| Where is my training bottleneck? hidden trade-offs in deep learning preprocessing pipelines A Isenko, R Mayer, J Jedele, HA Jacobsen Proceedings of the 2022 International Conference on Management of Data, 1825 …, 2022 | 11 | 2022 |
| Federated fine-tuning of llms on the very edge: The good, the bad, the ugly H Woisetschläger, A Erben, S Wang, R Mayer, HA Jacobsen Proceedings of the Eighth Workshop on Data Management for End-to-End Machine …, 2024 | 9 | 2024 |
| A survey on efficient federated learning methods for foundation model training H Woisetschläger, A Isenko, S Wang, R Mayer, HA Jacobsen arXiv preprint arXiv:2401.04472, 2024 | 6 | 2024 |
| Federated Learning Priorities Under the European Union Artificial Intelligence Act H Woisetschläger, A Erben, B Marino, S Wang, ND Lane, R Mayer, ... arXiv preprint arXiv:2402.05968, 2024 | 4 | 2024 |
| Predicting the in-game status in soccer with machine learning using spatiotemporal player tracking data S Lang, R Wild, A Isenko, D Link Scientific Reports 12 (1), 16291, 2022 | 4 | 2022 |
| Fledge: Benchmarking federated machine learning applications in edge computing systems H Woisetschläger, A Isenko, R Mayer, HA Jacobsen arXiv preprint arXiv:2306.05172, 2023 | 2 | 2023 |
| How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study A Erben, R Mayer, HA Jacobsen arXiv preprint arXiv:2306.03163, 2023 | 1 | 2023 |
| On Serving Image Classification Models A González-Vidal, A Isenko, KR Jayaram Proceedings of the 9th International Workshop on Serverless Computing, 48-52, 2023 | | 2023 |
| Vorhersage des in-game Status im Fußball mit Maschinellem Lernen basierend auf zeitkontinuierlichen Spielerpositionsdaten S Lang, R Wild, A Isenko, D Link | | |
Ruben MayerUniversity of Bayreuth在 uni-bayreuth.de 的电子邮件经过验证
