Why Parameter Count Fails to Measure True AI Model Complexity
Researchers explore effective dimension as a superior metric for understanding machine learning redundancy, generalization, and trainability. Unlike raw parameter counts or standard loss and accuracy metrics, effective dimension offers deeper insights into how models actually process information, helping developers build more reliable and efficient artificial intelligence systems.
Source: Towards Data Science