The paper defines computational effort as the number of gradient descent steps to reach target accuracy with high probability, shows large learning rates minimize this effort across models, and identifies phase transitions in optimal training strategies.
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Gradient-Descent Steps to Success over Mean Accuracy: A Paradigm Shift for ML
The paper defines computational effort as the number of gradient descent steps to reach target accuracy with high probability, shows large learning rates minimize this effort across models, and identifies phase transitions in optimal training strategies.