| Efficient approximation of high-dimensional functions with neural networks P Cheridito, A Jentzen, F Rossmannek IEEE Transactions on Neural Networks and Learning Systems, 2021 | 45* | 2021 |
| Non-convergence of stochastic gradient descent in the training of deep neural networks P Cheridito, A Jentzen, F Rossmannek Journal of Complexity 64, 101540, 2021 | 36 | 2021 |
| A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions P Cheridito, A Jentzen, A Riekert, F Rossmannek Journal of Complexity, 101646, 2022 | 25 | 2022 |
| Landscape analysis for shallow neural networks: complete classification of critical points for affine target functions P Cheridito, A Jentzen, F Rossmannek Journal of Nonlinear Science 32 (5), 1-45, 2022 | 18 | 2022 |
| Gradient descent provably escapes saddle points in the training of shallow ReLU networks P Cheridito, A Jentzen, F Rossmannek arXiv preprint arXiv:2208.02083, 2022 | 4 | 2022 |
| Efficient Sobolev approximation of linear parabolic PDEs in high dimensions P Cheridito, F Rossmannek arXiv preprint arXiv:2306.16811, 2023 | 3 | 2023 |
| A comment on approximation capacities of neural networks F Rossmannek florian.rossmannek.com/pdfs/ReLUvSig.pdf, 2024 | | 2024 |
| The curse of dimensionality and gradient-based training of neural networks: shrinking the gap between theory and applications F Rossmannek ETH Zurich, 2023 | | 2023 |
