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Lippert, F., Kranstauber, B., van Loon, E. E., & Forré, P. D. (in press). Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems. In Thirty-seventh Annual Conference on Neural Information Processing Systems (Advances in Neural Information Processing Systems (NeurIPS)). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2306.08445
2022
Lippert, F., Kranstauber, B., Forré, P. D., & van Loon, E. E. (2022). Learning to predict spatiotemporal movement dynamics from weather radar networks. Methods in Ecology and Evolution, 13(12), 2811-2826. Advance online publication. https://doi.org/10.1111/2041-210X.14007[details]
Lippert, F., Kranstauber, B., Forré, P. & van Loon, E. . (2022). Data from: Learning to predict spatio-temporal movement dynamics from static sensor networks. Zenodo. https://doi.org/10.5281/zenodo.6364941
Lippert, F., Kranstauber, B., Forré, P. & van Loon, E. . (2022). Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks. Zenodo. https://doi.org/10.5281/zenodo.6874789
2022
Lippert, F., Kranstauber, B., van Loon, E. E., & Forré, P. (2022). Physics-informed inference of aerial animal movements from weather radar data. Paper presented at Workshop AI for Science: Progress and Promises, New Orleans, Louisiana, United States. https://doi.org/10.48550/arXiv.2211.04539[details]
Lippert, F., Kranstauber, B., Forré, P. & van Loon, E. . (2022). Data from: Learning to predict spatio-temporal movement dynamics from static sensor networks. Zenodo. https://doi.org/10.5281/zenodo.6364941
Lippert, F., Kranstauber, B., Forré, P. & van Loon, E. . (2022). Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks. Zenodo. https://doi.org/10.5281/zenodo.6874789
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