| 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 (6), 4758-4766, 2021 | 28 | 2021 |
| Lasagne: Locality and structure aware graph node embedding E Faerman, F Borutta, K Fountoulakis, MW Mahoney 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 246-253, 2018 | 27 | 2018 |
| 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 |
| Active learning for entity alignment M Berrendorf, E Faerman, V Tresp Advances in Information Retrieval: 43rd European Conference on IR Research …, 2021 | 25 | 2021 |
| Knowledge graph entity alignment with graph convolutional networks: lessons learned M Berrendorf, E Faerman, V Melnychuk, V Tresp, T Seidl Advances in Information Retrieval: 42nd European Conference on IR Research …, 2020 | 25 | 2020 |
| On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods M Berrendorf, E Faerman, L Vermue, V Tresp arXiv preprint arXiv:2002.06914, 2020 | 17 | 2020 |
| Interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank M Berrendorf, E Faerman, L Vermue, V Tresp arXiv preprint arXiv:2002.06914, 2020 | 15 | 2020 |
| Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods M Berrendorf, E Faerman, L Vermue, V Tresp 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and …, 2020 | 12* | 2020 |
| Graph Alignment Networks with Node Matching Scores E Faerman, O Voggenreiter, F Borutta, T Emrich, M Berrendorf, ... Proceedings of Advances in Neural Information Processing Systems (NIPS), 2019 | 10 | 2019 |
| A critical assessment of state-of-the-art in entity alignment M Berrendorf, L Wacker, E Faerman Advances in Information Retrieval: 43rd European Conference on IR Research …, 2021 | 8 | 2021 |
| Prediction of soft proton intensities in the near-Earth space using machine learning EA Kronberg, T Hannan, J Huthmacher, M Münzer, F Peste, Z Zhou, ... The Astrophysical Journal 921 (1), 76, 2021 | 7 | 2021 |
| Towards a Holistic View on Argument Quality Prediction M Fromm, M Berrendorf, J Reiml, I Mayerhofer, S Bhargava, E Faerman, ... arXiv preprint arXiv:2205.09803, 2022 | 6 | 2022 |
| Structural Graph Representations based on Multiscale Local Network Topologies F Borutta, J Busch, E Faerman, A Klink, M Schubert IEEE/WIC/ACM International Conference on Web Intelligence, 91-98, 2019 | 6 | 2019 |
| Semi-Supervised Learning on Graphs Based on Local Label Distributions E Faerman, F Borutta, J Busch, M Schubert arXiv preprint arXiv:1802.05563, 2018 | 6 | 2018 |
| Active Learning for Argument Strength Estimation N Kees, M Fromm, E Faerman, T Seidl arXiv preprint arXiv:2109.11319, 2021 | 5 | 2021 |
| Diversity Aware Relevance Learning for Argument Search M Fromm, M Berrendorf, S Obermeier, T Seidl, E Faerman Advances in Information Retrieval: 43rd European Conference on IR Research …, 2021 | 5 | 2021 |
| Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering J Busch, E Faerman, M Schubert, T Seidl arXiv preprint arXiv:2009.12875, 2020 | 5 | 2020 |
| 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 |
| 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 | 2 | 2023 |
