1 code implementation • 6 Nov 2020 • 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 complex graph structures.
2 code implementations • ICLR 2021 • Gustav Sourek, Filip Zelezny, Ondrej Kuzelka
The computation graphs themselves then reflect the symmetries of the underlying data, similarly to the lifted graphical models.
2 code implementations • 13 Jul 2020 • 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 encode relational learning scenarios.
no code implementations • 5 Oct 2017 • Gustav Sourek, Martin Svatos, Filip Zelezny, Steven Schockaert, Ondrej Kuzelka
Lifted Relational Neural Networks (LRNNs) describe relational domains using weighted first-order rules which act as templates for constructing feed-forward neural networks.
1 code implementation • 20 Aug 2015 • Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezny, Ondrej Kuzelka
We propose a method combining relational-logic representations with neural network learning.