13 citations · 30 across the 5 of their papers we have counts for
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math.OC2023★ 11 cited
Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case
Meixuan He, Yuqing Liang, Jinlan Liu +1
Adam is a commonly used stochastic optimization algorithm in machine learning. However, its convergence is still not fully understood, especially in the non-convex setting. This pa…
cs.LG2023★ 4 cited
UAdam: Unified Adam-Type Algorithmic Framework for Non-Convex Stochastic Optimization
Yiming Jiang, Jinlan Liu, Dongpo Xu +1
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. To this…