| RenderGAN: Generating realistic labeled data L Sixt, B Wild, T Landgraf Frontiers in Robotics and AI 5, 66, 2018 | 162 | 2018 |
| Restricting the Flow: Information Bottlenecks for Attribution K Schulz, L Sixt, F Tombari, T Landgraf International Conference on Learning Representations, 2020 | 97 | 2020 |
| When Explanations Lie: Why Many Modified BP Attributions Fail L Sixt, M Granz, T Landgraf International Conference on Machine Learning, 9046-9057, 2020 | 82* | 2020 |
| Automatic localization and decoding of honeybee markers using deep convolutional neural networks B Wild, L Sixt, T Landgraf arXiv preprint arXiv:1802.04557, 2018 | 22 | 2018 |
| Rendergan: Generating realistic labeled data–with an application on decoding bee tags L Sixt unpublished Bachelor Thesis, Freie Universität, Berlin, 2016 | 9 | 2016 |
| Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset L Sixt, M Schuessler, OI Popescu, P Weiß, T Landgraf arXiv preprint arXiv:2204.11642, 2022 | 2 | 2022 |
| Two4two: Evaluating interpretable machine learning-a synthetic dataset for controlled experiments M Schuessler, P Weiß, L Sixt arXiv preprint arXiv:2105.02825, 2021 | 2 | 2021 |
| Interpretability Through Invertibility: A Deep Convolutional Network With Ideal Counterfactuals And Isosurfaces L Sixt, M Schuessler, P Weiß, T Landgraf | 2 | 2020 |
| DNNR: Differential Nearest Neighbors Regression Y Nader, L Sixt, T Landgraf arXiv preprint arXiv:2205.08434, 2022 | | 2022 |
| Analyzing a Caching Model L Sixt, EZ Liu, M Pellat, J Wexler, H Milad, B Kim, M Maas arXiv preprint arXiv:2112.06989, 2021 | | 2021 |
| The emojicite package Adds Emojis to Citations L Sixt | | |
Tim LandgrafDept. of Mathematics and Computer Science, Free University BerlinVerified email at fu-berlin.de
