Machine learning generalization linked to superconducting transition physics

Researchers have utilized dynamical mean field theory to explain the phenomenon of "double descent" in machine learning, where generalization improves even when model capacity exceeds data points. This behavior is identified as a phase transition in the training process, characterized by a breakdown of the fluctuation-dissipation theorem due to broken ergodicity. The study's findings suggest a connection between the response function of this transition and the London model of superconducting transitions, with wave function rigidity correlating to a neural network's generalization ability. AI

IMPACT Provides a theoretical framework for understanding and potentially improving generalization in large neural networks.

RANK_REASON Academic paper detailing a novel theoretical framework for machine learning generalization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• dynamical mean field theory
• fluctuation-dissipation theorem
• London model
• machine learning

• paper
• other

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