Second Order Moment

Second-order moments, representing the variance of gradients in optimization algorithms, are crucial for efficient and robust training of deep neural networks. Current research focuses on improving existing algorithms like Adam by refining second-moment estimations, incorporating conjugate gradient methods, and developing unified frameworks encompassing various Adam-type optimizers. These advancements aim to address challenges such as gradient vanishing/explosion, dataset sparsity, and noise in data, ultimately leading to faster convergence and improved performance in deep learning applications. The improved understanding and control of second-order moments are vital for enhancing the reliability and efficiency of training complex models.