2 citations · 3 across the 4 of their papers we have counts for
7 papers · 1 filter
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…
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…