Universal Approximation Property of Random Neural Networks

1 code implementation • 13 Dec 2023 • Ariel Neufeld, Philipp Schmocker

In this paper, we study random neural networks which are single-hidden-layer feedforward neural networks whose weights and biases are randomly initialized.

Global universal approximation of functional input maps on weighted spaces

1 code implementation • 5 Jun 2023 • Christa Cuchiero, Philipp Schmocker, Josef Teichmann

This then applies in particular to approximation of (non-anticipative) path space functionals via functional input neural networks.

Gaussian Processes regression +1

Chaotic Hedging with Iterated Integrals and Neural Networks

no code implementations • 21 Sep 2022 • Ariel Neufeld, Philipp Schmocker

In this paper, we extend the Wiener-Ito chaos decomposition to the class of diffusion processes, whose drift and diffusion coefficient are of linear growth.