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Found 9 papers, 0 papers with code
Learning Graph Neural Networks using Exact Compression
Graph Neural Networks (GNNs) are a form of deep learning that enable a wide range of machine learning applications on graph-structured data.
On the expressive power of message-passing neural networks as global feature map transformers
We investigate the power of message-passing neural networks (MPNNs) in their capacity to transform the numerical features stored in the nodes of their input graphs.
Shape Fragments
In this paper, (i) we define our extraction mechanism formally, building on recently proposed SHACL formalizations; (ii) we establish correctness properties, which relate shape fragments to notions of provenance for database queries; (iii) we compare shape fragments with SPARQL queries; (iv) we discuss implementation options; and (v) we present initial experiments demonstrating that shape fragments are a feasible new idea.
On the convergence of cycle detection for navigational reinforcement learning
We prove convergence of a cycle-detection learning algorithm on a class of tasks that we call reducible.
Mapping-equivalence and oid-equivalence of single-function object-creating conjunctive queries
In this paper, we focus on the class of single-function object-creating conjunctive queries, or sifo CQs for short.
Positive Neural Networks in Discrete Time Implement Monotone-Regular Behaviors
Each monotone-regular behavior can be implemented by a positive neural network with a delay of one time unit.
On the satisfiability problem for SPARQL patterns
The satisfiability problem for SPARQL patterns is undecidable in general, since the expressive power of SPARQL 1. 0 is comparable with that of the relational algebra.
FO(C): A Knowledge Representation Language of Causality
Cause-effect relations are an important part of human knowledge.
