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• W3146640504 abstract "Recent studies revealed complex convergence dynamics in gradient-based methods, which has been little understood so far. Changing the step size to balance between high convergence rate and small generalization error may not be sufficient: maximizing the test accuracy usually requires a larger learning rate than minimizing the training loss. To explore the dynamic bounds of convergence rate, this study introduces textit{differential capability} into an optimization process, which measures whether the test accuracy increases as fast as a model approaches the decision boundary in a classification problem. The convergence analysis showed that: 1) a higher convergence rate leads to slower capability growth; 2) a lower convergence rate results in faster capability growth and decay; 3) regulating a convergence rate in either direction reduces differential capability." @default.
• W3146640504 created "2021-04-13" @default.
• W3146640504 creator A5090465649 @default.
• W3146640504 date "2021-04-02" @default.
• W3146640504 modified "2023-09-26" @default.
• W3146640504 title "Neurons learn slower than they think." @default.
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• W3146640504 hasPublicationYear "2021" @default.
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