Adrian Riekert
Orcid: 0000-0002-1458-3388
According to our database1,
Adrian Riekert
authored at least 12 papers
between 2020 and 2024.
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Bibliography
2024
Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks.
CoRR, 2024
2023
Deep neural network approximation of composite functions without the curse of dimensionality.
CoRR, 2023
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations.
CoRR, 2023
2022
A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions.
J. Mach. Learn. Res., 2022
A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions.
J. Complex., 2022
Normalized gradient flow optimization in the training of ReLU artificial neural networks.
CoRR, 2022
2021
On the existence of global minima and convergence analyses for gradient descent methods in the training of deep neural networks.
CoRR, 2021
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions.
CoRR, 2021
Existence, uniqueness, and convergence rates for gradient flows in the training of artificial neural networks with ReLU activation.
CoRR, 2021
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation.
CoRR, 2021
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions.
CoRR, 2021
2020
Strong overall error analysis for the training of artificial neural networks via random initializations.
CoRR, 2020