| Transformer-based World Models Are Happy With 100k Interactions J Robine, M Höftmann, T Uelwer, S Harmeling International Conference on Learning Representations (ICLR), 2023 | 60 | 2023 |
| Phase Retrieval Using Conditional Generative Adversarial Networks T Uelwer, A Oberstraß, S Harmeling 2020 25th International Conference on Pattern Recognition (ICPR), 731-738, 2021 | 33 | 2021 |
| On the Vulnerability of Capsule Networks to Adversarial Attacks F Michels, T Uelwer, E Upschulte, S Harmeling ICML 2019 Workshop on Security and Privacy of Machine Learning, 2019 | 27 | 2019 |
| Non-iterative Phase Retrieval with Cascaded Neural Networks T Uelwer, T Hoffmann, S Harmeling International Conference on Artificial Neural Networks, 295-306, 2021 | 15 | 2021 |
| PyMatting: A Python Library for Alpha Matting T Germer, T Uelwer, S Conrad, S Harmeling Journal of Open Source Software 5 (54), 2481, 2020 | 15 | 2020 |
| Fast Multi-Level Foreground Estimation T Germer, T Uelwer, S Conrad, S Harmeling 2020 25th International Conference on Pattern Recognition (ICPR), 1104-1111, 2021 | 12 | 2021 |
| Smaller world models for reinforcement learning J Robine, T Uelwer, S Harmeling Neural Processing Letters 55 (8), 11397-11427, 2023 | 10* | 2023 |
| Comparison of cardiac volumetry using real-time MRI during free-breathing with standard cine MRI during breath-hold in children LM Röwer, KL Radke, J Hußmann, H Malik, T Uelwer, D Voit, J Frahm, ... Pediatric Radiology 52 (8), 1462-1475, 2022 | 10 | 2022 |
| Spirometry‐based reconstruction of real‐time cardiac MRI: Motion control and quantification of heart-lung interactions LM Röwer, T Uelwer, J Hußmann, H Malik, M Eichinger, D Voit, ... Magnetic Resonance in Medicine, 2021 | 10 | 2021 |
| A Survey on Self-Supervised Representation Learning T Uelwer, J Robine, SS Wagner, M Höftmann, E Upschulte, S Konietzny, ... arXiv preprint arXiv:2308.11455, 2023 | 5 | 2023 |
| Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data T Germer, J Robine, S Konietzny, S Harmeling, T Uelwer arXiv preprint arXiv:2309.06948, 2023 | 3 | 2023 |
| Optimizing Intermediate Representations of Generative Models for Phase Retrieval T Uelwer, S Konietzny, S Harmeling Transactions on Machine Learning Research (TMLR), 2022 | 3 | 2022 |
| Deblurring photographs of characters using deep neural networks T Germer, T Uelwer, S Harmeling Inverse Problems and Imaging 17 (5), 993-1007, 2022 | 2 | 2022 |
| A Closer Look at Reference Learning for Fourier Phase Retrieval T Uelwer, N Rucks, S Harmeling NeurIPS 2021 Workshop on Deep Learning and Inverse Problems, 2021 | 2 | 2021 |
| [Re] Solving Phase Retrieval With a Learned Reference N Rucks, T Uelwer, S Harmeling ML Reproducibility Challenge 2021 (Fall Edition), 2022 | 1 | 2022 |
| Learning to Detect Adversarial Examples Based on Class Scores T Uelwer, F Michels, O De Candido German Conference on Artificial Intelligence (Künstliche Intelligenz), 233-240, 2021 | 1 | 2021 |
| Learning Conditional Generative Models for Phase Retrieval T Uelwer, S Konietzny, A Oberstrass, S Harmeling Journal of Machine Learning Research 24 (332), 1-28, 2023 | | 2023 |
| Evaluating Robust Perceptual Losses for Image Reconstruction T Uelwer, F Michels, O De Candido I Can't Believe It's Not Better Workshop: Understanding Deep Learning …, 2022 | | 2022 |
| Learning to Plan via a Multi-step Policy Regression Method S Wagner, M Janschek, T Uelwer, S Harmeling International Conference on Artificial Neural Networks, 481-492, 2021 | | 2021 |
Stefan HarmelingProfessor at Technische Universität DortmundVerified email at tu-dortmund.de
