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This paper analyzes the Edge of Stability (EoS) in Gradient Descent (GD) for deep learning, revealing that traditional assumptions about learning rates and sharpness do not hold in practice. It introduces a mathematical framework to understand how GD can decrease loss despite oscillations and increasing sharpness, challenging existing optimization theories. The findings suggest a need to rethink implicit regularization and optimization dynamics in deep learning, particularly in relation to the behavior of G

Author

Pavan Bodke

Language

EN