A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions

1 April 2021

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

7 / 7 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 56 1 0 03 Mar 2025
Operator theory, kernels, and Feedforward Neural Networks P. Jorgensen Myung-Sin Song James Tian 30 0 0 03 Jan 2023
Identical Image Retrieval using Deep Learning Sayan Nath Nikhil Nayak VLM 24 1 0 10 May 2022
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 Adrian Riekert Luca Scarpa MLT 32 6 0 13 Dec 2021
Existence, uniqueness, and convergence rates for gradient flows in the training of artificial neural networks with ReLU activation Simon Eberle Arnulf Jentzen Adrian Riekert G. Weiss 31 12 0 18 Aug 2021
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 Arnulf Jentzen Adrian Riekert 25 13 0 10 Aug 2021
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation Arnulf Jentzen Adrian Riekert 19 23 0 09 Jul 2021