Maximilian Nickel
Maximilian Nickel
Research Scientist at FAIR, Meta AI
Verified email at - Homepage
Cited by
Cited by
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
A Review of Relational Machine Learning for Knowledge Graphs
M Nickel, K Murphy, V Tresp, E Gabrilovich
arXiv preprint arXiv:1503.00759, 2015
Holographic embeddings of knowledge graphs
M Nickel, L Rosasco, T Poggio
Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016
Poincaré Embeddings for Learning Hierarchical Representations
M Nickel, D Kiela
arXiv preprint arXiv:1705.08039, 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
Learning continuous hierarchies in the lorentz model of hyperbolic geometry
M Nickel, D Kiela
International conference on machine learning, 3779-3788, 2018
Hyperbolic graph neural networks
Q Liu, M Nickel, D Kiela
Advances in neural information processing systems 32, 2019
Flow matching for generative modeling
Y Lipman, RTQ Chen, H Ben-Hamu, M Nickel, M Le
arXiv preprint arXiv:2210.02747, 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
Hearst patterns revisited: Automatic hypernym detection from large text corpora
S Roller, D Kiela, M Nickel
arXiv preprint arXiv:1806.03191, 2018
Tensor factorization for multi-relational learning
M Nickel, V Tresp
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013
Learning neural event functions for ordinary differential equations
RTQ Chen, B Amos, M Nickel
arXiv preprint arXiv:2011.03902, 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
Riemannian continuous normalizing flows
E Mathieu, M Nickel
Advances in Neural Information Processing Systems 33, 2503-2515, 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
Neural spatio-temporal point processes
RTQ Chen, B Amos, M Nickel
arXiv preprint arXiv:2011.04583, 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
Learning visually grounded sentence representations
D Kiela, A Conneau, A Jabri, M Nickel
arXiv preprint arXiv:1707.06320, 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
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
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