Skip to content
conftrace_
2020 NeurIPS NeurIPS 2020

Factor Graph Grammars

We propose the use of hyperedge replacement graph grammars for factor graphs, or factor graph grammars (FGGs) for short. FGGs generate sets of factor graphs and can describe a more general class of models than plate notation, dynamic graphical models, case-factor diagrams, and sum-product networks can. Moreover, inference can be done on FGGs without enumerating all the generated factor graphs. For finite variable domains (but possibly infinite sets of graphs), a generalization of variable elimination to FGGs allows exact and tractable inference in many situations. For finite sets of graphs (but possibly infinite variable domains), a FGG can be converted to a single factor graph amenable to standard inference techniques.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Mathematics & Optimization

More from NeurIPS 2020

All NeurIPS 2020 papers →