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The Break-Even Point on Optimization Trajectories of Deep Neural Networks
[article]
2020
arXiv
pre-print
The early phase of training of deep neural networks is critical for their final performance. In this work, we study how the hyperparameters of stochastic gradient descent (SGD) used in the early phase of training affect the rest of the optimization trajectory. We argue for the existence of the "break-even" point on this trajectory, beyond which the curvature of the loss surface and noise in the gradient are implicitly regularized by SGD. In particular, we demonstrate on multiple classification
arXiv:2002.09572v1
fatcat:qyrskuopzrex7f2zz5mrq6w764