| 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 | 76 | 2019 |
| 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 | 72 | 2020 |
| 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 | 14 | 2017 |
| 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 |
| 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 International Conference on Medical Image Computing and Computer-Assisted …, 2015 | 1 | 2015 |
| 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 | | 2022 |
| 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 arXiv preprint arXiv:2204.03439, 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 Zürich, and DistinguishedVerified email at tuebingen.mpg.de
