Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks

arXiv:2607.04233v1 Announce Type: cross Abstract: Gradient based optimization methods are nowadays the methods of choice for training deep neural networks (DNNs) in artificial intelligence (AI) systems. In practically relevant DNN training problems, one does usually not apply the standard gradient descent (GD) optimization method but instead one employs suitable sophisticated GD optimization methods, which incorporate adaptivity and/or acceleration techniques, such as the famous Adam optimizer. It is a key contribution of this work to provide a general unified convergence analysis for GD optim

Source: arXiv cs.LG — read the full report at the original publisher.

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