4 papers
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,…
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…
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…
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…