Recursive Random Fields

Keywords

Probabilistic logic, Markov chain, Probabilistic CTL, Computer science, Conjunction (astronomy)

References

• Statistical Analysis of Non-Lattice DataJulian Besag — 1975
• Discriminative Training of Markov Logic NetworksParag Singla, Pedro Domingos — 2005
• Sound and Efficient Inference with Probabilistic and Deterministic DependenciesHoifung Poon, Pedro Domingos — 2006
• On the Statistical Analysis of Dirty PicturesJulian Besag — 1986
• Foundations of DatabasesSerge Abiteboul, Richard Hull, Victor Vianu — 1994
• Markov logic networksMatthew Richardson, Pedro Domingos — 2006
• Knowledge-Based Artificial Neural NetworksGeoffrey G. Towell, Jude Shavlik — 1994
• Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron AlgorithmsMichael Collins — 2002
• CORDS: automatic discovery of correlations and soft functional dependenciesIhab F. Ilyas, Volker Markl, Peter J. Haas — 2004
• Training Products of Experts by Minimizing Contrastive DivergenceGeoffrey E. Hinton — 2002
• Learning the structure of Markov logic networksStanley Kok, Pedro Domingos — 2005
• Clean Answers over Dirty Databases: A Probabilistic ApproachPeriklis Andritsos, Ariel Fuxman, Renée J. Miller — 2006
• A general stochastic approach to solving problems with hard and soft constraintsHenry A. Kautz, B. Selman, Yue Jiang — 1996

Cited by

• Robust Techniques for Visual SurveillanceS. Tran — 2008
• Markov Logic: An Interface Layer for Artificial IntelligencePedro Domingos, Daniel Lowd — 2009
• Multiple relationship based deduplicationPei Li — 2010
• Structured machine learning: the next ten yearsThomas G. Dietterich, Pedro Domingos, Lise Getoor — 2008
• On Lifting the Gibbs Sampling AlgorithmDeepak Venugopal, Vibhav Gogate — 2012
• Knowledge Base Refinement and Knowledge Translation with Markov Logic NetworksShangpu Jiang — 2016
• Research on Markov Logic NetworksCongfu Xu, Chunliang Hao, Bao-Jun Su — 2011
• Inductive Logic ProgrammingAkihiro Yamamoto, Hayato Ohwada, Katsumi Inoue — 2016
• Markov logic: theory, algorithms and applicationsPedro Domingos, Parag Parag — 2009
• Soft quantification in statistical relational learningGolnoosh Farnadi, Stephen H. Bach, Marie‐Francine Moens — 2017
• Modeling travel mode choice behavior with bounded rationality using Markov Logic NetworksTao Li, Hongzhi Guan, Jiaqi Ma — 2019
• Probabilistic Cognition for Technical Systems: Statistical Relational Models for High-Level Knowledge Representation, Learning and ReasoningDominik Jain — 2012
• Learning Mixtures of MLNsMohammad Maminur Islam, Somdeb Sarkhel, Deepak Venugopal — 2018
• Markov LogicPedro Domingos, Stanley Kok, Daniel Lowd — 2008
• Unifying logical and statistical AI with Markov logicPedro Domingos, Daniel Lowd — 2019
• Quantified Markov Logic NetworksVíctor Gutiérrez-Basulto, Jean Christoph Jung, Ondřej Kuželka — 2018
• Knowledge Engineering with Markov Logic Networks: A ReviewDominik Jain — 2011
• Markov Logic in Infinite DomainsParag Singla, Pedro Domingos — 2007
• Statistical Relational Learning with Soft QuantifiersG. Farnadi, Stephen H. Bach, Marjon Blondeel — 2015
• Statistical Relational Learning for Handwriting RecognitionArti Shivram, Tushar Khot, Sriraam Natarajan — 2014

Related papers

• Markov logic networksMatthew Richardson, Pedro Domingos — 2006
• Introduction to Statistical Relational LearningCharles Sutton, A. McCallum — 2007
• Discriminative Training of Markov Logic NetworksParag Singla, Pedro Domingos — 2005
• Entity Resolution with Markov LogicParag Singla, Pedro Domingos — 2006
• Learning the structure of Markov logic networksStanley Kok, Pedro Domingos — 2005
• Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron AlgorithmsMichael Collins — 2002
• Introduction to statistical relational learningLise Getoor, Ben Taskar — 2007
• Joint Inference in Information ExtractionHoifung Poon, Pedro Domingos — 2007
• First-order probabilistic inferenceDavid Poole — 2003