39 citations · 102 across the 36 of their papers we have counts for
6 papers · 1 filter
Parallel Noising in Neural Markov Logic Networks
Peter Jung, Giuseppe Marra, Ondrej Kuzelka
Neural Markov Logic Networks (NMLNs) are a flexible neurosymbolic relational model. Previous work has shown that, although NMLNs achieve strong performance as generative models for…
Learning with Molecules beyond Graph Neural Networks
Gustav Sourek, Filip Zelezny, Ondrej Kuzelka
We demonstrate a deep learning framework which is inherently based in the highly expressive language of relational logic, enabling to, among other things, capture arbitrarily compl…
Beyond Graph Neural Networks with Lifted Relational Neural Networks
Gustav Sourek, Filip Zelezny, Ondrej Kuzelka
We demonstrate a declarative differentiable programming framework based on the language of Lifted Relational Neural Networks, where small parameterized logic programs are used to e…
Neural Markov Logic Networks
Giuseppe Marra, Ondřej Kuželka
We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov logic networks (MLNs), NMLNs are an ex…
VC-Dimension Based Generalization Bounds for Relational Learning
Ondrej Kuzelka, Yuyi Wang, Steven Schockaert
In many applications of relational learning, the available data can be seen as a sample from a larger relational structure (e.g. we may be given a small fragment from some social n…
Stacked Structure Learning for Lifted Relational Neural Networks
Gustav Sourek, Martin Svatos, Filip Zelezny +2
Lifted Relational Neural Networks (LRNNs) describe relational domains using weighted first-order rules which act as templates for constructing feed-forward neural networks. While p…