Zobrazeno 1 - 4
of 4
pro vyhledávání: '"De Smet, Lennert"'
As illustrated by the success of integer linear programming, linear integer arithmetic is a powerful tool for modelling combinatorial problems. Furthermore, the probabilistic extension of linear programming has been used to formulate problems in neur
Externí odkaz:
http://arxiv.org/abs/2410.12389
Neural probabilistic logic systems follow the neuro-symbolic (NeSy) paradigm by combining the perceptive and learning capabilities of neural networks with the robustness of probabilistic logic. Learning corresponds to likelihood optimization of the n
Externí odkaz:
http://arxiv.org/abs/2408.08133
Categorical random variables can faithfully represent the discrete and uncertain aspects of data as part of a discrete latent variable model. Learning in such models necessitates taking gradients with respect to the parameters of the categorical prob
Externí odkaz:
http://arxiv.org/abs/2311.12569
Autor:
De Smet, Lennert, Martires, Pedro Zuidberg Dos, Manhaeve, Robin, Marra, Giuseppe, Kimmig, Angelika, De Raedt, Luc
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 facilitate inference on out-of-distribution data. Probabilistic NeSy f
Externí odkaz:
http://arxiv.org/abs/2303.04660