Model-driven Machine Learning
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Publications

2022

Ramesh, P., Lueckmann, J.-M., Boelts, J., Tejero-Cantero, A., Greenberg, D.S., Gonçalves, P.J., & Macke, J.H. GATSBI: Generative Adversarial Training for Simulation-Based Inference. International Conference on Learning Representations.

2021

Nonnenmacher, Marcel, and David S. Greenberg. Learning Implicit PDE Integration with Linear Implicit Layers. The Symbiosis of Deep Learning and Differential Equations, Conference on Neural Information Processing Systems.

Nonnenmacher, M., & Greenberg, D.S. Deep emulators for differentiation, forecasting, and parametrization in Earth science simulators. Journal of Advances in Modeling Earth Systems.

Lueckmann, Jan-Matthis, Boelts, Jan, Greenberg, David, Goncalves, Pedro, Macke, Jakob. Benchmarking Simulation-Based Inference. International Conference on Artificial Intelligence and Statistics.

Paasche, Hendrik, Gross, Matthias, Lüttgau, Jakob, Greenberg, David, Weigel. To the brave scientists: Aren’t we strong enough to stand (and profit from) uncertainty in Earth system measurement and modelling? Geoscience Data Journal.

2020

Tejero-Cantero, A.; Boelts, J.; Deistler, M.; Lueckmann, J.; Durkan, C.; Goncalves, P.; Greenberg, D.; Macke, J. sbi: A toolkit for simulation-based inference. The Journal of Open Source Software.

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.