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Results: 12
Number of items: 12
  • Open Access
    Younesian, T., Daza, D., van Krieken, E., Thanapalasingam, T., & Bloem, P. (2025). GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks. Transactions on Machine Learning Research, 2025, Article 3923. https://doi.org/10.48550/arXiv.2310.03399
  • Thanapalasingam, T., van Krieken, E., Bloem, P., & Groth, P. (2023, April). IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14787483
  • Thanapalasingam, T., van Krieken, E., Bloem, P., & Groth, P. (2023, April 13). IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8039857
  • Open Access
    Thanapalasingam, T., van Berkel, L., Bloem, P., & Groth, P. (2022). Relational graph convolutional networks: a closer look. PeerJ Computer Science, 8, Article e1073. https://doi.org/10.7717/PEERJ-CS.1073
  • Open Access
    Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018). Modeling Relational Data with Graph Convolutional Networks. In A. Gangemi, R. Navigli, M.-E. Vidal, P. Hitzler, R. Troncy, L. Hollink, A. Tordai, & M. Alam (Eds.), The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018 : proceedings (pp. 593-607). (Lecture Notes in Computer Science; Vol. 10843). Springer. https://doi.org/10.1007/978-3-319-93417-4_38
  • Open Access
    Bloem, P. (2016). Single sample statistics: Exercises in learning from just one example. [Thesis, fully internal, Universiteit van Amsterdam].
  • Bloem, P., de Rooij, S., & Adriaans, P. (2015). Two problems for sophistication. In K. Chaudhuri, C. Gentile, & S. Zilles (Eds.), Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015 : proceedings (pp. 379-394). (Lecture Notes in Computer Science; Vol. 9355), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-319-24486-0_25
  • Wibisono, A., Bloem, P., de Vries, G. K. D., Groth, P., Belloum, A., & Bubak, M. (2015). Generating scientific documentation for computational experiments using provenance. In B. Ludäscher, & B. Plale (Eds.), Provenance and Annotation of Data and Processes: 5th International Provenance and Annotation Workshop, IPAW 2014, Cologne, Germany, June 9-13, 2014 : revised selected papers (pp. 168-179). (Lecture Notes in Computer Science; Vol. 8628). Springer. https://doi.org/10.1007/978-3-319-16462-5_13
  • Open Access
    Bloem, P., & de Vries, G. K. D. (2014). Machine Learning on Linked Data, a Position Paper. In I. Tiddi, M. d'Aquin, & N. Jay (Eds.), Proceedings of the 1st Workshop on Linked Data for Knowledge Discovery: co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2014) : Nancy, France, September 19th, 2014 (pp. 69-73). (CEUR Workshop Proceedings; Vol. 1232). CEUR-WS. http://ceur-ws.org/Vol-1232/paper7.pdf
  • Open Access
    Bloem, P., Mota, F., de Rooij, S., Antunes, L., & Adriaans, P. (2014). A safe approximation for Kolmogorov complexity. In P. Auer, A. Clark, T. Zeugman, & S. Zilles (Eds.), Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014 : proceedings (pp. 336-350). (Lecture Notes in Computer Science; Vol. 8776), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-319-11662-4_24
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