| Cross-validation strategy impacts the performance and interpretation of machine learning models L Sweet, C Müller, M Anand, J Zscheischler Artificial Intelligence for the Earth Systems 2 (4), e230026, 2023 | 17 | 2023 |
| Using interpretable machine learning to identify compound meteorological drivers of crop yield failure L Sweet, J Zscheischler EGU General Assembly Conference Abstracts, EGU22-5464, 2022 | 2 | 2022 |
| How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences S Jiang, L Sweet, G Blougouras, A Brenning, W Li, M Reichstein, ... Earth's Future 12 (7), e2024EF004540, 2024 | 1 | 2024 |
| Monitoring voltage measurements for a vehicle battery C Meißner, M Marenz, L Hopp, LB Sweet, PR Verheijen US Patent 11,653,127, 2023 | 1 | 2023 |
| Identifying compound weather drivers of forest biomass loss with generative deep learning M Anand, FJ Bohn, G Camps-Valls, R Fischer, A Huth, L Sweet, ... Environmental Data Science 3, e4, 2024 | | 2024 |
| Insights into weather-driven forest mortality with a cross-modal transformer M Anand, L Sweet, FJ Bohn, G Camps-Valls, R Fischer, A Huth, ... AGU Fall Meeting Abstracts 2023, B34B-07, 2023 | | 2023 |
| Model evaluation strategy impacts the interpretation and performance of machine learning models L Sweet, C Müller, M Anand, J Zscheischler EGU General Assembly Conference Abstracts, EGU-8479, 2023 | | 2023 |
| Identifying compound weather prototypes of forest mortality with β-VAE M Anand, F Bohn, L Sweet, G Camps-Valls, J Zscheischler EGU General Assembly Conference Abstracts, EGU-10219, 2023 | | 2023 |
Jakob ZscheischlerHelmholtz Centre for Environmental Research - UFZ在 ufz.de 的电子邮件经过验证
