Florian Rossmannek

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 11

Top Topics

High Dimensions

Training Process

Top Venues

IEEE Trans. Neural Networks Learn. Syst.

J. Nonlinear Sci.

Publications

Juan-Pablo Ortega, Florian Rossmannek
State-Space Systems as Dynamic Generative Models. CoRR (2024)
Patrick Cheridito, Florian Rossmannek
Efficient Sobolev approximation of linear parabolic PDEs in high dimensions. CoRR (2023)
Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek
Gradient descent provably escapes saddle points in the training of shallow ReLU networks. CoRR (2022)
Patrick Cheridito, Arnulf Jentzen, Adrian Riekert, Florian Rossmannek
A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions. J. Complex. 72 (2022)
Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek
Landscape Analysis for Shallow Neural Networks: Complete Classification of Critical Points for Affine Target Functions. J. Nonlinear Sci. 32 (5) (2022)
Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek
Efficient Approximation of High-Dimensional Functions With Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 33 (7) (2022)
Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek
Non-convergence of stochastic gradient descent in the training of deep neural networks. J. Complex. 64 (2021)
Patrick Cheridito, Arnulf Jentzen, Adrian Riekert, Florian Rossmannek
A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions. CoRR (2021)
Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek
Landscape analysis for shallow ReLU neural networks: complete classification of critical points for affine target functions. CoRR (2021)
Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek
Non-convergence of stochastic gradient descent in the training of deep neural networks. CoRR (2020)
Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek
Efficient approximation of high-dimensional functions with deep neural networks. CoRR (2019)