404 citations · 797 across the 39 of their papers we have counts for
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EM-NeSy: Expectation Maximization for Neurosymbolic Learning
Annegret Seibt, Luc De Raedt, Giuseppe Marra
Neurosymbolic (NeSy) models integrate neural networks and symbolic reasoning for robust and interpretable AI. State-of-the-art NeSy models require that the symbolic component is ex…
DeepLog: A Software Framework for Modular Neurosymbolic AI
Robin Manhaeve, Stefano Colamonaco, Vincent Derkinderen +4
DeepLog is an operational neurosymbolic framework that unifies logic and deep learning within standard PyTorch workflows. While existing neurosymbolic systems focus on a particular…
EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic
Victor Verreet, Lennert De Smet, Luc De Raedt +1
Neural probabilistic logic systems follow the neuro-symbolic (NeSy) paradigm by combining the perceptive and learning capabilities of neural networks with the robustness of probabi…
Scaling Up Inductive Logic Programming by Learning from Interpretations
Hendrik Blockeel, Luc De Raedt, Nico Jacobs +1
When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming…
Top-down induction of clustering trees
Hendrik Blockeel, Luc De Raedt, Jan Ramon
An approach to clustering is presented that adapts the basic top-down induction of decision trees method towards clustering. To this aim, it employs the principles of instance base…