| Automatic posterior transformation for likelihood-free inference D Greenberg, M Nonnenmacher, J Macke International Conference on Machine Learning, 2404-2414, 2019 | 334 | 2019 |
| Flexible statistical inference for mechanistic models of neural dynamics JM Lueckmann, PJ Goncalves, G Bassetto, K Öcal, M Nonnenmacher, ... Advances in Neural Information Processing Systems, 1289-1299, 2017 | 276 | 2017 |
| Training deep neural density estimators to identify mechanistic models of neural dynamics PJ Gonçalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, ... Elife 9, e56261, 2020 | 219 | 2020 |
| Signatures of criticality arise from random subsampling in simple population models M Nonnenmacher, C Behrens, P Berens, M Bethge, JH Macke PLoS Computational Biology 13 (10), e1005718, 2017 | 44* | 2017 |
| Deep Emulators for Differentiation, Forecasting and Parametrization in Earth Science Simulators M Nonnenmacher, DS Greenberg Journal of Advances in Modeling Earth Systems 13 (7), e2021MS002554, 2021 | 28 | 2021 |
| Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations M Nonnenmacher, SC Turaga, JH Macke Advances in Neural Information Processing Systems, 5706-5716, 2017 | 24 | 2017 |
| Statistical Seasonal Prediction of European Summer Mean Temperature Using Observational, Reanalysis, and Satellite Data M Pyrina, M Nonnenmacher, S Wagner, E Zorita Weather and Forecasting 36 (4), 1537-1560, 2021 | 7 | 2021 |
| Robust statistical inference for simulation-based models in neuroscience M Nonnenmacher, PJ Goncalves, G Bassetto, JM Lueckmann, JH Macke Bernstein Conference 2018, Berlin, Germany, 2018 | 2 | 2018 |
| Learning Implicit PDE Integration with Linear Implicit Layers M Nonnenmacher, DS Greenberg The Symbiosis of Deep Learning and Differential Equations, 2021 | 1 | 2021 |
| A solution for the mean parametrization of the von Mises-Fisher distribution M Nonnenmacher, M Sahani arXiv preprint arXiv:2404.07358, 2024 | | 2024 |
| Adaptively learned calibration kernels for stable importance sampling in regression ABC M Nonnenmacher, K Oecal, JH Macke ISBA 2018 Workshop'ABC in Edinburgh', 2018, Edinburgh, UK, 2018 | | 2018 |
Jakob MackeMachine Learning in Science, Tübingen UniversityVerified email at uni-tuebingen.de
