Adam Family Method

Adam-family methods are adaptive learning rate optimization algorithms widely used in training deep neural networks, aiming to improve efficiency and generalization performance compared to traditional methods like stochastic gradient descent. Current research focuses on establishing rigorous convergence guarantees for these methods, particularly when applied to non-smooth optimization problems and incorporating techniques like decoupled weight decay. This work is significant because it provides a stronger theoretical foundation for understanding and improving the performance of Adam-family optimizers, leading to more robust and reliable training of complex machine learning models.