2 citations · 2 across the 4 of their papers we have counts for
4 papers
Autoregressive Models for Knowledge Graph Generation
Thiviyan Thanapalasingam, Antonis Vozikis, Peter Bloem +1
Knowledge Graph (KG) generation requires models to learn complex semantic dependencies between triples while maintaining domain validity constraints. Unlike link prediction, which…
Predicting integers from continuous parameters
Bas Maat, Peter Bloem
We study the problem of predicting numeric labels that are constrained to the integers or to a subrange of the integers. For example, the number of up-votes on social media posts,…
Universal pre-training by iterated random computation
Peter Bloem
We investigate the use of randomly generated data for the sake of pre-training a model. We justify this approach theoretically from the perspective of algorithmic complexity, build…
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Taraneh Younesian, Daniel Daza, Emile van Krieken +2
Graph neural networks (GNNs) learn to represent nodes by aggregating information from their neighbors. As GNNs increase in depth, their receptive field grows exponentially, leading…