1 citations · 1 across the 2 of their papers we have counts for
2 papers
math.OC2025
Asymptotic stability properties and a priori bounds for Adam and other gradient descent optimization methods
Steffen Dereich, Robin Graeber, Arnulf Jentzen +1
Gradient descent (GD) based optimization methods are these days the standard tools to train deep neural networks in artificial intelligence systems. In optimization procedures in d…
cs.LG2024★ 1 cited
Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates
Steffen Dereich, Robin Graeber, Arnulf Jentzen
Deep learning algorithms - typically consisting of a class of deep neural networks trained by a stochastic gradient descent (SGD) optimization method - are nowadays the key ingredi…