Factor Graph Grammars
Authors: David Chiang, Darcey Riley
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Academia | David Chiang University of Notre Dame dchiang@nd.edu Darcey Riley University of Notre Dame darcey.riley@nd.edu |
| Pseudocode | No | The paper describes algorithms and derivations mathematically but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | We plan to implement the algorithms described in this paper as differentiable operations and release them as open-source software. |
| Open Datasets | No | The paper is theoretical and does not describe experiments with datasets, thus no information on publicly available datasets for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with datasets, thus no information on validation dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, therefore no specific hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementation or dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments, therefore no specific experimental setup details like hyperparameters or training settings are provided. |