Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Luis Lamb
- sl:arxiv_num : 2003.00330
- sl:arxiv_published : 2020-02-29T18:55:13Z
- sl:arxiv_summary : Neural-symbolic computing has now become the subject of interest of both
academic and industry research laboratories. Graph Neural Networks (GNN) have
been widely used in relational and symbolic domains, with widespread
application of GNNs in combinatorial optimization, constraint satisfaction,
relational reasoning and other scientific domains. The need for improved
explainability, interpretability and trust of AI systems in general demands
principled methodologies, as suggested by neural-symbolic computing. In this
paper, we review the state-of-the-art on the use of GNNs as a model of
neural-symbolic computing. This includes the application of GNNs in several
domains as well as its relationship to current developments in neural-symbolic
computing.@en
- sl:arxiv_title : Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective@en
- sl:arxiv_updated : 2020-03-11T20:33:01Z
- sl:bookmarkOf : https://arxiv.org/abs/2003.00330
- sl:creationDate : 2020-03-15
- sl:creationTime : 2020-03-15T10:39:59Z
Documents with similar tags (experimental)
Préférences...