Momentum Based Method

Momentum-based methods are optimization techniques that accelerate the convergence of machine learning models by incorporating information from previous updates. Current research focuses on adapting these methods for various applications, including large language model fine-tuning (using zeroth-order optimization and novel loss functions), federated learning (addressing challenges posed by asynchronous updates), and domain generalization (balancing training difficulty and model capacity). These advancements improve model performance, efficiency, and robustness across diverse tasks, impacting fields ranging from natural language processing to computer vision and beyond.