| A branch--and--bound-based algorithm for nonconvex multiobjective optimization J Niebling, G Eichfelder SIAM Journal on Optimization 29 (1), 794-821, 2019 | 43 | 2019 |
| Solving multiobjective mixed integer convex optimization problems M De Santis, G Eichfelder, J Niebling, S Rocktäschel SIAM Journal on Optimization 30 (4), 3122-3145, 2020 | 36 | 2020 |
| Evaluation of multi feature fusion at score-level for appearance-based person re-identification M Eisenbach, A Kolarow, A Vorndran, J Niebling, HM Gross 2015 international joint conference on neural networks (IJCNN), 1-8, 2015 | 31 | 2015 |
| An algorithmic approach to multiobjective optimization with decision uncertainty G Eichfelder, J Niebling, S Rocktäschel Journal of Global Optimization 77 (1), 3-25, 2020 | 18 | 2020 |
| Analysis of railway track irregularities with convolutional autoencoders and clustering algorithms J Niebling, B Baasch, A Kruspe Dependable Computing-EDCC 2020 Workshops: AI4RAILS, DREAMS, DSOGRI, SERENE …, 2020 | 6 | 2020 |
| Nonconvex constrained optimization by a filtering branch and bound G Eichfelder, K Klamroth, J Niebling Journal of Global Optimization 80 (1), 31-61, 2021 | 4 | 2021 |
| A branch-and-bound algorithm for biobjective problems J Niebling, G Eichfelder Proceedings of the XIII Global Optimization Workshop GOW16, 57-60, 2016 | 4 | 2016 |
| Using a B&B algorithm from multiobjective optimization to solve constrained optimization problems G Eichfelder, K Klamroth, J Niebling AIP Conference Proceedings 2070 (1), 020028, 2019 | 2 | 2019 |
| Analysing the Interactions Between Training Dataset Size, Label Noise and Model Performance in Remote Sensing Data J Gütter, J Niebling, XX Zhu IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium …, 2022 | 1 | 2022 |
| IS IT WORTH IT? COMPARING SIX DEEP AND CLASSICAL METHODS FOR UNSUPERVISED ANOMALY DETECTION IN TIME SERIES F Rewicki, J Denzler, J Niebling | | 2023 |
| Is it worth it? An experimental comparison of six deep-and classical machine learning methods for unsupervised anomaly detection in time series F Rewicki, J Denzler, J Niebling arXiv preprint arXiv:2212.11080, 2022 | | 2022 |
| Domain Shifts in Dermoscopic Datasets S Chamarthi, K Fogelberg, J Niebling | | 2022 |
| Robust Distribution-Shift Aware Sar-Optical data Fusion for Multi-Label Scene Classification J Gawlikowski, S Saha, J Niebling, XX Zhu IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium …, 2022 | | 2022 |
| Impact of Training Set Size on the Ability of Deep Neural Networks to Deal with Omission Noise JA Gütter, A Kruspe, XX Zhu, J Niebling Frontiers in Remote Sensing, 2022 | | 2022 |
| A multiobjective view on creating counterfactual explanations for explaining uncertainty in machine learning J Niebling | | 2022 |
| Structuring Uncertainty for Fine-Grained Sampling in Stochastic Segmentation Networks F Nussbaum, J Gawlikowski, J Niebling Advances in Neural Information Processing Systems, 2022 | | 2022 |
| Using Predictive Uncertainty for Cleaning Noisy Annotations JA Gütter, H Ulman, J Niebling | | 2022 |
| Nonconvex and mixed integer multiobjective optimization with an application to decision uncertainty J Niebling Dissertation, Ilmenau, TU Ilmenau, 2019, 2019 | | 2019 |
| Ein Branch-and-Bound-Verfahren für bikriterielle Optimierungsprobleme J Niebling | | 2015 |
| Eigenvalue conditions for induced subgraphs J Harant Deutsche Nationalbibliothek, 2014 | | 2014 |
