# Rapidly Adapting Moment Estimation

## TL;DR

This paper introduces RAME, a new adaptive gradient method that uses the most recent first moment of gradients to compute learning rates, aiming to improve convergence speed and generalization in deep neural network training.

## Contribution

RAME is a novel adaptive gradient method that leverages the latest first moment of gradients, differing from existing methods that focus on the second moment.

## Key findings

- RAME shows faster convergence compared to Adam and RMSprop.
- RAME achieves comparable or better generalization performance.
- Theoretical convergence of deterministic RAME is established.

## Abstract

Adaptive gradient methods such as Adam have been shown to be very effective for training deep neural networks (DNNs) by tracking the second moment of gradients to compute the individual learning rates. Differently from existing methods, we make use of the most recent first moment of gradients to compute the individual learning rates per iteration. The motivation behind it is that the dynamic variation of the first moment of gradients may provide useful information to obtain the learning rates. We refer to the new method as the rapidly adapting moment estimation (RAME). The theoretical convergence of deterministic RAME is studied by using an analysis similar to the one used in [1] for Adam. Experimental results for training a number of DNNs show promising performance of RAME w.r.t. the convergence speed and generalization performance compared to the stochastic heavy-ball (SHB) method, Adam, and RMSprop.