Verifying Properties of Tsetlin Machines

Tsetlin Machines (TsMs) are a promising and interpretable machine learning method which can be applied for various classification tasks. We present an exact encoding of TsMs into propositional logic and formally verify properties of TsMs using a SAT solver. In particular, we introduce in this work a...

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Bibliographic Details
Published inarXiv.org
Main Authors Przybysz, Emilia, Bhattarai, Bimal, Persia, Cosimo, Ozaki, Ana, Ole-Christoffer Granmo, Sharma, Jivitesh
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 02.07.2023
Subjects
Equivalence
Image classification
Machine learning
Neural networks
Robustness
Similarity
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Summary:Tsetlin Machines (TsMs) are a promising and interpretable machine learning method which can be applied for various classification tasks. We present an exact encoding of TsMs into propositional logic and formally verify properties of TsMs using a SAT solver. In particular, we introduce in this work a notion of similarity of machine learning models and apply our notion to check for similarity of TsMs. We also consider notions of robustness and equivalence from the literature and adapt them for TsMs. Then, we show the correctness of our encoding and provide results for the properties: adversarial robustness, equivalence, and similarity of TsMs. In our experiments, we employ the MNIST and IMDB datasets for (respectively) image and sentiment classification. We discuss the results for verifying robustness obtained with TsMs with those in the literature obtained with Binarized Neural Networks on MNIST.
ISSN:2331-8422