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math.OC2022★ 3 cited
Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks
Davide Gallon, Arnulf Jentzen, Felix Lindner
In this article we investigate blow up phenomena for gradient descent optimization methods in the training of artificial neural networks (ANNs). Our theoretical analysis is focused…
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…