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Graph Neural Networks for Maximum Constraint Satisfaction
Many combinatorial optimization problems can be phrased in the language of constraint satisfaction problems. We introduce a graph neural network architecture for solving such optimization problems. The architecture is generic; it works for all binary constraint satisfaction problems. Training is uns...
Autores principales: | Tönshoff, Jan, Ritzert, Martin, Wolf, Hinrikus, Grohe, Martin |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959828/ https://www.ncbi.nlm.nih.gov/pubmed/33733220 http://dx.doi.org/10.3389/frai.2020.580607 |
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