  Second-order optimization has been developed to accelerate the training of
deep neural networks and it is being applied to increasingly larger-scale
models. In this study, towards training on further larger scales, we identify a
specific parameterization for second-order optimization that promotes feature
learning in a stable manner even if the network width increases significantly.
Inspired by a maximal update parameterization, we consider a one-step update of
the gradient and reveal the appropriate scales of hyperparameters including
random initialization, learning rates, and damping terms. Our approach covers
two major second-order optimization algorithms, K-FAC and Shampoo, and we
demonstrate that our parameterization achieves higher generalization
performance in feature learning. In particular, it enables us to transfer the
hyperparameters across models with different widths.
