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Stochastic And-Or Grammars: A Unified Framework and Logic Perspective
[article]
2016
arXiv
pre-print
Stochastic And-Or grammars (AOG) extend traditional stochastic grammars of language to model other types of data such as images and events. In this paper we propose a representation framework of stochastic AOGs that is agnostic to the type of the data being modeled and thus unifies various domain-specific AOGs. Many existing grammar formalisms and probabilistic models in natural language processing, computer vision, and machine learning can be seen as special cases of this framework. We also
arXiv:1506.00858v3
fatcat:t7nz7nhcnbdvjbfx7fyvcalttu