Adam Algorithm

The Adam algorithm is a popular optimization method used to train machine learning models, particularly deep neural networks, aiming for efficient and stable convergence. Current research focuses on understanding its theoretical properties, including convergence guarantees under less restrictive assumptions and addressing observed divergence issues, particularly in the context of federated learning and training on manifolds. This work is significant because improved understanding and modifications to Adam can lead to faster and more reliable training of complex models across various applications, from computer vision and natural language processing to medical diagnostics. Furthermore, research explores efficient adaptations of Adam for distributed and resource-constrained environments.