Adam V2

Adam, a popular optimization algorithm for training deep learning models, is undergoing refinement and extension to improve efficiency and performance. Current research focuses on variants like Adam-mini (reducing memory footprint), AdaMoE and AdaMoLE (adapting to mixture-of-experts architectures for larger models), and methods incorporating Adam's principles into other frameworks (e.g., AdamNODEs for neural ODEs). These advancements aim to enhance training speed, reduce computational costs, and improve model accuracy across various applications, including large language models and medical image analysis.