| Attentional feature fusion Y Dai, F Gieseke, S Oehmcke, Y Wu, K Barnard Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021 | 77 | 2021 |
| Input quality aware convolutional LSTM networks for virtual marine sensors S Oehmcke, O Zielinski, O Kramer Neurocomputing 275, 2603-2615, 2018 | 43 | 2018 |
| kNN ensembles with penalized DTW for multivariate time series imputation S Oehmcke, O Zielinski, O Kramer 2016 International Joint Conference on Neural Networks (IJCNN), 2774-2781, 2016 | 34 | 2016 |
| Event detection in marine time series data S Oehmcke, O Zielinski, O Kramer Joint German/Austrian Conference on Artificial Intelligence (Künstliche …, 2015 | 16 | 2015 |
| Detecting hardly visible roads in low-resolution satellite time series data S Oehmcke, C Thrysøe, A Borgstad, MAV Salles, M Brandt, F Gieseke 2019 IEEE International Conference on Big Data (Big Data), 2403-2412, 2019 | 13 | 2019 |
| Storyteller: in-situ reflection on study experiences B Poppinga, S Oehmcke, W Heuten, S Boll Proceedings of the 15th international conference on Human-computer …, 2013 | 10 | 2013 |
| Spatio-temporal wind power prediction using recurrent neural networks WL Woon, S Oehmcke, O Kramer International Conference on Neural Information Processing, 556-563, 2017 | 7 | 2017 |
| Recurrent neural networks and exponential PAA for virtual marine sensors S Oehmcke, O Zielinski, O Kramer 2017 International Joint Conference on Neural Networks (IJCNN), 4459-4466, 2017 | 7 | 2017 |
| Analysis of diversity methods for evolutionary multi-objective ensemble classifiers S Oehmcke, J Heinermann, O Kramer European Conference on the Applications of Evolutionary Computation, 567-578, 2015 | 6 | 2015 |
| Knowledge sharing for population based neural network training S Oehmcke, O Kramer Joint German/Austrian Conference on Artificial Intelligence (Künstliche …, 2018 | 5 | 2018 |
| Evolution of stacked autoencoders T Silhan, S Oehmcke, O Kramer 2019 IEEE Congress on Evolutionary Computation (CEC), 823-830, 2019 | 4 | 2019 |
| Manifold learning with iterative dimensionality photo-projection D Lückehe, S Oehmcke, O Kramer 2017 International Joint Conference on Neural Networks (IJCNN), 2555-2561, 2017 | 4 | 2017 |
| Attention as activation Y Dai, S Oehmcke, F Gieseke, Y Wu, K Barnard 2020 25th International Conference on Pattern Recognition (ICPR), 9156-9163, 2021 | 3 | 2021 |
| Creating cloud-free satellite imagery from image time series with deep learning S Oehmcke, THK Chen, AV Prishchepov, F Gieseke Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics …, 2020 | 3 | 2020 |
| Magnitude and uncertainty pruning criterion for neural networks V Ko, S Oehmcke, F Gieseke 2019 IEEE International Conference on Big Data (Big Data), 2317-2326, 2019 | 3 | 2019 |
| Direct training of dynamic observation noise with UMarineNet S Oehmcke, O Zielinski, O Kramer International Conference on Artificial Neural Networks, 123-133, 2018 | 2 | 2018 |
| Preferences-based choice prediction in evolutionary multi-objective optimization M Aggarwal, J Heinermann, S Oehmcke, O Kramer European Conference on the Applications of Evolutionary Computation, 715-724, 2017 | 2 | 2017 |
| Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass S Oehmcke, L Li, J Revenga, T Nord-Larsen, K Trepekli, F Gieseke, C Igel arXiv preprint arXiv:2112.11335, 2021 | 1 | 2021 |
| Prediction of above ground biomass and C-stocks based on UAV-LiDAR, multispectral imagery and machine learning methods. JC Revenga, K Trepekli, S Oehmcke, F Gieseke, C Igel, R Jensen, ... EGU21, 2021 | 1 | 2021 |
| Remember to correct the bias when using deep learning for regression! C Igel, S Oehmcke arXiv preprint arXiv:2203.16470, 2022 | | 2022 |
Fabian GiesekeProfessor at Department of Information Systems, University of MünsterVerified email at uni-muenster.de
