| Pykeen 1.0: A python library for training and evaluating knowledge graph embeddings M Ali, M Berrendorf, CT Hoyt, L Vermue, S Sharifzadeh, V Tresp, ... Journal of Machine Learning Research 22, 1-6, 2021 | 306 | 2021 |
| Bringing light into the dark: A large-scale evaluation of knowledge graph embedding models under a unified framework M Ali, M Berrendorf, CT Hoyt, L Vermue, M Galkin, S Sharifzadeh, ... IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (12), 8825 …, 2021 | 220 | 2021 |
| Medium energy electron flux in earth's outer radiation belt (MERLIN): A machine learning model AG Smirnov, M Berrendorf, YY Shprits, EA Kronberg, HJ Allison, ... Space Weather 18 (11), e2020SW002532, 2020 | 67 | 2020 |
| Improving inductive link prediction using hyper-relational facts M Ali, M Berrendorf, M Galkin, V Thost, T Ma, V Tresp, J Lehmann International Semantic Web Conference, 74-92, 2021 | 47 | 2021 |
| Query Embedding on Hyper-relational Knowledge Graphs D Alivanistos, M Berrendorf, M Cochez, M Galkin International Conference on Learning Representations (ICLR), 2022 | 43 | 2022 |
| 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 6 (35), 4758-4766, 2020 | 43 | 2020 |
| A novel neural network model of Earth’s topside ionosphere A Smirnov, Y Shprits, F Prol, H Lühr, M Berrendorf, I Zhelavskaya, C Xiong Scientific Reports 13 (1), 1303, 2023 | 39 | 2023 |
| An open challenge for inductive link prediction on knowledge graphs M Galkin, M Berrendorf, CT Hoyt arXiv preprint arXiv:2203.01520, 2022 | 39 | 2022 |
| 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 | 36 | 2020 |
| A unified framework for rank-based evaluation metrics for link prediction in knowledge graphs CT Hoyt, M Berrendorf, M Galkin, V Tresp, BM Gyori arXiv preprint arXiv:2203.07544, 2022 | 32 | 2022 |
| Improving visual relation detection using depth maps S Sharifzadeh, SM Baharlou, M Berrendorf, R Koner, V Tresp 2020 25th International Conference on Pattern Recognition (ICPR), 3597-3604, 2021 | 31 | 2021 |
| Knowledge graph entity alignment with graph convolutional networks: Lessons learned M Berrendorf, E Faerman, V Melnychuk, V Tresp, T Seidl European Conference on Information Retrieval, 3-11, 2020 | 31 | 2020 |
| Active learning for entity alignment M Berrendorf, E Faerman, V Tresp 43rd European Conference on Information Retrieval (ECIR-21), 2021 | 29 | 2021 |
| 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 | 29* | 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:2002.06914v3, 2020 | 21 | 2020 |
| Prediction and understanding of soft-proton contamination in XMM-newton: a machine learning approach EA Kronberg, F Gastaldello, S Haaland, A Smirnov, M Berrendorf, ... The Astrophysical Journal 903 (2), 89, 2020 | 17 | 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) 2, 2019 | 13 | 2019 |
| k-distance approximation for memory-efficient RkNN retrieval M Berrendorf, F Borutta, P Kröger International Conference on Similarity Search and Applications, 57-71, 2019 | 11 | 2019 |
| 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 | 10 | 2021 |
| A critical assessment of state-of-the-art in entity alignment M Berrendorf, L Wacker, E Faerman European conference on information retrieval, 18-32, 2021 | 10 | 2021 |
Volker TrespLudwig-Maximilians-Universität München (LMU Munich)Verified email at dbs.ifi.lmu.de
