Exponential Moving Average

Exponential moving averages (EMAs) are a statistical technique used to smooth out noisy data by weighting recent observations more heavily, providing stable estimates of dynamic quantities. Current research focuses on optimizing EMA implementations within various machine learning contexts, including improving model training stability (e.g., through damped harmonic motion analogies and adaptive momentum optimizers), enhancing generalization performance (e.g., via "Switch EMA"), and scaling EMAs effectively across different batch sizes and model architectures. These advancements have significant implications for improving the efficiency and robustness of deep learning models across diverse applications, from image classification and language modeling to wireless channel prediction and federated learning.