most citedOn the existence of infinitely many realization functions of non-global local minima in the training of artificial neural networks with ReLU activation

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math.OC2025

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems

Steffen Dereich, Arnulf Jentzen, Adrian Riekert

Deep learning methods - usually consisting of a class of deep neural networks (DNNs) trained by a stochastic gradient descent (SGD) optimization method - are nowadays omnipresent i…

math.OC2022★ 1 cited

On the existence of infinitely many realization functions of non-global local minima in the training of artificial neural networks with ReLU activation

Shokhrukh Ibragimov, Arnulf Jentzen, Timo Kröger +1

Gradient descent (GD) type optimization schemes are the standard instruments to train fully connected feedforward artificial neural networks (ANNs) with rectified linear unit (ReLU…