| A large-scale evaluation of computational protein function prediction P Radivojac, WT Clark, TR Oron, AM Schnoes, T Wittkop, A Sokolov, ... Nature methods 10 (3), 221-227, 2013 | 1108 | 2013 |
| Performance, accuracy, and web server for evolutionary placement of short sequence reads under maximum likelihood SA Berger, D Krompass, A Stamatakis Systematic biology 60 (3), 291-302, 2011 | 522 | 2011 |
| Pruning rogue taxa improves phylogenetic accuracy: an efficient algorithm and webservice AJ Aberer, D Krompass, A Stamatakis Systematic biology 62 (1), 162-166, 2013 | 403 | 2013 |
| Tensor-train recurrent neural networks for video classification Y Yang, D Krompass, V Tresp International Conference on Machine Learning, 3891-3900, 2017 | 289 | 2017 |
| Type-constrained representation learning in knowledge graphs D Krompaß, S Baier, V Tresp The Semantic Web-ISWC 2015: 14th International Semantic Web Conference …, 2015 | 275 | 2015 |
| Homology-based inference sets the bar high for protein function prediction T Hamp, R Kassner, S Seemayer, E Vicedo, C Schaefer, D Achten, F Auer, ... BMC bioinformatics 14, 1-10, 2013 | 64 | 2013 |
| Few-shot one-class classification via meta-learning A Frikha, D Krompaß, HG Köpken, V Tresp Proceedings of the AAAI conference on artificial intelligence 35 (8), 7448-7456, 2021 | 58 | 2021 |
| Predicting sequences of clinical events by using a personalized temporal latent embedding model C Esteban, D Schmidt, D Krompaß, V Tresp 2015 International conference on healthcare informatics, 130-139, 2015 | 58 | 2015 |
| Predicting the co-evolution of event and knowledge graphs C Esteban, V Tresp, Y Yang, S Baier, D Krompaß 2016 19th International Conference on Information Fusion (FUSION), 98-105, 2016 | 56 | 2016 |
| Non-negative tensor factorization with rescal D Krompaß, M Nickel, X Jiang, V Tresp Tensor Methods for Machine Learning, ECML workshop, 1-10, 2013 | 50 | 2013 |
| Querying factorized probabilistic triple databases D Krompaß, M Nickel, V Tresp The Semantic Web–ISWC 2014: 13th International Semantic Web Conference, Riva …, 2014 | 38 | 2014 |
| Learning with memory embeddings V Tresp, C Esteban, Y Yang, S Baier, D Krompaß arXiv preprint arXiv:1511.07972, 2015 | 33 | 2015 |
| RogueNaRok: An efficient and exact algorithm for rogue taxon identification AJ Aberer, D Krompaß, A Stamatakis Heidelberg Institute for Theoretical Studies: Exelixis-RRDR-2011–10, 2011 | 32 | 2011 |
| Large-scale factorization of type-constrained multi-relational data D Krompaß, M Nickel, V Tresp 2014 International Conference on Data Science and Advanced Analytics (DSAA …, 2014 | 27 | 2014 |
| Exploiting latent embeddings of nominal clinical data for predicting hospital readmission D Krompaß, C Esteban, V Tresp, M Sedlmayr, T Ganslandt KI-Künstliche Intelligenz 29, 153-159, 2015 | 26 | 2015 |
| FRAug: Tackling federated learning with Non-IID features via representation augmentation H Chen, A Frikha, D Krompass, J Gu, V Tresp Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 25 | 2023 |
| Feddat: An approach for foundation model finetuning in multi-modal heterogeneous federated learning H Chen, Y Zhang, D Krompass, J Gu, V Tresp Proceedings of the AAAI Conference on Artificial Intelligence 38 (10), 11285 …, 2024 | 21 | 2024 |
| Ensemble solutions for link-prediction in knowledge graphs D Krompaß, V Tresp PKDD ECML 2nd Workshop on Linked Data for Knowledge Discovery, 2015 | 19 | 2015 |
| Towards data-free domain generalization A Frikha, H Chen, D Krompaß, T Runkler, V Tresp Asian Conference on Machine Learning, 327-342, 2023 | 15 | 2023 |
| Cl-crossvqa: A continual learning benchmark for cross-domain visual question answering Y Zhang, H Chen, A Frikha, Y Yang, D Krompass, G Zhang, J Gu, V Tresp arXiv preprint arXiv:2211.10567, 2022 | 10 | 2022 |