13 citations as recorded by crossref.

1. Machine Learning Emulation of 3D Cloud Radiative Effects D. Meyer et al. 10.1029/2021MS002550
2. Machine Learning Emulation of Urban Land Surface Processes D. Meyer et al. 10.1029/2021MS002744
3. Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook P. Dueben et al. 10.1175/AIES-D-21-0002.1
4. Statistical mechanics in climate emulation: Challenges and perspectives I. Sudakow et al. 10.1017/eds.2022.15
5. Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images L. Wang et al. 10.1016/j.geoderma.2022.116321
6. Synthia: multidimensional synthetic data generation in Python D. Meyer & T. Nagler 10.21105/joss.02863
7. Improving Predictions of Technical Inefficiency R. James et al. 10.2139/ssrn.4028125
8. Yet Another Discriminant Analysis (YADA): A Probabilistic Model for Machine Learning Applications R. Field et al. 10.3390/math12213392
9. Improving Weather Forecasts for Sailing Events Using a Combination of a Numerical Forecast Model and Machine Learning Postprocessing S. Beimel et al. 10.3390/app14072950
10. Soybean yield prediction using machine learning algorithms under a cover crop management system L. Santos et al. 10.1016/j.atech.2024.100442
11. Synthetic data generation using Copula model and driving behavior analysis E. Savran & F. Karpat 10.1016/j.asej.2024.103060
12. Generative Adversarial Networks for Synthetic Data Generation in Finance: Evaluating Statistical Similarities and Quality Assessment F. Ramzan et al. 10.3390/ai5020035
13. Comparison of tabular synthetic data generation techniques using propensity and cluster log metric A. Pathare et al. 10.1016/j.jjimei.2023.100177