| Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes S Dereich, S Kassing arXiv preprint arXiv:2102.09385, 2021 | 27 | 2021 |
| On minimal representations of shallow ReLU networks S Dereich, S Kassing Neural Networks 148, 121-128, 2022 | 14 | 2022 |
| Cooling down stochastic differential equations: Almost sure convergence S Dereich, S Kassing Stochastic Processes and their Applications 152, 289-311, 2022 | 8 | 2022 |
| Central limit theorems for stochastic gradient descent with averaging for stable manifolds S Dereich, S Kassing Electronic Journal of Probability 28, 1-48, 2023 | 7* | 2023 |
| On the existence of minimizers in shallow residual ReLU neural network optimization landscapes S Dereich, A Jentzen, S Kassing arXiv preprint arXiv:2302.14690, 2023 | 5 | 2023 |
| Stochastic modified flows, mean-field limits and dynamics of stochastic gradient descent B Gess, S Kassing, V Konarovskyi Journal of Machine Learning Research 25 (30), 1-27, 2024 | 4 | 2024 |
| On the Existence of Optimal Shallow Feedforward Networks with ReLU Activation S Dereich, S Kassing Journal of Machine Learning 3 (1), 1-22, 2024 | 3 | 2024 |
| Convergence rates for momentum stochastic gradient descent with noise of machine learning type B Gess, S Kassing arXiv preprint arXiv:2302.03550, 2023 | 1 | 2023 |
| Stochastic Modified Flows for Riemannian Stochastic Gradient Descent B Gess, S Kassing, N Rana arXiv preprint arXiv:2402.03467, 2024 | | 2024 |
| Stochastic Approximation with a Focus on Machine Learning Applications S Kassing Westfälische Wilhelms-Universität Münster, 2021 | | 2021 |
Benjamin GessBielefeld University & Max Planck Institute for Mathematics in the Sciences, LeipzigBestätigte E-Mail-Adresse bei math.uni-bielefeld.de
