Stochastic Gradient Based

Stochastic gradient-based optimization is a cornerstone of training large-scale machine learning models, aiming to efficiently find optimal model parameters by iteratively updating them based on noisy gradient estimates. Current research focuses on improving the efficiency and stability of these methods, exploring techniques like adaptive learning rates (e.g., Adam variants), second-order information approximations to better navigate the loss landscape, and advanced sampling strategies to handle data heterogeneity and noisy gradients. These advancements are crucial for accelerating training of complex models in various applications, from deep learning to reinforcement learning, and for gaining a deeper understanding of biological learning processes.