Adaptive Gradient

Adaptive gradient methods, which adjust learning rates for individual parameters during training, aim to improve the efficiency and effectiveness of deep learning optimization compared to standard methods like stochastic gradient descent (SGD). Current research focuses on understanding their theoretical convergence properties, particularly in large-batch settings and non-convex optimization problems, with algorithms like Adam and AdaGrad being central to these investigations. This research is crucial for advancing deep learning, as improved optimization techniques directly impact the training speed, generalization performance, and scalability of large-scale models across various applications.