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Explore the integration of gradient descent in machine learning through recognition theory.

March 14, 2026Open Access

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

Key Points

Explore the integration of gradient descent in machine learning through recognition theory.
Part 17 of a comprehensive 22-paper series
Focus on fundamental concepts in recognition theory
Application of mathematical frameworks to machine learning.
Demonstrates the relevance of recognition theory in machine learning.
Provides new insights into unsolved problems in physics and mathematics.

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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