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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions
Arnulf Jentzen, Adrian Riekert
Zeitschrift für angewandte Mathematik und Physik (2022) Vol. 73, Iss. 5
Open Access | Times Cited: 10

Showing 10 citing articles:

Data driven control based on Deep Q-Network algorithm for heading control and path following of a ship in calm water and waves
Sivaraman Sivaraj, Suresh Rajendran, Lokukaluge P. Perera
Ocean Engineering (2022) Vol. 259, pp. 111802-111802
Closed Access | Times Cited: 28
A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions
Patrick Cheridito, Arnulf Jentzen, Adrian Riekert, et al.
Journal of Complexity (2022) Vol. 72, pp. 101646-101646
Open Access | Times Cited: 19
On the performance of different Deep Reinforcement Learning based controllers for the path-following of a ship
Sivaraman Sivaraj, Awanish Chandra Dubey, Suresh Rajendran
Ocean Engineering (2023) Vol. 286, pp. 115607-115607
Closed Access | Times Cited: 12
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation
Arnulf Jentzen, Adrian Riekert
Journal of Mathematical Analysis and Applications (2022) Vol. 517, Iss. 2, pp. 126601-126601
Open Access | Times Cited: 12
On the Existence of Global Minima and Convergence Analyses for Gradient Descent Methods in the Training of Deep Neural Networks
Arnulf Jentzen, Adrian Riekert
Journal of Machine Learning (2022) Vol. 1, Iss. 2, pp. 141-246
Open Access | Times Cited: 6
Improved Himawari-8 10-minute scale aerosol optical depth product using deep neural network over Japan
Yunhui Tan, Quan Wang, Zhaoyang Zhang
Atmospheric Pollution Research (2023) Vol. 15, Iss. 3, pp. 102005-102005
Closed Access | Times Cited: 3
Existence, uniqueness, and convergence rates for gradient flows in the training of artificial neural networks with ReLU activation
Simon Eberle, Arnulf Jentzen, Adrian Riekert, et al.
Electronic Research Archive (2023) Vol. 31, Iss. 5, pp. 2519-2554
Open Access | Times Cited: 2
College student activity attendance management system design based on Internet of Things and deep learning technology
Yan Wang, Jinyan Pang
(2024), pp. 32-32
Closed Access
Deep reinforcement learning in playing Tetris with robotic arm experiment
Yu Yan, Peng Liu, Jin Zhao, et al.
Transactions of the Institute of Measurement and Control (2022)
Closed Access | Times Cited: 2
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
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 1
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