Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation

3 March 2020

Papers citing "Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation"

7 / 7 papers shown

Title
Error analysis for deep neural network approximations of parametric hyperbolic conservation laws Tim De Ryck Siddhartha Mishra PINN 15 10 0 15 Jul 2022
On the approximation of functions by tanh neural networks Tim De Ryck S. Lanthaler Siddhartha Mishra 21 137 0 18 Apr 2021
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 MLT 32 13 0 01 Apr 2021
Convergence rates for gradient descent in the training of overparameterized artificial neural networks with biases Arnulf Jentzen T. Kröger ODL 28 7 0 23 Feb 2021
Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes Steffen Dereich Sebastian Kassing 34 27 0 16 Feb 2021
Non-convergence of stochastic gradient descent in the training of deep neural networks Patrick Cheridito Arnulf Jentzen Florian Rossmannek 14 37 0 12 Jun 2020
Solving the Kolmogorov PDE by means of deep learning C. Beck S. Becker Philipp Grohs Nor Jaafari Arnulf Jentzen 6 91 0 01 Jun 2018