| A benchmark for data imputation methods S Jäger, A Allhorn, F Bießmann Frontiers in big Data 4, 693674, 2021 | 174 | 2021 |
| Nudging sustainable consumption: A large-scale data analysis of sustainability labels for fashion in German online retail M Gossen, S Jäger, ML Hoffmann, F Bießmann, R Korenke, T Santarius Frontiers in Sustainability 3, 922984, 2022 | 17 | 2022 |
| Parallelized training of deep nn: comparison of current concepts and frameworks S Jäger, HP Zorn, S Igel, C Zirpins Proceedings of the Second Workshop on Distributed Infrastructures for Deep …, 2018 | 10 | 2018 |
| GreenDB: Toward a Product-by-Product Sustainability Database S Jäger, J Greene, M Jakob, R Korenke, T Santarius, F Biessmann arXiv preprint arXiv:2205.02908, 2022 | 4 | 2022 |
| Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data A Flick, S Jäger, I Trajanovska, F Biessmann European Conference on Information Retrieval, 230-235, 2023 | 1 | 2023 |
| From Data Imputation to Data Cleaning—Automated Cleaning of Tabular Data Improves Downstream Predictive Performance S Jäger, F Biessmann International Conference on Artificial Intelligence and Statistics, 3394-3402, 2024 | | 2024 |
| GreenDB--A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods S Jäger, A Flick, JAS Garcia, K Driesch, K Brendel, F Biessmann arXiv preprint arXiv:2207.10733, 2022 | | 2022 |
| Compressing BERT–An Evaluation and Combination of Methods S Jäger | | |
| Horizontales Skalieren von Deep Learning Frameworks S Jäger | | |
Felix BiessmannEinstein Center Digital Future, Berlin University of Applied SciencesVerified email at bht-berlin.de
