Researchers Develop Statistical-Mechanical Description of Neural Network Learning in Function Space

A new arXiv paper proposes analyzing how deep neural networks learn by studying them at the level of functions rather than individual parameters. The authors borrow tools from statistical mechanics, treating parameter configurations as microscopic states to explain why networks with billions of weights show consistent, predictable learning patterns. The approach aims to provide a theoretical framework for understanding training dynamics in very large models.