1 citations · 1 across the 2 of their papers we have counts for
3 papers
DPNL: A DPLL-based Algorithm for Probabilistic Neurosymbolic Learning
Thomas Jean-Michel Valentin, Pierre Genev{è}s, Luisa Sophie Werner +3
Probabilistic Neurosymbolic Learning (PNL) combines neural predictions with symbolic reasoning, enabling end-to-end learning from final-output supervision without labels for interm…
On Scaling Neurosymbolic Programming through Guided Logical Inference
Thomas Jean-Michel Valentin, Luisa Sophie Werner, Pierre Genevès +1
Probabilistic neurosymbolic learning seeks to integrate neural networks with symbolic programming. Many state-of-the-art systems rely on a reduction to the Probabilistic Weighted M…
Knowledge Enhanced Graph Neural Networks for Graph Completion
Luisa Werner, Nabil Layaïda, Pierre Genevès +1
Graph data is omnipresent and has a wide variety of applications, such as in natural science, social networks, or the semantic web. However, while being rich in information, graphs…