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

13 December 2021

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

6 / 6 papers shown

Title
Non-convergence to the optimal risk for Adam and stochastic gradient descent optimization in the training of deep neural networks Thang Do Arnulf Jentzen Adrian Riekert 53 1 0 03 Mar 2025
Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks Arnulf Jentzen Adrian Riekert 25 4 0 07 Feb 2024
Global Convergence of SGD On Two Layer Neural Nets Pulkit Gopalani Anirbit Mukherjee 18 5 0 20 Oct 2022
Normalized gradient flow optimization in the training of ReLU artificial neural networks Simon Eberle Arnulf Jentzen Adrian Riekert G. Weiss 24 0 0 13 Jul 2022
$On bounds for norms of reparameterized ReLU artificial neural network parameters: sums of fractional powers of the Lipschitz norm control the network parameter vector$ On bounds for norms of reparameterized ReLU artificial neural network parameters: sums of fractional powers of the Lipschitz norm control the network parameter vector Arnulf Jentzen T. Kröger 22 0 0 27 Jun 2022
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 119 1,198 0 16 Aug 2016