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arXiv Machine Learning By Volkan Da\u{g}l{\i}, Zerrin Da\u{g}l{\i}, Da\u{g}han Da\u{g}l{\i}

Orbital Error Dynamics: Self-Organized Criticality, Ephemeral Parameter Resonance, and Non-Linear Biological Ontologies in Zero-Storage Neural Synthesis

Read the original on arXiv Machine Learning →

The paper introduces Orbital Error Dynamics (OED), an analytical framework that reinterprets neural network weights as transient topological resonances rather than static matrices, derived from a complex quadratic polynomial map. It proposes the Bent Sine Wave Hypothesis to explain non‑equilibrium living systems, defines Observer Horizon Geometry in parameter space, and presents a heavy‑tailed Biomimetic Perturbed Jump Operator inspired by biological processes. Empirical tests on the Two‑Moons manifold show that procedural parameterization from a 24‑byte seed yields competitive accuracy compared to a conventional gradient baseline, while also aligning conceptually with an analog optical co‑processor.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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