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Found 5 papers, 2 papers with code
Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case
Adam is a commonly used stochastic optimization algorithm in machine learning.
UAdam: Unified Adam-Type Algorithmic Framework for Non-Convex Stochastic Optimization
Adam-type algorithms have become a preferred choice for optimisation in the deep learning setting, however, despite success, their convergence is still not well understood.
Last-iterate convergence analysis of stochastic momentum methods for neural networks
The stochastic momentum method is a commonly used acceleration technique for solving large-scale stochastic optimization problems in artificial neural networks.
Scaling transition from momentum stochastic gradient descent to plain stochastic gradient descent
The momentum stochastic gradient descent uses the accumulated gradient as the updated direction of the current parameters, which has a faster training speed.
A decreasing scaling transition scheme from Adam to SGD
Adaptive gradient algorithm (AdaGrad) and its variants, such as RMSProp, Adam, AMSGrad, etc, have been widely used in deep learning.
