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 Hessian-based 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.
Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks
