6 citations · 18 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
Extremal Fitting CQs do not Generalize
Balder ten Cate, Maurice Funk, Jean Christoph Jung +1
A fitting algorithm for conjunctive queries (CQs) produces, given a set of positively and negatively labeled data examples, a CQ that fits these examples. In general, there may be…
On the non-efficient PAC learnability of conjunctive queries
Balder ten Cate, Maurice Funk, Jean Christoph Jung +1
This note serves three purposes: (i) we provide a self-contained exposition of the fact that conjunctive queries are not efficiently learnable in the Probably-Approximately-Correct…
Extremal Fitting Problems for Conjunctive Queries
Balder ten Cate, Victor Dalmau, Maurice Funk +1
The fitting problem for conjunctive queries (CQs) is the problem to construct a CQ that fits a given set of labeled data examples. When a fitting CQ exists, it is in general not un…