Efficient approximation of high-dimensional functions with deep neural networks. - Patrick Cheridito; Arnulf Jentzen; Florian Rossmannek (CoRR, 2019)

Landscape analysis for shallow ReLU neural networks: complete classification of critical points for affine target functions. - Patrick Cheridito; Arnulf Jentzen; Florian Rossmannek (CoRR, 2021)

Non-convergence of stochastic gradient descent in the training of deep neural networks. - Patrick Cheridito; Arnulf Jentzen; Florian Rossmannek (J. Complex., 2021)

A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions. - Patrick Cheridito; Arnulf Jentzen; Adrian Riekert; Florian Rossmannek (J. Complex., 2022)

Landscape Analysis for Shallow Neural Networks: Complete Classification of Critical Points for Affine Target Functions. - Patrick Cheridito; Arnulf Jentzen; Florian Rossmannek (J. Nonlinear Sci., 2022)

Efficient Approximation of High-Dimensional Functions With Neural Networks. - Patrick Cheridito; Arnulf Jentzen; Florian Rossmannek (IEEE Trans. Neural Networks Learn. Syst., 2022)

Efficient Sobolev approximation of linear parabolic PDEs in high dimensions. - Patrick Cheridito; Florian Rossmannek (CoRR, 2023)

The curse of dimensionality and gradient-based training of neural networks: shrinking the gap between theory and applications. - Florian Rossmannek (2023)

State-Space Systems as Dynamic Generative Models. - Juan-Pablo Ortega; Florian Rossmannek (CoRR, 2024)

Fading memory and the convolution theorem. - Juan-Pablo Ortega; Florian Rossmannek (CoRR, 2024)

Gradient Descent Provably Escapes Saddle Points in the Training of Shallow ReLU Networks. - Patrick Cheridito; Arnulf Jentzen; Florian Rossmannek (J. Optim. Theory Appl., 2024)