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Gradient descent provably escapes saddle points in the training of shallow ReLU networks., , and . CoRR, (2022)A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions., , , and . J. Complex., (2022)The curse of dimensionality and gradient-based training of neural networks: shrinking the gap between theory and applications.. ETH Zurich, Zürich, Switzerland, (2023)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/602160).Efficient approximation of high-dimensional functions with deep neural networks., , and . CoRR, (2019)Non-convergence of stochastic gradient descent in the training of deep neural networks., , and . CoRR, (2020)Landscape Analysis for Shallow Neural Networks: Complete Classification of Critical Points for Affine Target Functions., , and . J. Nonlinear Sci., 32 (5): 64 (2022)Efficient Approximation of High-Dimensional Functions With Neural Networks., , and . IEEE Trans. Neural Networks Learn. Syst., 33 (7): 3079-3093 (2022)Landscape analysis for shallow ReLU neural networks: complete classification of critical points for affine target functions., , and . CoRR, (2021)Efficient Sobolev approximation of linear parabolic PDEs in high dimensions., and . CoRR, (2023)Non-convergence of stochastic gradient descent in the training of deep neural networks., , and . J. Complex., (2021)