Dr. David Greenberg
Dr. David Greenberg
Modellbasiertes Maschinelles Lernen
AbteilungsleiterTel: +49 (0) 4152 87-2133
- Ramesh, P., Lueckmann, J.-M., Boelts, J., Tejero-Cantero, A., Greenberg, D.S., Gonçalves, P.J., & Macke, J.H. (2022): GATSBI: Generative Adversarial Training for Simulation-Based Inference. arXivLabs, Machine Learning, doi:10.48550/arXiv.2203.06481
- Nonnenmacher, M., & Greenberg, D.S. (2021): Deep emulators for differentiation, forecasting, and parametrization in Earth science simulators. Journal of Advances in Modeling Earth Systems, 13, e2021MS002554, doi:10.1029/2021MS002554
- Tejero-Cantero, A.; Boelts, J.; Deistler, M.; Lueckmann, J.; Durkan, C.; Goncalves, P.; Greenberg, D.; Macke, J.: sbi: A toolkit for simulation-based inference. In: The Journal of Open Source Software. Vol. 5 (2020) 52, 2505, DOI: /10.21105/joss.0250
- Gonçalves, P. J., Lueckmann, J. M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G., Chintaluri, C., Podlaski, W. F., Haddad, S. A., Vogels, T. P., Greenberg, D. S., & Macke, J. H. (2020). Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife, 9, e56261. doi:10.7554/eLife.56261
- Jan-Matthis Lueckmann, Jan Boelts, David Greenberg, Pedro Goncalves, Jakob Macke: Benchmarking Simulation-Based Inference. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:343-351, 2021.