 2representative citing papers
FastOmniTMAE parallelizes clause learning in Tsetlin Machine autoencoders to achieve up to 5x faster training with comparable embedding quality and low-footprint FPGA deployment.
citing papers explorer
-
The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs
GraphTM uses message passing on graphs to build nested deep clauses, achieving 3.86% higher accuracy than convolutional TM on CIFAR-10 and competitive results on action tracking, recommendations, and genome sequences.
-
FastOmniTMAE: Parallel Clause Learning for Scalable and Hardware-Efficient Tsetlin Embeddings
FastOmniTMAE parallelizes clause learning in Tsetlin Machine autoencoders to achieve up to 5x faster training with comparable embedding quality and low-footprint FPGA deployment.