Factor Graph Grammars
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 3.
4 papers.
- 2022Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGscited by 10
- 2022Low-Rank Constraints for Fast Inference in Structured Modelscited by 8
- 2022Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammarscited by 7
- 2021Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networkscited by 3
- 2022Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars18 Dec 2022
- 2022Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs1 May 2022
- 2022Low-Rank Constraints for Fast Inference in Structured Models8 Jan 2022
- 2021Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks2 Nov 2021