Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks
The episode discusses the paper "Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks." Hosts explore how this research provides a mathematical roadmap for predicting noise-induced drift in AI systems. They detail key findings, including three distinct learning behaviors and the emergence of a critical correction term, to improve system stability and reliability.