8 citations · 8 across the 3 of their papers we have counts for
3 papers
Relational Neurosymbolic Markov Models
Lennert De Smet, Gabriele Venturato, Luc De Raedt +1
Sequential problems are ubiquitous in AI, such as in reinforcement learning or natural language processing. State-of-the-art deep sequential models, like transformers, excel in the…
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…
Neural Probabilistic Logic Programming in Discrete-Continuous Domains
Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve +3
Neural-symbolic AI (NeSy) allows neural networks to exploit symbolic background knowledge in the form of logic. It has been shown to aid learning in the limited data regime and to…