| Determining the Relevance of Features for Deep Neural Networks C Reimers, J Runge, J Denzler Proceedings of the European Conference on Computer Vision (ECCV) 1, 2020 | 20 | 2020 |
| Conditional dependence tests reveal the usage of ABCD rule features and bias variables in automatic skin lesion classification C Reimers, N Penzel, P Bodesheim, J Runge, J Denzler Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 16 | 2021 |
| Deep learning–an opportunity and a challenge for geo-and astrophysics C Reimers, C Requena-Mesa Knowledge discovery in big data from astronomy and earth observation, 251-265, 2020 | 16 | 2020 |
| Conditional adversarial debiasing: Towards learning unbiased classifiers from biased data C Reimers, P Bodesheim, J Runge, J Denzler DAGM German Conference on Pattern Recognition, 48-62, 2021 | 13* | 2021 |
| Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning R ElGhawi, B Kraft, C Reimers, M Reichstein, M Körner, P Gentine, ... Environmental Research Letters 18 (3), 034039, 2023 | 9 | 2023 |
| Spatio‐temporal Autoencoders in Weather and Climate Research XA Tibau, C Reimers, C Requena‐Mesa, J Runge Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote …, 2021 | 9 | 2021 |
| A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections XA Tibau, C Reimers, A Gerhardus, J Denzler, V Eyring, J Runge Environmental Data Science 1, e12, 2022 | 7 | 2022 |
| Learning disentangled discrete representations D Friede, C Reimers, H Stuckenschmidt, M Niepert Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 4 | 2023 |
| SupernoVAE: VAE based Kernel-PCA for analysis of spatio-temporal earth data XA Tibau, C Requena-Mesa, C Reimers, J Denzler, V Eyring, ... Proceedinfs of the 8th international workshop on climate informatics: CI …, 2018 | 4 | 2018 |
| Investigating neural network training on a feature level using conditional independence N Penzel, C Reimers, P Bodesheim, J Denzler European Conference on Computer Vision, 383-399, 2022 | 3 | 2022 |
| Using causal inference to globally understand black box predictors beyond saliency maps C Reimers, J Runge, J Denzler Proceedings of the 9th International Workshop on Climate Informatics: CI …, 2019 | 3 | 2019 |
| Investigating the Consistency of Uncertainty Sampling in Deep Active Learning N Penzel, C Reimers, CA Brust, J Denzler DAGM German Conference on Pattern Recognition, 159-173, 2021 | | 2021 |
| Spatiotemporal model for benchmarking causal discovery algorithms XA Tibau, C Reimers, V Eyring, J Denzler, M Reichstein, J Runge EGU2020, 2020 | | 2020 |
| Toy models to analyze emergent constraint approaches. XA Tibau, C Reimers, V Eyring, J Denzler, M Reichstein, J Runge Geophysical Research Abstracts 21, 2019 | | 2019 |
| SupernoVAE: Using deep learning to find spatio-temporal dynamics in Earth system data XA Tibau, C Requena-Mesa, C Reimers, J Denzler, V Eyring, ... AGU Fall Meeting 2018, 2018 | | 2018 |
Joachim DenzlerProfessor für Informatik, Friedrich-Schiller University JenaDirección de correo verificada de uni-jena.de
