| Variable structures in M87* from space, time and frequency resolved interferometry P Arras, P Frank, P Haim, J Knollmüller, R Leike, M Reinecke, T Enßlin Nature Astronomy 6 (2), 259-269, 2022 | 68 | 2022 |
| NIFTy 3 – Numerical Information Field Theory: A Python Framework for Multicomponent Signal Inference on HPC Clusters T Steininger, J Dixit, P Frank, M Greiner, S Hutschenreuter, J Knollmüller, ... Annalen der Physik 531 (3), 1800290, 2019 | 37 | 2019 |
| Metric Gaussian variational inference J Knollmüller, TA Enßlin arXiv preprint arXiv:1901.11033, 2019 | 33 | 2019 |
| The ngEHT analysis challenges F Roelofs, L Blackburn, G Lindahl, SS Doeleman, MD Johnson, P Arras, ... Galaxies 11 (1), 12, 2023 | 30 | 2023 |
| Nifty5: Numerical information field theory v5 P Arras, M Baltac, TA Ensslin, P Frank, S Hutschenreuter, J Knollmueller, ... Astrophysics Source Code Library, ascl: 1903.008, 2019 | 29 | 2019 |
| Accretion flow morphology in numerical simulations of black holes from the ngEHT model library: the impact of radiation physics K Chatterjee, A Chael, P Tiede, Y Mizuno, R Emami, C Fromm, A Ricarte, ... Galaxies 11 (2), 38, 2023 | 14 | 2023 |
| Metric gaussian variational inference. arXiv 2019 J Knollmüller, TA Enßlin arXiv preprint arXiv:1901.11033, 0 | 12 | |
| Radio imaging with information field theory P Arras, J Knollrnüller, H Junklewitz, TA Enßlin 2018 26th European Signal Processing Conference (EUSIPCO), 2683-2687, 2018 | 10 | 2018 |
| Noisy independent component analysis of autocorrelated components J Knollmüller, TA Enßlin Physical Review E 96 (4), 042114, 2017 | 10 | 2017 |
| Inference of signals with unknown correlation structure from nonlinear measurements J Knollmüller, T Steininger, TA Enßlin arXiv preprint arXiv:1711.02955, 2017 | 9 | 2017 |
| The variable shadow of M87* P Arras, P Frank, P Haim, J Knollmüller, R Leike, M Reinecke, T Enßlin arXiv preprint arXiv:2002.05218 96, 2020 | 7 | 2020 |
| Encoding prior knowledge in the structure of the likelihood J Knollmüller, TA Enßlin arXiv preprint arXiv:1812.04403, 2018 | 7 | 2018 |
| Multicomponent imaging of the Fermi gamma-ray sky in the spatio-spectral domain LI Scheel-Platz, J Knollmüller, P Arras, P Frank, M Reinecke, D Jüstel, ... Astronomy & Astrophysics 680, A2, 2023 | 6 | 2023 |
| M87* in space, time, and frequency P Arras, P Frank, P Haim, J Knollmüller, R Leike, M Reinecke, T Enßlin arXiv preprint arXiv:2002.05218, 2020 | 5 | 2020 |
| Metric gaussian variational inference. arXiv J Knollmüller, TA Enßlin arXiv preprint arXiv:1901.11033, 2019 | 5 | 2019 |
| Bias-free estimation of signals on top of unknown backgrounds J Diehl, J Knollmüller, O Schulz Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2024 | 4 | 2024 |
| The Galactic 3D large-scale dust distribution via Gaussian process regression on spherical coordinates RH Leike, G Edenhofer, J Knollmüller, C Alig, P Frank, TA Enßlin arXiv preprint arXiv:2204.11715, 2022 | 3 | 2022 |
| Bayesian reasoning with trained neural networks J Knollmüller, TA Enßlin Entropy 23 (6), 693, 2021 | 3 | 2021 |
| Correlated signal inference by free energy exploration TA Enßlin, J Knollmüller arXiv preprint arXiv:1612.08406, 2016 | 3 | 2016 |
| Separating diffuse from point-like sources-a Bayesian approach J Knollmüller, P Frank, TA Enßlin arXiv preprint arXiv:1804.05591, 2018 | 2 | 2018 |
Reimar LeikeMember of Technical Staff at OpenAI在 openai.com 的电子邮件经过验证
Reimar LeikeMember of Technical Staff at OpenAI在 openai.com 的电子邮件经过验证