| Enhancing gravitational-wave science with machine learning E Cuoco, J Powell, M Cavaglià, K Ackley, M Bejger, C Chatterjee, ... Machine Learning: Science and Technology 2 (1), 011002, 2020 | 110 | 2020 |
| Convolutional neural networks: a magic bullet for gravitational-wave detection? TD Gebhard, N Kilbertus, I Harry, B Schölkopf Physical Review D 100 (6), 063015, 2019 | 105 | 2019 |
| CONVWAVE: Searching for Gravitational Waves with Fully Convolutional Neural Nets T Gebhard, N Kilbertus, G Parascandolo, I Harry, B Schölkopf Workshop on Deep Learning for Physical Sciences (DLPS) at the 31st …, 2017 | 16 | 2017 |
| Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework TD Gebhard, MJ Bonse, SP Quanz, B Schölkopf Astronomy & Astrophysics 666 (A9), 2022 | 4 | 2022 |
| Using machine learning to parameterize pressure-temperature profiles for atmospheric retrievals of exoplanets T Gebhard, D Angerhausen, E Alei, B Konrad, B Schölkopf, SP Quanz 2022 Astrobiology Science Conference, 2022 | 2 | 2022 |
| Software Quality Control at Belle II M Ritter, T Kuhr, C Pulvermacher, M Kristof, T Hauth, T Gebhard J. Phys. Conf. Ser. 898, 072029, 2017 | 2 | 2017 |
| Comparing Apples with Apples: Statistically sound Detection Limits for Exoplanet High Contrast Imaging M Bonse, E Garvin, T Gebhard, F Dannert, G Cugno, S Quanz Bulletin of the American Astronomical Society 54 (5), 102.392, 2022 | 1 | 2022 |
| Physically constrained causal noise models for high-contrast imaging of exoplanets TD Gebhard, MJ Bonse, SP Quanz, B Schölkopf | 1 | 2020 |
| Sample size estimation for outlier detection T Gebhard, I Koerte, S Bouix Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th …, 2015 | 1 | 2015 |
| Applefy: Robust detection limits for high-contrast imaging MJ Bonse, T Gebhard Astrophysics Source Code Library, ascl: 2304.002, 2023 | | 2023 |
| Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise MJ Bonse, EO Garvin, TD Gebhard, FA Dannert, F Cantalloube, G Cugno, ... arXiv preprint arXiv:2303.12030, 2023 | | 2023 |
| Modeling Molecular Complexity: Building a Novel Multidisciplinary Machine Learning Framework to Understand Molecular Synthesis and Signatures JJA Hastings, AC Bell, T Gebhard, J Gong, AG Baydin, M Fricke, ... AGU Fall Meeting Abstracts 2022, IN22D-0334, 2022 | | 2022 |
| Molecular Complexity to Biosignatures: A Machine Learning Pipeline that Connects Mass Spectrometry to Molecular Synthesis and Reaction Networks J Gong, AC Bell, T Gebhard, JJA Hastings, AG Baydin, K Warren-Rhodes, ... AGU Fall Meeting Abstracts 2022, P25A-75, 2022 | | 2022 |
| Atmospheric retrievals of exoplanets using learned parameterizations of pressure-temperature profiles TD Gebhard, D Angerhausen, B Konrad, E Alei, SP Quanz, B Schölkopf Machine Learning and the Physical Sciences workshop at NeurIPS 2022, 2022 | | 2022 |
| Inferring molecular complexity from mass spectrometry data using machine learning TD Gebhard, AC Bell, J Gong, JJA Hastings, GM Fricke, N Cabrol, ... Machine Learning and the Physical Sciences workshop at NeurIPS 2022, 2022 | | 2022 |
| On the Applicability of Machine Learning to Aid the Search for Gravitational Waves at the LIGO Experiment T Gebhard Karlsruher Institut für Technologie Karlsruhe, 2018 | | 2018 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH ZürichVerified email at tuebingen.mpg.de
