83 citations · 205 across the 16 of their papers we have counts for
17 papers
SUBMASSIVE: Resolving Subclass Cycles in Very Large Knowledge Graphs
Shuai Wang, Peter Bloem, Joe Raad +1
Large knowledge graphs capture information of a large number of entities and their relations. Among the many relations they capture, class subsumption assertions are usually presen…
End-to-End Learning on Multimodal Knowledge Graphs
W. X. Wilcke, P. Bloem, V. de Boer +1
Knowledge graphs enable data scientists to learn end-to-end on heterogeneous knowledge. However, most end-to-end models solely learn from the relational information encoded in grap…
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation
Thiviyan Thanapalasingam, Emile van Krieken, Peter Bloem +1
Knowledge Graph Embedding (KGE) models are used to learn continuous representations of entities and relations. A key task in the literature is predicting missing links between enti…
Relational Graph Convolutional Networks: A Closer Look
Thiviyan Thanapalasingam, Lucas van Berkel, Peter Bloem +1
In this paper, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model. Our reproduction…
Finding Motifs in Knowledge Graphs using Compression
Peter Bloem
We introduce a method to find network motifs in knowledge graphs. Network motifs are useful patterns or meaningful subunits of the graph that recur frequently. We extend the common…
Uncertainty Intervals for Graph-based Spatio-Temporal Traffic Prediction
Tijs Maas, Peter Bloem
Many traffic prediction applications rely on uncertainty estimates instead of the mean prediction. Statistical traffic prediction literature has a complete subfield devoted to unce…
End-to-End Entity Classification on Multimodal Knowledge Graphs
W. X. Wilcke, P. Bloem, V. de Boer +2
End-to-end multimodal learning on knowledge graphs has been left largely unaddressed. Instead, most end-to-end models such as message passing networks learn solely from the relatio…
A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study
Ahmed El-Gazzar, Mirjam Quaak, Leonardo Cerliani +3
Functional Magnetic Resonance Imaging (fMRI) captures the temporal dynamics of neural activity as a function of spatial location in the brain. Thus, fMRI scans are represented as 4…
Exploiting Temporality for Semi-Supervised Video Segmentation
Radu Sibechi, Olaf Booij, Nora Baka +1
In recent years, there has been remarkable progress in supervised image segmentation. Video segmentation is less explored, despite the temporal dimension being highly informative.…
End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks
Floris Hermsen, Peter Bloem, Fabian Jansen +1
We study the problem of end-to-end learning from complex multigraphs with potentially very large numbers of edges between two vertices, each edge labeled with rich information. Exa…
Three Tools for Practical Differential Privacy
Koen Lennart van der Veen, Ruben Seggers, Peter Bloem +1
Differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on…
A tutorial on MDL hypothesis testing for graph analysis
Peter Bloem, Steven de Rooij
This document provides a tutorial description of the use of the MDL principle in complex graph analysis. We give a brief summary of the preliminary subjects, and describe the basic…
Learning sparse transformations through backpropagation
Peter Bloem
Many transformations in deep learning architectures are sparsely connected. When such transformations cannot be designed by hand, they can be learned, even through plain backpropag…
Deep Learning for Classification Tasks on Geospatial Vector Polygons
Rein van 't Veer, Peter Bloem, Erwin Folmer
In this paper, we evaluate the accuracy of deep learning approaches on geospatial vector geometry classification tasks. The purpose of this evaluation is to investigate the ability…
An Expectation-Maximization Algorithm for the Fractal Inverse Problem
Peter Bloem, Steven de Rooij
We present an Expectation-Maximization algorithm for the fractal inverse problem: the problem of fitting a fractal model to data. In our setting the fractals are Iterated Function…
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem +3
Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance,…
Large-scale network motif analysis using compression
Peter Bloem, Steven de Rooij
We introduce a new method for finding network motifs: interesting or informative subgraph patterns in a network. Subgraphs are motifs when their frequency in the data is high compa…