Hybrid Markov Logic Networks

By Jue Wang, Pedro M. Domingos. 2008

154 citations recorded by the source corpus.

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Citations in the corpus

Logic Tensor Networks for Semantic Image Interpretation (2017, background): “, 8]. The area of Statistical Relational Learning (SRL), or Statistical Artiﬁcial Intelligence (StarAI), seeks to combine data-driven learning, in the presence of uncertainty, with symbolic knowledge [29, 2, 13, 7, 26, 23]. However, only very few SRL systems have been applied to SII tasks (c.f. Sect”
Learning relational dependency networks in hybrid domains (2015, background): “There are several formalisms that can represent hybrid relational domains including Hybrid Markov Logic Networks (HMLNs) (Wang and Domingos 2008), Hybrid Problog (HProblog) (Gutmann et al.”
Tensor Networks : Deep Learning and Logical Reasoning from Data and Knowledge ⋆ (2016, methodology): “Real logic is orthogonal to the approach taken by (Hybrid) Ma rkov Logic Networks (MLNs) and its variations [24, 27, 22].”
Figaro : An Object-Oriented Probabilistic Programming Language (2009, background): “Markov logic has been recen tly xtended to hybrid domains [11], but it does not allow general expressions in the constraint w eights, so it cannot represent this constraint.”
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, background): “Hybrid Markov logic networks (Wang and Domingos, 2008) mix discrete and continuous variables.”
Extending ProbLog with Continuous Distributions (2010, methodology): “Similarly to Hybrid ProbLog, Hybrid Markov Logic Networks (HMLNs) [5] aim at integrating Boolean and numerical random variables in a probabilistic-logic modeling framework.”
Learning and reasoning in logic tensor networks: theory and application to semantic image interpretation (2017, methodology): “easily encoded in LTN. Real Logic is orthogonal to the approach taken by (Hybrid) Logic Networks (MLNs) and its variations [21, 26, 18].”