IF-009
Adaptive Learning Rate via Covariance Matrix Based Preconditioning for Deep Neural Networks

◎井田安俊・藤原靖宏・岩村相哲（NTT）

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.