| Deep learning as a competitive feature-free approach for automated algorithm selection on the traveling salesperson problem M Seiler, J Pohl, J Bossek, P Kerschke, H Trautmann International Conference on Parallel Problem Solving from Nature, 48-64, 2020 | 28 | 2020 |
| FakeYou!-a gamified approach for building and evaluating resilience against fake news L Clever, D Assenmacher, K Müller, MV Seiler, DM Riehle, M Preuss, ... Multidisciplinary international symposium on disinformation in open online …, 2020 | 27 | 2020 |
| A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes MV Seiler, RP Prager, P Kerschke, H Trautmann Proceedings of the Genetic and Evolutionary Computation Conference, 657-665, 2022 | 21 | 2022 |
| Moderator- and Crowd-Annotated German News Comment Datasets D Assenmacher, M Niemann, K Müller, M Seiler, DM Riehle, H Trautmann Thirty-fifth conference on neural information processing systems datasets …, 2021 | 21 | 2021 |
| A Twitter Streaming Dataset collected before and after the Onset of the War between Russia and Ukraine in 2022 J Pohl, MV Seiler, D Assenmacher, C Grimme Available at SSRN 4066543, 2022 | 20 | 2022 |
| Automated algorithm selection in single-objective continuous optimization: a comparative study of deep learning and landscape analysis methods RP Prager, MV Seiler, H Trautmann, P Kerschke International Conference on Parallel Problem Solving from Nature, 3-17, 2022 | 19 | 2022 |
| Towards feature-free automated algorithm selection for single-objective continuous black-box optimization RP Prager, MV Seiler, H Trautmann, P Kerschke 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2021 | 17 | 2021 |
| A study on the effects of normalized TSP features for automated algorithm selection J Heins, J Bossek, J Pohl, M Seiler, H Trautmann, P Kerschke Theoretical Computer Science 940, 123-145, 2023 | 13 | 2023 |
| Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single-and Multi-Objective Continuous Optimization Problems MV Seiler, P Kerschke, H Trautmann arXiv preprint arXiv:2401.01192, 2024 | 9 | 2024 |
| Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. JS Pohl, D Assenmacher, MV Seiler, H Trautmann, C Grimme ICWSM Workshops, 2022 | 6 | 2022 |
| On the potential of normalized tsp features for automated algorithm selection J Heins, J Bossek, J Pohl, M Seiler, H Trautmann, P Kerschke Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic …, 2021 | 5 | 2021 |
| Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP MV Seiler, J Rook, J Heins, OL Preuß, J Bossek, H Trautmann 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 361-368, 2023 | 4 | 2023 |
| Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis S Noteboom, M Seiler, C Chien, RP Rane, F Barkhof, EMM Strijbis, F Paul, ... Journal of Neurology 271 (8), 5577-5589, 2024 | 1 | 2024 |
| Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection M Seiler, U Skvorc, C Doerr, H Trautmann The 18th Learning and Intelligent OptimizatioN Conference (LION 2024), 2024 | 1 | 2024 |
| Pioneering new paths: the role of generative modelling in neurological disease research M Seiler, K Ritter Pflügers Archiv-European Journal of Physiology, 1-19, 2024 | | 2024 |
| Learned Features vs. Classical ELA on Affine BBOB Functions M Seiler, U Škvorc, G Cenikj, C Doerr, H Trautmann International Conference on Parallel Problem Solving from Nature, 137-153, 2024 | | 2024 |
| Potential and Challenges of Feature-free Automated Algorithm Selection and Configuration MV Seiler Universität Münster, 2023 | | 2023 |
| Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries M Seiler, H Trautmann, P Kerschke 2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020 | | 2020 |
| Machine learning based prediction of clinical progression in multiple sclerosis S Noteboom, M Seiler, C Chien, RP Rane, EMM Strijbis, F Paul, ... | | |
| Automated Algorithm Configuration of Sparse Neural Networks MV Seiler | | |
