Zenodo (CERN)
open access
CC BY 4.0
The Davis Duality of Approximation and Obstruction: Why Machine Learning Works, Why the Vacuum Has Mass, and the Universal Law of Flat Failure
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learning#1076438
- A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning#996846
- Sloppy Models, Renormalization Group Realism, and the Success of Science#102982
- Machine Learning-Driven First-Principles Modeling of Far-Infrared Materials. (Modélisations ab initio de matériaux pour l'infrarouge lointain s'appuyant sur le Machine Learning).#489477
- Phase Transitions in Artificial General Intelligence: Scaling Laws, Predictability Limits, and Singularity#219376
Record · ID 181370
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive