39 citations · 69 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Lossless Compression of Structured Convolutional Models via Lifting
Gustav Sourek, Filip Zelezny, Ondrej Kuzelka
Lifting is an efficient technique to scale up graphical models generalized to relational domains by exploiting the underlying symmetries. Concurrently, neural models are continuous…
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…