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 | 2820 | 2011 |
A Review of Relational Machine Learning for Knowledge Graphs M Nickel, K Murphy, V Tresp, E Gabrilovich arXiv preprint arXiv:1503.00759, 2015 | 1931 | 2015 |
Holographic embeddings of knowledge graphs M Nickel, L Rosasco, T Poggio Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 1435 | 2016 |
Poincaré Embeddings for Learning Hierarchical Representations M Nickel, D Kiela arXiv preprint arXiv:1705.08039, 2017 | 1409 | 2017 |
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 | 531 | 2012 |
Learning continuous hierarchies in the lorentz model of hyperbolic geometry M Nickel, D Kiela International conference on machine learning, 3779-3788, 2018 | 460 | 2018 |
Hyperbolic graph neural networks Q Liu, M Nickel, D Kiela Advances in neural information processing systems 32, 2019 | 377 | 2019 |
Flow matching for generative modeling Y Lipman, RTQ Chen, H Ben-Hamu, M Nickel, M Le arXiv preprint arXiv:2210.02747, 2022 | 369 | 2022 |
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 | 164 | 2019 |
Hearst patterns revisited: Automatic hypernym detection from large text corpora S Roller, D Kiela, M Nickel arXiv preprint arXiv:1806.03191, 2018 | 157 | 2018 |
Tensor factorization for multi-relational learning M Nickel, V Tresp Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013 | 122 | 2013 |
Learning neural event functions for ordinary differential equations RTQ Chen, B Amos, M Nickel arXiv preprint arXiv:2011.03902, 2020 | 119 | 2020 |
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 | 112 | 2014 |
Riemannian continuous normalizing flows E Mathieu, M Nickel Advances in Neural Information Processing Systems 33, 2503-2515, 2020 | 107 | 2020 |
Neural spatio-temporal point processes RTQ Chen, B Amos, M Nickel arXiv preprint arXiv:2011.04583, 2020 | 94 | 2020 |
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 | 93 | 2022 |
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 | 93 | 2020 |
Learning visually grounded sentence representations D Kiela, A Conneau, A Jabri, M Nickel arXiv preprint arXiv:1707.06320, 2017 | 88 | 2017 |
Inferring concept hierarchies from text corpora via hyperbolic embeddings M Le, S Roller, L Papaxanthos, D Kiela, M Nickel arXiv preprint arXiv:1902.00913, 2019 | 75 | 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 | 60 | 2019 |