Symmetry-Enriched Learning: A Category-Theoretic Framework for Robust Machine Learning Models

• URL: http://arxiv.org/abs/2409.12100v1
• Date: Wed, 18 Sep 2024 16:20:57 GMT
• Title: Symmetry-Enriched Learning: A Category-Theoretic Framework for Robust Machine Learning Models
• Authors: Ronald Katende,
• Abstract summary: We introduce new mathematical constructs, including hyper-symmetry categories and functorial representations, to model complex transformations within machine learning algorithms. Our contributions include the design of symmetry-enriched learning models, the development of advanced optimization techniques leveraging categorical symmetries, and the theoretical analysis of their implications for model robustness, generalization, and convergence.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This manuscript presents a novel framework that integrates higher-order symmetries and category theory into machine learning. We introduce new mathematical constructs, including hyper-symmetry categories and functorial representations, to model complex transformations within learning algorithms. Our contributions include the design of symmetry-enriched learning models, the development of advanced optimization techniques leveraging categorical symmetries, and the theoretical analysis of their implications for model robustness, generalization, and convergence. Through rigorous proofs and practical applications, we demonstrate that incorporating higher-dimensional categorical structures enhances both the theoretical foundations and practical capabilities of modern machine learning algorithms, opening new directions for research and innovation.

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