AIBullisharXiv – CS AI · Mar 167/10
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From Garbage to Gold: A Data-Architectural Theory of Predictive Robustness
Researchers propose a new theoretical framework explaining why modern machine learning models achieve robust performance using high-dimensional, error-prone data, challenging the traditional 'Garbage In, Garbage Out' principle. The study introduces concepts like 'Informative Collinearity' and 'Proactive Data-Centric AI' to show how data architecture and model capacity work together to overcome noise and structural uncertainty.