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.
The Tsetlin Machine–A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
abstract
Although simple individually, artificial neurons provide state-of-the-art performance when interconnected in deep networks. Arguably, the Tsetlin Automaton is an even simpler and more versatile learning mechanism, capable of solving the multi-armed bandit problem. Merely by means of a single integer as memory, it learns the optimal action in stochastic environments through increment and decrement operations. In this paper, we introduce the Tsetlin Machine, which solves complex pattern recognition problems with propositional formulas, composed by a collective of Tsetlin Automata. To eliminate the longstanding problem of vanishing signal-to-noise ratio, the Tsetlin Machine orchestrates the automata using a novel game. Further, both inputs, patterns, and outputs are expressed as bits, while recognition and learning rely on bit manipulation, simplifying computation. Our theoretical analysis establishes that the Nash equilibria of the game align with the propositional formulas that provide optimal pattern recognition accuracy. This translates to learning without local optima, only global ones. In five benchmarks, the Tsetlin Machine provides competitive accuracy compared with SVMs, Decision Trees, Random Forests, Naive Bayes Classifier, Logistic Regression, and Neural Networks. We further demonstrate how the propositional formulas facilitate interpretation. In conclusion, we believe the combination of high accuracy, interpretability, and computational simplicity makes the Tsetlin Machine a promising tool for a wide range of domains.
representative citing papers
A Graph Tsetlin Machine with hypervectorized macro multigraphs and message-passing clauses anticipates four USD/JPY regimes, reaching 70.7% overall OOS accuracy and beating reduced-graph and CoTM baselines on stagnant and choppy classes.
A stochastic Tsetlin machine constructed from thermodynamic logic gates achieves classification accuracy statistically comparable to the conventional digital version.
A domain-specific reduced RISC-V core for Tsetlin Machine inference delivers up to 98% faster execution and 29.7x lower energy than baseline RV32IM while matching or exceeding BNN accuracy on tested datasets.
Tsetlin Machine IDS detects IoMT cyberattacks at 99.5% binary and 90.7% multi-class accuracy on CICIoMT-2024, outperforming traditional ML with added interpretability via class-wise votes and activation heatmaps.
TRUST searches for minimal input changes that achieve a user-defined confidence target in PTM models, claiming perfect robustness and low cost on benchmarks versus standard boundary-crossing methods.
Tsetlin Machine IDS reaches 97.83% macro F1 on MedSec-25 dataset for IoMT attack detection, with on-device Raspberry Pi deployment and feature-level interpretability.
FastOmniTMAE parallelizes clause learning in Tsetlin Machine autoencoders to achieve up to 5x faster training with comparable embedding quality and low-footprint FPGA deployment.
Promise Theory is augmented with Bayesian methods and information optimization to model intentionality, inference, and swarm formation in autonomous agents.
citing papers explorer
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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.
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Macroeconomic Message Passing for Anticipating Foreign Exchange Regime Changes: A Deep Logical Learning Approach using Graph Tsetlin Machines
A Graph Tsetlin Machine with hypervectorized macro multigraphs and message-passing clauses anticipates four USD/JPY regimes, reaching 70.7% overall OOS accuracy and beating reduced-graph and CoTM baselines on stagnant and choppy classes.
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Interpretable rule-based learning in an autonomous thermodynamic network
A stochastic Tsetlin machine constructed from thermodynamic logic gates achieves classification accuracy statistically comparable to the conventional digital version.
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Low-Energy Reduced RISC-V Instruction Subset Processor for Tsetlin Machine Inference at the Edge
A domain-specific reduced RISC-V core for Tsetlin Machine inference delivers up to 98% faster execution and 29.7x lower energy than baseline RV32IM while matching or exceeding BNN accuracy on tested datasets.
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A Tsetlin Machine-driven Intrusion Detection System for Next-Generation IoMT Security
Tsetlin Machine IDS detects IoMT cyberattacks at 99.5% binary and 90.7% multi-class accuracy on CICIoMT-2024, outperforming traditional ML with added interpretability via class-wise votes and activation heatmaps.
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Target-confidence Recourse Using tSeTlin machines: TRUST
TRUST searches for minimal input changes that achieve a user-defined confidence target in PTM models, claiming perfect robustness and low cost on benchmarks versus standard boundary-crossing methods.
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On-Device Interpretable Tsetlin Machine-Based Intrusion Detection for Secure IoMT
Tsetlin Machine IDS reaches 97.83% macro F1 on MedSec-25 dataset for IoMT attack detection, with on-device Raspberry Pi deployment and feature-level interpretability.
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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.
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Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents
Promise Theory is augmented with Bayesian methods and information optimization to model intentionality, inference, and swarm formation in autonomous agents.