| Flow matching for generative modeling Y Lipman, RTQ Chen, H Ben-Hamu, M Nickel, M Le arXiv preprint arXiv:2210.02747, 2022 | 4023 | 2022 |
| A Three-Way Model for Collective Learning on Multi-Relational Data M Nickel, V Tresp, HP Kriegel Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 3438 | 2011 |
| A Review of Relational Machine Learning for Knowledge Graphs M Nickel, K Murphy, V Tresp, E Gabrilovich arXiv preprint arXiv:1503.00759, 2015 | 2330 | 2015 |
| Poincaré Embeddings for Learning Hierarchical Representations M Nickel, D Kiela arXiv preprint arXiv:1705.08039, 2017 | 2036 | 2017 |
| Holographic embeddings of knowledge graphs M Nickel, L Rosasco, T Poggio Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 1693 | 2016 |
| Learning continuous hierarchies in the lorentz model of hyperbolic geometry M Nickel, D Kiela International conference on machine learning, 3779-3788, 2018 | 698 | 2018 |
| Hyperbolic graph neural networks Q Liu, M Nickel, D Kiela Advances in neural information processing systems 32, 2019 | 592 | 2019 |
| Factorizing YAGO: Scalable Machine Learning for Linked Data M Nickel, V Tresp, HP Kriegel Proceedings of the 21st International Conference on World Wide Web, 271-280, 2012 | 560 | 2012 |
| Task-driven modular networks for zero-shot compositional learning S Purushwalkam, M Nickel, A Gupta, MA Ranzato Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 266 | 2019 |
| Revisiting the evaluation of theory of mind through question answering M Le, YL Boureau, M Nickel Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 186 | 2019 |
| Hearst patterns revisited: Automatic hypernym detection from large text corpora S Roller, D Kiela, M Nickel Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 185 | 2018 |
| The United States COVID-19 forecast hub dataset EY Cramer, Y Huang, Y Wang, EL Ray, M Cornell, J Bracher, A Brennen, ... Scientific data 9 (1), 462, 2022 | 180 | 2022 |
| Riemannian continuous normalizing flows E Mathieu, M Nickel Advances in neural information processing systems 33, 2503-2515, 2020 | 178 | 2020 |
| Hyperbolic image-text representations K Desai, M Nickel, T Rajpurohit, J Johnson, SR Vedantam International Conference on Machine Learning, 7694-7731, 2023 | 175 | 2023 |
| Learning neural event functions for ordinary differential equations RTQ Chen, B Amos, M Nickel arXiv preprint arXiv:2011.03902, 2020 | 173 | 2020 |
| Neural spatio-temporal point processes RTQ Chen, B Amos, M Nickel arXiv preprint arXiv:2011.04583, 2020 | 155 | 2020 |
| Poincaré maps for analyzing complex hierarchies in single-cell data A Klimovskaia, D Lopez-Paz, L Bottou, M Nickel Nature communications 11 (1), 2966, 2020 | 153 | 2020 |
| Tensor factorization for multi-relational learning M Nickel, V Tresp Joint European Conference on Machine Learning and Knowledge Discovery in …, 2013 | 135 | 2013 |
| Flow matching for generative modeling, 2023 Y Lipman, RTQ Chen, H Ben-Hamu, M Nickel, M Le URL https://arxiv. org/abs/2210.02747 2 (4), 6, 2023 | 131 | 2023 |
| Reducing the rank in relational factorization models by including observable patterns M Nickel, X Jiang, V Tresp Advances in Neural Information Processing Systems 27, 2014 | 118 | 2014 |
Volker TrespLudwig-Maximilians-Universität München (LMU Munich)Verified email at dbs.ifi.lmu.de
