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Extrapolation for Large-batch Training in Deep Learning
[article]
2020
arXiv
pre-print
Deep learning networks are typically trained by Stochastic Gradient Descent (SGD) methods that iteratively improve the model parameters by estimating a gradient on a very small fraction of the training data. A major roadblock faced when increasing the batch size to a substantial fraction of the training data for improving training time is the persistent degradation in performance (generalization gap). To address this issue, recent work propose to add small perturbations to the model parameters
arXiv:2006.05720v1
fatcat:yz2d4stqrjgtbokc5eyjm6fati