Publications

These are some selected highlights. For an exhaustive list, please see my Google Scholar profile. They're much better at this sort of thing.

Stefan F. Schouten
Piek Vossen

Black box NLP 2022

Relational graph convolutional networks: a closer look

with

Thiviyan Thanapalasingam
Paul Groth

PeerJ

`kgbench`: A Collection of Knowledge Graph Datasets for Evaluating Relational and Multimodal Machine Learning

with

Xander Wilcke
Lucas van Berkel
Victor de Boer

ESWC

Large-Scale Network Motif Learning with Compression

with

S. de Rooij

ECML-PKDD 2020 2020

Three tools for practical differential privacy

with

Koen van der Veen
Ruben Seggers
Giorgio Patrini

PPML 2018 2018

Learning Sparse Transformations through Backpropagation

preprint available

An Expectation-Maximization Algorithm for the Fractal Inverse Problem

with

S. de Rooij

preprint available

Modeling Relational Data with Graph Convolutional Networks

with

M. Schlichtkrull
T. Kipf
R. van den Berg
I. Titov
M. Welling

ESWC 2018 (best student paper award)

The knowledge graph as the default data model for learning on heterogeneous knowledge

with

X. Wilcke
V. de Boer

2017 IOSPress Data Science

Are names meaningful? Quantifying social meaning on the semantic web

with

S. de Rooij
W. Beek
F. van Harmelen
S. Slobach

ISWC 2016 2016

Single sample statistics

PhD thesis University of Amsterdam

Two problems for sophistication

with

S. de Rooij
P. Adriaans

ALT 2015

A safe approximation for Kolmogorov complexity

with

F. Mota
S. de Rooij
L. Antunes
P. Adriaans

2014 ALT 2014

Machine learning and fractal geometry (MSc thesis)

2010 MSc thesis

Unraveling principal component analysis

Probing the representations of named entities in Transformer-based Language Models

Relational graph convolutional networks: a closer look

`kgbench`: A Collection of Knowledge Graph Datasets for Evaluating Relational and Multimodal Machine Learning

Large-Scale Network Motif Learning with Compression

Three tools for practical differential privacy

Learning Sparse Transformations through Backpropagation

An Expectation-Maximization Algorithm for the Fractal Inverse Problem

Modeling Relational Data with Graph Convolutional Networks

The knowledge graph as the default data model for learning on heterogeneous knowledge

Are names meaningful? Quantifying social meaning on the semantic web

Single sample statistics

Two problems for sophistication

A safe approximation for Kolmogorov complexity

Machine learning and fractal geometry (MSc thesis)

Publications

Unraveling principal component analysis

Probing the representations of named entities in Transformer-based Language Models

Relational graph convolutional networks: a closer look

kgbench: A Collection of Knowledge Graph Datasets for Evaluating Relational and Multimodal Machine Learning

Large-Scale Network Motif Learning with Compression

Three tools for practical differential privacy

Learning Sparse Transformations through Backpropagation

An Expectation-Maximization Algorithm for the Fractal Inverse Problem

Modeling Relational Data with Graph Convolutional Networks

The knowledge graph as the default data model for learning on heterogeneous knowledge

Are names meaningful? Quantifying social meaning on the semantic web

Single sample statistics

Two problems for sophistication

A safe approximation for Kolmogorov complexity

Machine learning and fractal geometry (MSc thesis)

`kgbench`: A Collection of Knowledge Graph Datasets for Evaluating Relational and Multimodal Machine Learning