| A2d2: Audi autonomous driving dataset J Geyer, Y Kassahun, M Mahmudi, X Ricou, R Durgesh, AS Chung, ... arXiv preprint arXiv:2004.06320, 2020 | 488 | 2020 |
| Center3d: Center-based monocular 3d object detection with joint depth understanding Y Tang, S Dorn, C Savani Pattern Recognition: 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen …, 2021 | 50 | 2021 |
| A2d2: Audi autonomous driving dataset. arXiv 2020 J Geyer, Y Kassahun, M Mahmudi, X Ricou, R Durgesh, AS Chung, ... arXiv preprint arXiv:2004.06320, 2004 | 48 | 2004 |
| A2D2: AEV autonomous driving dataset J Geyer, Y Kassahun, M Mahmudi, X Ricou, R Durgesh, AS Chung, ... arXiv preprint arXiv:2004.06320, 2019 | 40 | 2019 |
| The primordial magnetic field in our cosmic backyard S Hutschenreuter, S Dorn, J Jasche, F Vazza, D Paoletti, G Lavaux, ... Classical and Quantum Gravity 35 (15), 154001, 2018 | 29 | 2018 |
| Generic inference of inflation models by non-Gaussianity and primordial power spectrum reconstruction S Dorn, E Ramirez, KE Kunze, S Hofmann, TA Ensslin Journal of Cosmology and Astroparticle Physics 2014 (06), 048, 2014 | 16 | 2014 |
| Cosmic expansion history from SNe Ia data via information field theory: the charm code N Porqueres, TA Enßlin, M Greiner, V Böhm, S Dorn, P Ruiz-Lapuente, ... Astronomy & Astrophysics 599, A92, 2017 | 13 | 2017 |
| Stochastic determination of matrix determinants S Dorn, TA Enßlin Physical Review E 92 (1), 013302, 2015 | 13 | 2015 |
| Fast and precise way to calculate the posterior for the local non-Gaussianity parameter <?format ?> from cosmic microwave background observations S Dorn, N Oppermann, R Khatri, M Selig, TA Enßlin Physical Review D—Particles, Fields, Gravitation, and Cosmology 88 (10), 103516, 2013 | 12 | 2013 |
| Diagnostics for insufficiencies of posterior calculations in Bayesian signal inference S Dorn, N Oppermann, TA Enßlin Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 88 (5 …, 2013 | 9 | 2013 |
| Signal inference with unknown response: Calibration-uncertainty renormalized estimator S Dorn, TA Enßlin, M Greiner, M Selig, V Boehm Physical Review E 91 (1), 013311, 2015 | 8 | 2015 |
| All-sky reconstruction of the primordial scalar potential from WMAP temperature data S Dorn, M Greiner, TA Enßlin Journal of Cosmology and Astroparticle Physics 2015 (02), 041, 2015 | 7 | 2015 |
| Optimization and interpretability of graph attention networks for small sparse graph structures in automotive applications M Neumeier, A Tollkühn, S Dorn, M Botsch, W Utschick 2023 IEEE Intelligent Vehicles Symposium (IV), 1-8, 2023 | 6 | 2023 |
| A2D2: Audi Autonomous Driving Dataset (arXiv: 2004.06320). arXiv J Geyer, Y Kassahun, M Mahmudi, X Ricou, R Durgesh, AS Chung, ... | 4 | 2004 |
| Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain M Neumeier, S Dorn, M Botsch, W Utschick 2023 IEEE 26th International Conference on Intelligent Transportation …, 2023 | 2 | 2023 |
| Bayesian inference of early-universe signals S Dorn lmu, 2016 | 2 | 2016 |
| Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models M Neumeier, S Dorn, M Botsch, W Utschick Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 1 | 2024 |
| Gradient Derivation for Learnable Parameters in Graph Attention Networks M Neumeier, A Tollkühn, S Dorn, M Botsch, W Utschick arXiv preprint arXiv:2304.10939, 2023 | 1 | 2023 |
| Classification and Uncertainty Quantification of Corrupted Data Using Supervised Autoencoders P Joppich, S Dorn, O De Candido, J Knollmüller, W Utschick Physical Sciences Forum 5 (1), 12, 2022 | 1 | 2022 |
| Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders P Joppich, S Dorn, O De Candido, W Utschick, J Knollmüller arXiv preprint arXiv:2105.13393, 2021 | | 2021 |
Torsten EnsslinMax Planck Institute for Astrophysics, Ludwig Maximilians University MunichVerified email at MPA-Garching.MPG.DE
