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

  • Published:2022-10 Issue: Volume:72 Page:101646
  • ISSN:0885-064X
  • Container-title:Journal of Complexity
  • language:en
  • Short-container-title:Journal of Complexity

Author:

Cheridito Patrick,Jentzen Arnulf,Riekert Adrian,Rossmannek Florian

Funder

Deutsche Forschungsgemeinschaft

Publisher

Elsevier BV

Subject

Applied Mathematics,Control and Optimization,Numerical Analysis,Statistics and Probability,Algebra and Number Theory,General Mathematics

Reference34 articles.

1. Learning and generalization in overparameterized neural networks, going beyond two layers;Allen-Zhu,2019

2. A convergence theory for deep learning via over-parameterization;Allen-Zhu,2019

3. Breaking the curse of dimensionality with convex neural networks;Bach;J. Mach. Learn. Res.,2017

4. Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n);Bach,2013

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