| Machine learning outperforms classical forecasting on horticultural sales predictions F Haselbeck, J Killinger, K Menrad, T Hannus, DG Grimm Machine Learning with Applications 7, 100239, 2022 | 37 | 2022 |
| Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields N Genze, R Ajekwe, Z Güreli, F Haselbeck, M Grieb, DG Grimm Computers and Electronics in Agriculture 202, 107388, 2022 | 30 | 2022 |
| A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species M John, F Haselbeck, R Dass, C Malisi, P Ricca, C Dreischer, ... Frontiers in Plant Science 13, 932512, 2022 | 12 | 2022 |
| EVARS-GPR: EVent-triggered augmented refitting of gaussian process regression for seasonal data F Haselbeck, DG Grimm KI 2021: Advances in Artificial Intelligence: 44th German Conference on AI …, 2021 | 9 | 2021 |
| Dynamically self-adjusting Gaussian processes for data stream modelling JD Hüwel, F Haselbeck, DG Grimm, C Beecks German Conference on Artificial Intelligence (Künstliche Intelligenz), 96-114, 2022 | 7 | 2022 |
| Inter-individual variability of eeg features during microsleep events M Golz, A Schenka, F Haselbeck, MP Pauli Current Directions in Biomedical Engineering 5 (1), 13-16, 2019 | 6 | 2019 |
| Superior protein thermophilicity prediction with protein language model embeddings F Haselbeck, M John, Y Zhang, J Pirnay, JP Fuenzalida-Werner, ... NAR Genomics and Bioinformatics 5 (4), lqad087, 2023 | 3 | 2023 |
| ForeTiS: A comprehensive time series forecasting framework in Python J Eiglsperger, F Haselbeck, DG Grimm Machine Learning with Applications 12, 100467, 2023 | 1 | 2023 |
| Forecasting seasonally fluctuating sales of perishable products in the horticultural industry J Eiglsperger, F Haselbeck, V Stiele, CG Serrano, K Lim-Trinh, K Menrad, ... Expert Systems with Applications, 123438, 2024 | | 2024 |
| Time Series Forecasting with Self-Adaptive Gaussian Process Regression F Haselbeck Technische Universität München, 2023 | | 2023 |
| easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization F Haselbeck, M John, DG Grimm Bioinformatics Advances 3 (1), vbad035, 2023 | | 2023 |
Dominik G. GrimmProfessor of Bioinformatics at TUM Campus Straubing, HSWTVerified email at hswt.de
