Timothy Gebhard
Timothy Gebhard
PhD Student at the Max Planck Institute for Intelligent Systems
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
Physically constrained causal noise models for high-contrast imaging of exoplanets
TD Gebhard, MJ Bonse, SP Quanz, B Schölkopf
Sample size estimation for outlier detection
T Gebhard, I Koerte, S Bouix
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th …, 2015
Applefy: Robust detection limits for high-contrast imaging
MJ Bonse, T Gebhard
Astrophysics Source Code Library, ascl: 2304.002, 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
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
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
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
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
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
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