Solving a Machine Learning Regression Problem Based on the Theory of Random Functions — mentioned by Yuriy_Bakhvalov

Wed, 07 Jan 2026 14:01:00 GMT

Hi HN, I’m the author of this project. I am an independent researcher working on deriving deep learning architectures from first principles (Random Function Theory) rather than empirical heuristics. The core idea: Instead of choosing kernels (like RBF) arbitrarily, I derive the optimal kernel analytically from symmetry postulates (translation, rotation, scale invariance). This leads uniquely to the Polyharmonic Spline. Key features of this architecture: 1. No Gradient Descent: Training uses a synchronized global linear solver (Lagrange multipliers) to update function values. 2. Deep & Stable…

Paper and discussion history. A mention is not an endorsement.

Solving a Machine Learning Regression Problem Based on the Theory of Random Functions · HackerPapers discussions

Solving a Machine Learning Regression Problem Based on the Theory of Random Functions — mentioned by Yuriy_Bakhvalov