Order Gradient

Order gradient methods, primarily focusing on second-order gradients (and sometimes higher-order), aim to improve the efficiency and stability of optimization algorithms across diverse applications. Current research emphasizes the development and analysis of these methods within machine learning, particularly for training deep neural networks and generative models, with a focus on addressing computational challenges and improving convergence rates. The improved optimization offered by these techniques has significant implications for various fields, including scientific computing, design space exploration, and the development of more robust and efficient machine learning models.