| Informed machine learning–a taxonomy and survey of integrating prior knowledge into learning systems L Von Rueden, S Mayer, K Beckh, B Georgiev, S Giesselbach, R Heese, ... IEEE Transactions on Knowledge and Data Engineering 35 (1), 614-633, 2021 | 766 | 2021 |
| Combining machine learning and simulation to a hybrid modelling approach: Current and future directions L von Rueden, S Mayer, R Sifa, C Bauckhage, J Garcke Advances in Intelligent Data Analysis XVIII: 18th International Symposium on …, 2020 | 225 | 2020 |
| Informed machine learning–towards a taxonomy of explicit integration of knowledge into machine learning L Von Rueden, S Mayer, J Garcke, C Bauckhage, J Schuecker Learning 18, 19-20, 2019 | 77 | 2019 |
| On weighted Hilbert spaces and integration of functions of infinitely many variables M Gnewuch, S Mayer, K Ritter Journal of Complexity 30 (2), 29-47, 2014 | 47 | 2014 |
| Counting via entropy: new preasymptotics for the approximation numbers of Sobolev embeddings T Kühn, S Mayer, T Ullrich SIAM Journal on Numerical Analysis 54 (6), 3625-3647, 2016 | 40 | 2016 |
| Entropy and sampling numbers of classes of ridge functions S Mayer, T Ullrich, J Vybiral Constructive Approximation 42 (2), 231-264, 2015 | 32 | 2015 |
| Application cases of biological transformation in manufacturing technology T Bergs, U Schwaneberg, S Barth, L Hermann, T Grunwald, S Mayer, ... CIRP journal of Manufacturing Science and Technology 31, 68-77, 2020 | 23 | 2020 |
| Entropy numbers of finite dimensional mixed-norm balls and function space embeddings with small mixed smoothness S Mayer, T Ullrich Constructive Approximation 53, 249-279, 2021 | 16 | 2021 |
| Knowledge-based adaptation of product and process design in blisk manufacturing P Ganser, M Landwehr, S Schiller, C Vahl, S Mayer, T Bergs Journal of Engineering for Gas Turbines and Power 144 (1), 011023, 2022 | 10 | 2022 |
| The recovery of ridge functions on the hypercube suffers from the curse of dimensionality B Doerr, S Mayer Journal of Complexity 63, 101521, 2021 | 9 | 2021 |
| Decision support by interpretable machine learning in acoustic emission based cutting tool wear prediction A Schmetz, C Vahl, Z Zhen, D Reibert, S Mayer, D Zontar, J Garcke, ... 2021 IEEE International Conference on Industrial Engineering and Engineering …, 2021 | 7 | 2021 |
| Counting via entropy—new preasymptotics for the approximation numbers of Sobolev embeddings. ArXiv e-prints (2015) T Kühn, S Mayer, T Ullrich arXiv preprint arXiv:1505.00631, 0 | 6 | |
| Preasymptotic error bounds via metric entropy SA Mayer Verlag Dr. Hut, 2018 | 2 | 2018 |
| Entropy numbers of spheres in Banach and quasi-Banach spaces A Hinrichs, S Mayer Journal of Approximation Theory 200, 144-152, 2015 | 2 | 2015 |
| Randomized Dimensionality Reduction in Machine Learning S Mayer matrix 100 (R2), r2, 0 | | |
| Pathological artifical neurons S Mayer | | |
| Tractability results for classes of ridge functions S MAYER | | |
Jochen GarckeUniversität Bonn, Fraunhofer SCAI在 ins.uni-bonn.de 的电子邮件经过验证
