Publikationen von Christian Reimers

Liu, G.; Migliavacca, M.; Reimers, C.; Kraft, B.; Reichstein, M.; Richardson, A. D.; Wingate, L.; Delpierre, N.; Yang, H.; Winkler, A.: DeepPhenoMem V1.0: Deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology. Geoscientific Model Development 17 (17), S. 6683 - 6701 (2024)

Winkler, A.; Myneni, R.; Reimers, C.; Reichstein, M.; Brovkin, V.: Carbon system state determines warming potential of emissions. PLOS ONE 19 (8), e0306128 (2024)

Wutzler, T.; Reimers, C.; Ahrens, B.; Schrumpf, M.: Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)). Geoscientific Model Development 17 (7), S. 2705 - 2725 (2024)

Reimers, C.; Bodesheim, P.; Runge, J.; Denzler, J.: Conditional adversarial debiasing: Towards learning unbiased classifiers from biased data. In: Pattern Recognition. DAGM GCPR 2021. Lecture Notes in Computer Science, Bd. 13024, S. 48 - 62. Springer International Publishing, Cham (2021)

Friede, D.; Reimers, C.; Stuckenschmidt, H.; Niepert, M.: Learning disentangled discrete representations. Machine learning and knowledge discovery in databases: Research track. ECML PKDD 2023. Lecture Notes in Computer Science 14172, S. 593 - 609 (2023)

Benson, V.; Bastos, A.; Reimers, C.; Winkler, A.; Yang, F.; Reichstein, M.: Atmospheric transport modeling of CO₂ with neural networks. arXiv (2024)

Reimers, C.; Hafezi Rachti, D.; Liu, G.; Winkler, A.: Comparing data-driven and mechanistic models for predicting phenology in deciduous broadleaf forests. arXiv (2024)