Linear Range in Gradient Descent
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Publication:6318591
arXiv1905.04561MaRDI QIDQ6318591
Author name not available (Why is that?)
Publication date: 11 May 2019
Abstract: This paper defines linear range as the range of parameter perturbations which lead to approximately linear perturbations in the states of a network. We compute linear range from the difference between actual perturbations in states and the tangent solution. Linear range is a new criterion for estimating the effectivenss of gradients and thus having many possible applications. In particular, we propose that the optimal learning rate at the initial stages of training is such that parameter changes on all minibatches are within linear range. We demonstrate our algorithm on two shallow neural networks and a ResNet.
Has companion code repository: https://github.com/niangxiu/linGrad
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