Faster Convergence

Faster convergence in machine learning aims to reduce the time and computational resources required to train models to a desired level of accuracy. Current research focuses on improving optimization algorithms (e.g., variants of Adam, SGD, and ADMM), developing novel sampling techniques for efficient data utilization (e.g., in federated learning and Physics-Informed Neural Networks), and leveraging architectural innovations (e.g., in Transformer networks and graph-based models) to accelerate training. These advancements are significant because faster convergence translates to reduced energy consumption, faster model deployment, and improved efficiency in various applications, from edge computing to large-scale deep learning.