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Found 6 papers, 2 papers with code
Fuzzy Datalog$^\exists$ over Arbitrary t-Norms
One of the main challenges in the area of Neuro-Symbolic AI is to perform logical reasoning in the presence of both neural and symbolic data.
Homomorphism Counts for Graph Neural Networks: All About That Basis
In this work, we show that both of these approaches are sub-optimal in a certain sense and argue for a more fine-grained approach, which incorporates the homomorphism counts of all structures in the "basis" of the target pattern.
On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters
A central focus of research in this field revolves around determining the least dimensionality $k$, for which $k$WL can discern graphs with different number of occurrences of a pattern graph $P$.
Incremental Updates of Generalized Hypertree Decompositions
As decompositions can be reused to solve CSPs with the same constraint scopes, investing resources in computing good decompositions is beneficial, even though the computation itself is hard.
On the Complexity of Inductively Learning Guarded Rules
We investigate the computational complexity of mining guarded clauses from clausal datasets through the framework of inductive logic programming (ILP).
The HyperTrac Project: Recent Progress and Future Research Directions on Hypergraph Decompositions
Constraint Satisfaction Problems (CSPs) play a central role in many applications in Artificial Intelligence and Operations Research.
