most citedConvergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions

2 citations · 2 across the 1 of their papers we have counts for

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cs.LG2021★ 2 cited

Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions

Martin Hutzenthaler, Arnulf Jentzen, Katharina Pohl +2

In many numerical simulations stochastic gradient descent (SGD) type optimization methods perform very effectively in the training of deep neural networks (DNNs) but till this day…