no code implementations • 29 Apr 2024 • Stefan F. Schouten, Peter Bloem, Ilia Markov, Piek Vossen
Recent work has demonstrated that the latent spaces of large language models (LLMs) contain directions predictive of the truth of sentences.
1 code implementation • 23 Oct 2023 • Stefan F. Schouten, Peter Bloem, Ilia Markov, Piek Vossen
But no resources exist to evaluate how well Large Language Models can use explicit reasoning to resolve ambiguity in language.
1 code implementation • 5 Oct 2023 • Taraneh Younesian, Daniel Daza, Emile van Krieken, Thiviyan Thanapalasingam, Peter Bloem
To this end, we introduce GRAPES, an adaptive sampling method that learns to identify the set of nodes crucial for training a GNN.
1 code implementation • 13 Jul 2023 • Thiviyan Thanapalasingam, Emile van Krieken, Peter Bloem, Paul Groth
However, Knowledge Graphs are not just sets of links but also have semantics underlying their structure.
1 code implementation • 21 Jul 2021 • Thiviyan Thanapalasingam, Lucas van Berkel, Peter Bloem, Paul Groth
In this paper, we describe a reproduction of the Relational Graph Convolutional Network (RGCN).
1 code implementation • 16 Apr 2021 • Peter Bloem
We introduce a method to find network motifs in knowledge graphs.
no code implementations • 9 Dec 2020 • Tijs Maas, Peter Bloem
Many traffic prediction applications rely on uncertainty estimates instead of the mean prediction.
no code implementations • 14 Feb 2020 • Ahmed El-Gazzar, Mirjam Quaak, Leonardo Cerliani, Peter Bloem, Guido van Wingen, Rajat Mani Thomas
Functional Magnetic Resonance Imaging (fMRI) captures the temporal dynamics of neural activity as a function of spatial location in the brain.
1 code implementation • 29 Aug 2019 • Radu Sibechi, Olaf Booij, Nora Baka, Peter Bloem
In this paper, we tackle the issue of label scarcity by using consecutive frames of a video, where only one frame is annotated.
1 code implementation • 14 Aug 2019 • Floris Hermsen, Peter Bloem, Fabian Jansen, Wolf Vos
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.
no code implementations • 7 Dec 2018 • Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, Giorgio Patrini
Differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on private data reduces the privacy budget, and ad-hoc privacy attacks are often required to test model privacy.
no code implementations • 31 Oct 2018 • Peter Bloem, Steven de Rooij
This document provides a tutorial description of the use of the MDL principle in complex graph analysis.
2 code implementations • 22 Oct 2018 • Peter Bloem
Many transformations in deep learning architectures are sparsely connected.
1 code implementation • 11 Jun 2018 • 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.
1 code implementation • 9 Jun 2017 • 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.
27 code implementations • 17 Mar 2017 • Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
Ranked #1 on Node Classification on AIFB
no code implementations • 8 Jan 2017 • Peter Bloem, Steven de Rooij
We introduce a new method for finding network motifs: interesting or informative subgraph patterns in a network.