Jannes Münchmeyer
Jannes Münchmeyer
ISTerre - Université Grenoble Alpes
Verified email at - Homepage
Cited by
Cited by
Nlprolog: Reasoning with weak unification for question answering in natural language
L Weber, P Minervini, J Münchmeyer, U Leser, T Rocktäschel
arXiv preprint arXiv:1906.06187, 2019
HunFlair: an easy-to-use tool for state-of-the-art biomedical named entity recognition
L Weber, M Sänger, J Münchmeyer, M Habibi, U Leser, A Akbik
Bioinformatics 37 (17), 2792-2794, 2021
HUNER: improving biomedical NER with pretraining
L Weber, J Münchmeyer, T Rocktäschel, M Habibi, U Leser
Bioinformatics 36 (1), 295-302, 2020
Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers
J Münchmeyer, J Woollam, A Rietbrock, F Tilmann, D Lange, T Bornstein, ...
Journal of Geophysical Research: Solid Earth 127 (1), e2021JB023499, 2022
The transformer earthquake alerting model: A new versatile approach to earthquake early warning
J Münchmeyer, D Bindi, U Leser, F Tilmann
Geophysical Journal International 225 (1), 646-656, 2021
Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network
J Münchmeyer, D Bindi, U Leser, F Tilmann
Geophysical Journal International 226 (2), 1086-1104, 2021
SeisBench—A toolbox for machine learning in seismology
J Woollam, J Münchmeyer, F Tilmann, A Rietbrock, D Lange, T Bornstein, ...
Seismological Society of America 93 (3), 1695-1709, 2022
Estimating genome-wide regulatory activity from multi-omics data sets using mathematical optimization
S Trescher, J Münchmeyer, U Leser
BMC systems biology 11, 1-18, 2017
Low uncertainty multifeature magnitude estimation with 3-D corrections and boosting tree regression: application to North Chile
J Münchmeyer, D Bindi, C Sippl, U Leser, F Tilmann
Geophysical Journal International 220 (1), 142-159, 2020
Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction
WJ Foster, G Ayzel, J Münchmeyer, T Rettelbach, NH Kitzmann, TT Isson, ...
Paleobiology 48 (3), 357-371, 2022
Graph neural networks for learning molecular excitation spectra
K Singh, J Münchmeyer, L Weber, U Leser, A Bande
Journal of Chemical Theory and Computation 18 (7), 4408-4417, 2022
Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling
L Weber, J Münchmeyer, S Garda, U Leser
Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021
Fast earthquake assessment and earthquake early warning dataset for Italy
J Münchmeyer, D Bindi, U Leser, F Tilmann
GFZ Data Services, 2020
A probabilistic view on rupture predictability: All earthquakes evolve similarly
J Münchmeyer, U Leser, F Tilmann
Geophysical Research Letters 49 (13), e2022GL098344, 2022
Fast earthquake assessment dataset for chile
J Münchmeyer, D Bindi, U Leser, F Tilmann
GFZ Data Services, 2021
Team–the transformer earthquake alerting model
J Münchmeyer, D Bindi, U Leser, F Tilmann
GFZ Data Services, 2021
PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning
T Bornstein, D Lange, J Münchmeyer, J Woollam, A Rietbrock, ...
arXiv preprint arXiv:2304.06635, 2023
The Northern Chile forearc constrained by 15 years of permanent seismic monitoring
C Sippl, B Schurr, J Münchmeyer, S Barrientos, O Oncken
Journal of South American Earth Sciences, 104326, 2023
How predictable are mass extinction events?
WJ Foster, BJ Allen, NH Kitzmann, J Münchmeyer, T Rettelbach, JD Witts, ...
Royal Society Open Science 10 (3), 221507, 2023
Employing machine learning pickers for routine global earthquake monitoring with SeisComP: What are the benefits and how can we quantify the uncertainty of picks?
F Tilmann, T Bornstein, J Saul, J Münchmeyer, M Beutel
EGU23, 2023
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