1 citations · 2 across the 8 of their papers we have counts for
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Incremental Evaluation and Training in Relational Deep Learning
Jakub Peleška, Gustav Šír
Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning. However, prevailing RDL evaluation prac…
Universal Encoders for Modular Relational Deep Learning
Jakub Peleška, Gustav Šír
Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs for end-to-end representation learning. While RDL is evolving rapidly, existing appro…
Binarizing Physics-Inspired GNNs for Combinatorial Optimization
Martin Krutský, Gustav Šír, Vyacheslav Kungurtsev +1
Physics-inspired graph neural networks (PI-GNNs) have been utilized as an efficient unsupervised framework for relaxing combinatorial optimization problems encoded through a specif…
Task-Agnostic Contrastive Pretraining for Relational Deep Learning
Jakub Peleška, Gustav Šír
Relational Deep Learning (RDL) is an emerging paradigm that leverages Graph Neural Network principles to learn directly from relational databases by representing them as heterogene…
REDELEX: A Framework for Relational Deep Learning Exploration
Jakub Peleška, Gustav Šír
Relational databases (RDBs) are widely regarded as the gold standard for storing structured information. Consequently, predictive tasks leveraging this data format hold significant…
"Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning"
Vyacheslav Kungurtsev, Leonardo Christov Moore, Gustav Sir +1
Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising computational techniques to reveal ``true'' causality. In thi…