Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments
Authors: Boris Ginsburg, Patrice Castonguay, Oleksii Hrinchuk, Oleksii Kuchaiev, Vitaly Lavrukhin, Ryan Leary, Jason Li, Huyen Nguyen, Yang Zhang, Jonathan M. Cohen
Keywords: deep learning, optimization, SGD, Adam, NovoGrad, large batch training
TL;DR: NovoGrad -  an adaptive SGD method with layer-wise gradient normalization and decoupled weight decay. 
Abstract: We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay. In our experiments on neural networks for image classification, speech recognition, machine translation, and language modeling, it performs on par or better than well tuned SGD with momentum and Adam/AdamW. 
Additionally, NovoGrad (1) is robust to the choice of learning rate and weight initialization, (2) works well in a large batch setting, and (3) has two times smaller memory footprint than Adam.