| Modeling relational data with graph convolutional networks M Schlichtkrull, TN Kipf, P Bloem, M Berg, R. vd, Titov, I., and Welling European Semantic Web Conference, 593-607, 2018 | 801 | 2018 |
| The knowledge graph as the default data model for learning on heterogeneous knowledge X Wilcke, P Bloem, V De Boer Data Science 1 (1-2), 39-57, 2017 | 34 | 2017 |
| The MIDI linked data cloud A Meroño-Peñuela, R Hoekstra, A Gangemi, P Bloem, R de Valk, ... International Semantic Web Conference, 156-164, 2017 | 13 | 2017 |
| Machine learning on linked data, a position paper P Bloem, GKD De Vries Linked Data for Knowledge Discovery, 69, 2014 | 11 | 2014 |
| Are names meaningful? Quantifying social meaning on the semantic web S de Rooij, W Beek, P Bloem, F van Harmelen, S Schlobach International Semantic Web Conference, 184-199, 2016 | 10 | 2016 |
| Simplifying RDF Data for Graph-Based Machine Learning. P Bloem, A Wibisono, G De Vries KNOW@ LOD 1243, 2014 | 10 | 2014 |
| A safe approximation for Kolmogorov complexity P Bloem, F Mota, S de Rooij, L Antunes, P Adriaans International Conference on Algorithmic Learning Theory, 336-350, 2014 | 9 | 2014 |
| Large-scale network motif learning with compression P Bloem, S de Rooij CoRR arXiv 1701, 2017 | 6* | 2017 |
| Two problems for sophistication P Bloem, S de Rooij, P Adriaans International Conference on Algorithmic Learning Theory, 379-394, 2015 | 6 | 2015 |
| Generating scientific documentation for computational experiments using provenance A Wibisono, P Bloem, GKD de Vries, P Groth, A Belloum, M Bubak International Provenance and Annotation Workshop, 168-179, 2014 | 4 | 2014 |
| Deep Learning for Classification Tasks on Geospatial Vector Polygons R van't Veer, P Bloem, E Folmer arXiv, arXiv: 1806.03857, 2018 | 3 | 2018 |
| A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study A El-Gazzar, M Quaak, L Cerliani, P Bloem, G van Wingen, RM Thomas OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical …, 2019 | 2 | 2019 |
| Single sample statistics: exercises in learning from just one example P Bloem University of Amsterdam, 2016 | 2 | 2016 |
| End-to-End Learning from Complex Multigraphs with Latent Graph Convolutional Networks F Hermsen, P Bloem, F Jansen, W Vos arXiv preprint arXiv:1908.05365, 2019 | 1 | 2019 |
| Exploiting Temporality for Semi-Supervised Video Segmentation R Sibechi, O Booij, N Baka, P Bloem Proceedings of the IEEE International Conference on Computer Vision …, 2019 | 1 | 2019 |
| Three tools for practical differential privacy KL van der Veen, R Seggers, P Bloem, G Patrini arXiv preprint arXiv:1812.02890, 2018 | 1 | 2018 |
| A tutorial on MDL hypothesis testing for graph analysis P Bloem, S de Rooij arXiv preprint arXiv:1810.13163, 2018 | 1 | 2018 |
| Deep Learning for Classification Tasks on Geospatial Vector Polygons R Veer, P Bloem, E Folmer arXiv preprint arXiv:1806.03857, 2018 | 1 | 2018 |
| End-to-End Entity Classification on Multimodal Knowledge Graphs WX Wilcke, P Bloem, V de Boer, RH van t Veer, FAH van Harmelen arXiv, arXiv: 2003.12383, 2020 | | 2020 |
| SUBMASSIVE: Resolving Subclass Cycles in Very Large Knowledge Graphs S Wang, P Bloem, J Raad, F van Harmelen Workshop on Large Scale RDF Analytics (LASCAR), 2020 | | 2020 |
Ivan TitovUniversity of Edinburgh / University of AmsterdamVerified email at inf.ed.ac.uk
