| Automated detection of conifer seedlings in drone imagery using convolutional neural networks M Fromm, M Schubert, G Castilla, J Linke, G McDermid Remote Sensing 11 (21), 2585, 2019 | 95 | 2019 |
| TACAM: topic and context aware argument mining M Fromm, E Faerman, T Seidl IEEE/WIC/ACM International Conference on Web Intelligence, 99-106, 2019 | 31 | 2019 |
| Argument Mining Driven Analysis of Peer-Reviews M Fromm, E Faerman, M Berrendorf, S Bhargava, R Qi, Y Zhang, ... Proceedings of the AAAI Conference on Artificial Intelligence 35 (1), 4758-4766, 2021 | 28 | 2021 |
| Unsupervised anomaly detection for X-ray images D Davletshina, V Melnychuk, V Tran, H Singla, M Berrendorf, E Faerman, ... arXiv preprint arXiv:2001.10883, 2020 | 26 | 2020 |
| Relational and Fine-Grained Argument Mining: The LMU Munich project ReMLAV within the DFG Priority Program RATIO “Robust Argumentation Machines” D Trautmann, M Fromm, V Tresp, T Seidl, H Schütze Datenbank-Spektrum 20, 99-105, 2020 | 14 | 2020 |
| Tokenizer Choice For LLM Training: Negligible or Crucial? M Ali, M Fromm, K Thellmann, R Rutmann, M Lübbering, J Leveling, ... arXiv preprint arXiv:2310.08754, 2023 | 11 | 2023 |
| Cross-domain argument quality estimation M Fromm, M Berrendorf, E Faerman, T Seidl Findings of the Association for Computational Linguistics: ACL 2023, 13435-13448, 2023 | 8* | 2023 |
| Using supervised learning to classify metadata of research data by field of study T Weber, D Kranzlmüller, M Fromm, NT de Sousa Quantitative Science Studies 1 (2), 525-550, 2020 | 8 | 2020 |
| Active Learning for Argument Strength Estimation N Kees, M Fromm, E Faerman, T Seidl Proceedings of the Second Workshop on Insights from Negative Results in NLP …, 2021 | 5 | 2021 |
| Diversity Aware Relevance Learning for Argument Search F Michael, B Max, O Sandra, T Seidl, F Evgeniy EUROPEAN CONFERENCE ON INFORMATION RETRIEVAL 43, 264–271, 2021 | 5* | 2021 |
| XD-STOD: Cross-Domain Superresolution for Tiny Object Detection M Fromm, M Berrendorf, E Faerman, Y Chen, B Schüss, M Schubert 2019 International Conference on Data Mining Workshops (ICDMW), 142-148, 2019 | 3 | 2019 |
| Conifer Seedling Detection in UAV-Imagery with RGB-Depth Information J Jooste, M Fromm, M Schubert arXiv preprint arXiv:2111.11388, 2021 | 1 | 2021 |
| Spatial Interpolation with Message Passing Framework E Faerman, M Rogalla, N Strauß, A Krüger, B Blümel, M Berrendorf, ... 2019 International Conference on Data Mining Workshops (ICDMW), 135-141, 2019 | 1 | 2019 |
| Do Multilingual Large Language Models Mitigate Stereotype Bias? S Nie, M Fromm, C Welch, R Görge, A Karimi, J Plepi, NA Mowmita, ... arXiv preprint arXiv:2407.05740, 2024 | | 2024 |
| Tokenizer Choice For LLM Training: Negligible or Crucial? Findings of the Association for Computational Linguistics: NAACL 2024, 3907–3924, 2024 | | 2024 |
| Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions? AA Weber, K Thellmann, J Ebert, N Flores-Herr, J Lehmann, M Fromm, ... arXiv preprint arXiv:2402.13703, 2024 | | 2024 |
| Machine learning driven argument mining M Fromm lmu, 2022 | | 2022 |
Evgeniy FaermanPhD Student, Ludwig-Maximilians-Universität MünchenVerified email at dbs.ifi.lmu.de
