Maximilian Nickel
Maximilian Nickel
Research Scientist at Facebook AI Research
Zweryfikowany adres z fb.com - Strona główna
Cytowane przez
Cytowane przez
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
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
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2013
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
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
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
Learning neural event functions for ordinary differential equations
RTQ Chen, B Amos, M Nickel
arXiv preprint arXiv:2011.03902, 2020
Logistic tensor factorization for multi-relational data
M Nickel, V Tresp
arXiv preprint arXiv:1306.2084, 2013
Riemannian continuous normalizing flows
E Mathieu, M Nickel
Advances in Neural Information Processing Systems 33, 2503-2515, 2020
Non-negative tensor factorization with rescal
D Krompaß, M Nickel, X Jiang, V Tresp
Tensor Methods for Machine Learning, ECML workshop, 1-10, 2013
Poincaré maps for analyzing complex hierarchies in single-cell data
A Klimovskaia, D Lopez-Paz, L Bottou, M Nickel
Nature communications 11 (1), 1-9, 2020
Fast linear model for knowledge graph embeddings
A Joulin, E Grave, P Bojanowski, M Nickel, T Mikolov
arXiv preprint arXiv:1710.10881, 2017
Complex and holographic embeddings of knowledge graphs: a comparison
T Trouillon, M Nickel
arXiv preprint arXiv:1707.01475, 2017
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