17 citations · 49 across the 23 of their papers we have counts for
27 papers
Neuro-Relational Programs: Unifying Queries and Neural Computation over Structured Data
Arie Soeteman, Balder ten Cate, Maurice Funk +3
The conventional approach to deep learning over relational databases applies neural models, such as Graph Neural Networks (GNNs), to a graph representation of the database. Recent…
Expressive Power of Deep Homomorphism Networks over Relational Databases
Moritz Schönherr, Balder ten Cate, Maurice Funk +3
The expressive limitations of message-passing Graph Neural Networks (GNNs) have motivated a wide range of more powerful graph learning architectures. We advocate Deep Homomorphism…
When do modal definability and preservation theorems transfer to the finite?
Johan van Benthem, Balder ten Cate, Xi Yang
We study which classic modal definability and preservation results survive when attention is restricted to finite structures, where many first-order transfer theorems are known to…
How Expressive Are Graph Neural Networks in the Presence of Node Identifiers?
Arie Soeteman, Michael Benedikt, Martin Grohe +1
Graph neural networks (GNNs) are a widely used class of machine learning models for graph-structured data, based on local aggregation over neighbors. GNNs have close connections to…
Six Proofs of Interpolation for the Modal Logic K
Nick Bezhanishvili, Balder ten Cate, Rosalie Iemhoff
In this chapter, we present six different proofs of Craig interpolation for the modal logic K, each using a different set of techniques (model-theoretic, proof-theoretic, syntactic…
Interpolation in First-Order Logic
Balder ten Cate, Jesse Comer
In this chapter we give a basic overview of known results regarding Craig interpolation for first-order logic as well as for fragments of first-order logic. Our aim is to provide a…