Adaptive Momentum

Adaptive momentum methods, which adjust optimization steps based on past gradients, are central to training many machine learning models, particularly deep neural networks. Current research focuses on improving the stability and convergence properties of these methods, addressing issues like numerical instability in long training runs and exploring their application in diverse areas such as generative models and neural ordinary differential equations (NODEs). This work aims to enhance the efficiency and accuracy of training, leading to improved performance in various applications, from image generation to scientific simulations like neutrino momentum reconstruction. The development of robust and theoretically well-understood adaptive momentum algorithms is crucial for advancing the capabilities of machine learning across numerous fields.