Algebraic Dynamical Systems in Machine Learning

• URL: http://arxiv.org/abs/2311.03118v1
• Date: Mon, 6 Nov 2023 14:10:40 GMT
• Title: Algebraic Dynamical Systems in Machine Learning
• Authors: Iolo Jones, Jerry Swan, and Jeffrey Giansiracusa
• Abstract summary: We show that a function applied to the output of an iterated rewriting system defines a formal class of models. We also show that these algebraic models are a natural language for describing the compositionality of dynamic models. These models provide a template for the generalisation of the above dynamic models to learning problems on structured or non-numerical data.
• Score: 0.1843404256219181
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce an algebraic analogue of dynamical systems, based on term rewriting. We show that a recursive function applied to the output of an iterated rewriting system defines a formal class of models into which all the main architectures for dynamic machine learning models (including recurrent neural networks, graph neural networks, and diffusion models) can be embedded. Considered in category theory, we also show that these algebraic models are a natural language for describing the compositionality of dynamic models. Furthermore, we propose that these models provide a template for the generalisation of the above dynamic models to learning problems on structured or non-numerical data, including 'hybrid symbolic-numeric' models.

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