Meta-LR-Schedule-Net: Learned LR Schedules that Scale and Generalize

29 Jul 2020 Jun Shu Yanwen Zhu Qian Zhao Deyu Meng Zongben Xu

The learning rate (LR) is one of the most important hyper-parameters in stochastic gradient descent (SGD) for deep neural networks (DNNs) training and generalization. However, current hand-designed LR schedules need to manually pre-specify schedule as well as its extra hyper-parameters, which limits its ability to adapt non-convex optimization problems due to the significant variation of training dynamic... (read more)

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