Adaptive Learning Rate via Covariance Matrix Based Preconditioning
for Deep Neural Networks
Abstract
Adaptive learning rate algorithms such as RMSProp are widely used for training deep neural networks. RMSProp offers efficient training since it
uses first order gradients to approximate Hessianbased preconditioning. However, since the first order gradients include noise caused by stochastic optimization, the approximation may be inaccurate.
In this paper, we propose a novel adaptive learning rate algorithm called SDProp. Its key idea is
effective handling of the noise by preconditioning
based on covariance matrix. For various neural networks, our approach is more efficient and effective
than RMSProp and its variant