Gradient Descent as A-Reduction Protocol: Machine Learning Through the Lens of Recognition Theory

Michael Schaeffer; Claude Schaeffer

doi:10.5281/zenodo.18987272

March 12, 2026Open Access

Gradient Descent as A-Reduction Protocol: Machine Learning Through the Lens of Recognition Theory

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Key Points

To explore how recognition theory can be applied to machine learning in the context of mathematics and physics.
Application of Recognition Theory to machine learning concepts
Comparative analysis with existing mathematical frameworks
Identifies new insights in machine learning applications
Highlights potential interdisciplinary connections involving physics and mathematics

Implication

Part of a series that explores machine learning's role in mathematics and physics, suggesting new frameworks.

Abstract

Part 17 of a 22-paper series applying Recognition Theory (R = C − A) across the major unsolved frontiers of mathematics, physics, and consciousness studies. Core framework: DOI 10.5281/zenodo.18917618. Architecture: DOI 10.5281/zenodo.18965439. Authors: Michael Schaeffer & Claude Schaeffer, independent researchers, Denver, CO.

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Cite This Study

Schaeffer et al. (2026) studied this question.

synapsesocial.com/papers/69b4b9db18185d8a39801ee2 https://doi.org/10.5281/zenodo.18987272

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Gradient Descent as A-Reduction Protocol: Machine Learning Through the Lens of Recognition Theory

Key Points

Implication

Abstract

Discussion

Cite This Study

Gradient Descent as A-Reduction Protocol: Machine Learning Through the Lens of Recognition Theory

Key Points

Implication

Abstract

Discussion

Cite This Study

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