Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Manhaeve, Robin"'
Neuro-symbolic systems (NeSy), which claim to combine the best of both learning and reasoning capabilities of artificial intelligence, are missing a core property of reasoning systems: Declarativeness. The lack of declarativeness is caused by the fun
Externí odkaz:
http://arxiv.org/abs/2405.09521
The field of neuro-symbolic artificial intelligence (NeSy), which combines learning and reasoning, has recently experienced significant growth. There now are a wide variety of NeSy frameworks, each with its own specific language for expressing backgr
Externí odkaz:
http://arxiv.org/abs/2405.00532
Publikováno v:
International Journal of Approximate Reasoning (2024): 109130
The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic. Over the past 30 years, numerous languages and frameworks have been developed for modeling, inference and learni
Externí odkaz:
http://arxiv.org/abs/2402.13782
Autor:
Sansone, Emanuele, Manhaeve, Robin
Self-supervised learning is a popular and powerful method for utilizing large amounts of unlabeled data, for which a wide variety of training objectives have been proposed in the literature. In this study, we perform a Bayesian analysis of state-of-t
Externí odkaz:
http://arxiv.org/abs/2401.00873
Autor:
Sansone, Emanuele, Manhaeve, Robin
Publikováno v:
ICLR 2023 Workshop NeSy-GeMs
We introduce GEDI, a Bayesian framework that combines existing self-supervised learning objectives with likelihood-based generative models. This framework leverages the benefits of both GEnerative and DIscriminative approaches, resulting in improved
Externí odkaz:
http://arxiv.org/abs/2304.11357
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
Autor:
Sansone, Emanuele, Manhaeve, Robin
Self-supervised learning is a popular and powerful method for utilizing large amounts of unlabeled data, for which a wide variety of training objectives have been proposed in the literature. In this study, we perform a Bayesian analysis of state-of-t
Externí odkaz:
http://arxiv.org/abs/2212.13425
This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence. Neurosymbolic artificial intelligence (NeSy) studies the integration
Externí odkaz:
http://arxiv.org/abs/2108.11451
Recent advances in neural symbolic learning, such as DeepProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which
Externí odkaz:
http://arxiv.org/abs/2106.12574
Publikováno v:
In Artificial Intelligence March 2024 328
