  Optimizers like Adam and AdaGrad have been very successful in training
large-scale neural networks. Yet, the performance of these methods is heavily
dependent on a carefully tuned learning rate schedule. We show that in many
large-scale applications, augmenting a given optimizer with an adaptive tuning
method of the step-size greatly improves the performance. More precisely, we
maintain a global step-size scale for the update as well as a gain factor for
each coordinate. We adjust the global scale based on the alignment of the
average gradient and the current gradient vectors. A similar approach is used
for updating the local gain factors. This type of step-size scale tuning has
been done before with gradient descent updates. In this paper, we update the
step-size scale and the gain variables with exponentiated gradient updates
instead. Experimentally, we show that our approach can achieve compelling
accuracy on standard models without using any specially tuned learning rate
schedule. We also show the effectiveness of our approach for quickly adapting
to distribution shifts in the data during training.
