A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit

ICLR 2019 Seong Jin ChoSunghun KangChang D. Yoo

Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search. This paper considers a resizable mini-batch gradient descent (RMGD) algorithm based on a multi-armed bandit for achieving best performance in grid search by selecting an appropriate batch size at each epoch with a probability defined as a function of its previous success/failure... (read more)

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