| 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 | 618 | 2021 |
| Wavelet-packets for deepfake image analysis and detection M Wolter, F Blanke, R Heese, J Garcke Machine Learning 111 (11), 4295-4327, 2022 | 26 | 2022 |
| Optimized data exploration applied to the simulation of a chemical process R Heese, M Walczak, T Seidel, N Asprion, M Bortz Computers & Chemical Engineering 124, 326-342, 2019 | 25 | 2019 |
| Quantum optimization: Potential, challenges, and the path forward A Abbas, A Ambainis, B Augustino, A Bärtschi, H Buhrman, C Coffrin, ... arXiv preprint arXiv:2312.02279, 2023 | 18 | 2023 |
| Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet simulations with constraints PO Ludl, R Heese, J Höller, N Asprion, M Bortz Frontiers of Chemical Science and Engineering 16 (2), 183-197, 2022 | 14 | 2022 |
| Feature selection on quantum computers S Mücke, R Heese, S Müller, M Wolter, N Piatkowski Quantum Machine Intelligence 5 (1), 11, 2023 | 12 | 2023 |
| Gradient-free quantum optimization on NISQ devices L Franken, B Georgiev, S Muecke, M Wolter, N Piatkowski, C Bauckhage arXiv preprint arXiv:2012.13453, 2020 | 12 | 2020 |
| Representation of binary classification trees with binary features by quantum circuits R Heese, P Bickert, AE Niederle Quantum 6, 676, 2022 | 10 | 2022 |
| The Good, the Bad and the Ugly: Augmenting a black-box model with expert knowledge R Heese, M Walczak, L Morand, D Helm, M Bortz Artificial Neural Networks and Machine Learning–ICANN 2019: Workshop and …, 2019 | 10 | 2019 |
| Informed machine learning-a taxonomy and survey of integrating knowledge into learning systems (2020) L Von Rueden, S Mayer, K Beckh, B Georgiev, S Giesselbach, R Heese, ... arXiv preprint arXiv:1903.12394, 1903 | 10 | 1903 |
| Quantum optimization: Potential, challenges, and the path forward (2023) A Abbas, A Ambainis, B Augustino, A Bärtschi, H Buhrman, C Coffrin, ... arXiv preprint arXiv:2312.02279, 0 | 10 | |
| Entropic uncertainty relation for pointer-based simultaneous measurements of conjugate observables R Heese, M Freyberger Physical Review A 87 (1), 012123, 2013 | 7 | 2013 |
| Explaining quantum circuits with shapley values: Towards explainable quantum machine learning R Heese, T Gerlach, S Mücke, S Müller, M Jakobs, N Piatkowski arXiv preprint arXiv:2301.09138, 2023 | 6 | 2023 |
| Quantum circuit evolution on nisq devices L Franken, B Georgiev, S Mucke, M Wolter, R Heese, C Bauckhage, ... 2022 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2022 | 5 | 2022 |
| Pointer-based simultaneous measurements of conjugate observables in a thermal environment R Heese, M Freyberger Physical Review A 89 (5), 052111, 2014 | 5 | 2014 |
| The big picture of neurodegeneration: a meta study to extract the essential evidence on neurodegenerative diseases in a network-based approach N Ruffini, S Klingenberg, R Heese, S Schweiger, S Gerber Frontiers in aging neuroscience 14, 866886, 2022 | 4 | 2022 |
| Calibrated simplex-mapping classification R Heese, J Schmid, M Walczak, M Bortz PLoS One 18 (1), e0279876, 2023 | 3 | 2023 |
| Adaptive sampling of pareto frontiers with binary constraints using regression and classification R Heese, M Bortz 2020 25th International Conference on Pattern Recognition (ICPR), 3404-3411, 2021 | 3 | 2021 |
| On the effects of biased quantum random numbers on the initialization of artificial neural networks R Heese, M Wolter, S Mücke, L Franken, N Piatkowski Machine Learning, 1-29, 2024 | 2 | 2024 |
| Multiplicities in thermodynamic activity coefficients J Werner, T Seidel, R Jafar, R Heese, H Hasse, M Bortz AIChE Journal 69 (12), e18251, 2023 | 2 | 2023 |
Michael BortzFraunhofer ITWMBestätigte E-Mail-Adresse bei itwm.fraunhofer.de
