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Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning.
Vagueness, truth and logic
Kit Fine · 1975
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Stochastic logic programs
Stephen Muggleton · 1996
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Learning probabilistic relational models
Nir Friedman, Lise Getoor, Daphne Koller, and Avi Pfeffer · 1999
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Dimensions of neural-symbolic integration-a structured survey
Sebastian Bader and Pascal Hitzler · 2005
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Learning the structure of markov logic networks
Stanley Kok and Pedro Domingos · 2005
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Markov logic networks
Matthew Richardson and Pedro M. Domingos · 2006
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An Introduction to Statistical Relational Learning
L. Getoor and B. Taskar, editors · 2007
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Bayesian logic programming: Theory and tool
Kristian Kersting and Luc De Raedt · 2007
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Efficient weight learning for markov logic networks
Daniel Lowd and Pedro Domingos · 2007
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Parameter learning in probabilistic databases: A least squares approach
Bernd Gutmann, Angelika Kimmig, Kristian Kersting, and Luc De Raedt · 2008
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The independent choice logic and beyond
David Poole · 2008
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Probabilistic Graphical Models - Principles and Techniques
Daphne Koller and Nir Friedman · 2009
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Prediction of protein
Marco Lippi and Paolo Frasconi · 2009
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Sdd: A new canonical representation of propositional knowledge bases
Adnan Darwiche · 2011
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Probabilistic (logic) programming concepts
Luc De Raedt and Angelika Kimmig · 2015
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Inference and learning in probabilistic logic programs using weighted boolean formulas
Daan Fierens, Guy Van den Broeck, Joris Renkens, Dimitar Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens, and Luc De Raedt · 2015
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Injecting logical background knowledge into embeddings for relation extraction
Tim Rocktäschel, Sameer Singh, and Sebastian Riedel · 2015
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Unifying logic and probability
Stuart Russell · 2015
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Statistical relational artificial intelligence: Logic, probability, and computation
Luc De Raedt, Kristian Kersting, Sriraam Natarajan, and David Poole · 2016
Cited alongside, same era.
Lifted rule injection for relation embeddings
Thomas Demeester, Tim Rocktäschel, and Sebastian Riedel · 2016
Cited alongside, same era.
Hinge-loss markov random fields and probabilistic soft logic
Stephen H. Bach, Matthias Broecheler, Bert Huang, and Lise Getoor · 2017
Cited alongside, same era.
Neural-symbolic learning and reasoning: A survey and interpretation
Tarek R Besold, Artur d’Avila Garcez, Sebastian Bader, Howard Bowman, Pedro Domingos, Pascal Hitzler, Kai-Uwe Kühnberger, Luis C Lamb, Daniel Lowd, Priscila Machado Vieira Lima, et al · 2017
Cited alongside, same era.
Programming with a differentiable forth interpreter
Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky, and Sebastian Riedel · 2017
Cited alongside, same era.
A semantic loss function for deep learning with symbolic knowledge
Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy Van den Broeck · 2018
Later among the works it cites.
Lifted relational neural networks: Efficient learning of latent relational structures
Gustav Šourek, Vojtech Aschenbrenner, Filip Zelezný, Steven Schockaert, and Ondrej Kuželka · 2018
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Playgol: Learning programs through play
Andrew Cropper · 2019
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Bridging machine learning and logical reasoning by abductive learning
Wang-Zhou Dai, Qiuling Xu, Yang Yu, and Zhi-Hua Zhou · 2019
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Neuro-symbolic= neural+ logical+ probabilistic
Luc De Raedt, Robin Manhaeve, Sebastijan Dumančić, Thomas Demeester, and Angelika Kimmig · 2019
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Neural logic machines
Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, and Denny Zhou · 2019
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William W. Cohen, Fan Yang, and Kathryn Mazaitis · 2017
Cited alongside, same era.
Semantic-based regularization for learning and inference
Michelangelo Diligenti, Marco Gori, and Claudio Saccà · 2017
Cited alongside, same era.
Logic tensor networks for semantic image interpretation
Ivan Donadello, Luciano Serafini, and Artur S. d’Avila Garcez · 2017
Cited alongside, same era.
Adversarial sets for regularising neural link predictors
Pasquale Minervini, Thomas Demeester, Tim Rocktäschel, and Sebastian Riedel · 2017
Cited alongside, same era.
End-to-end differentiable proving
Tim Rocktäschel and Sebastian Riedel · 2017
Cited alongside, same era.
Query processing on probabilistic data: A survey
Guy Van den Broeck, Dan Suciu, et al · 2017
Cited alongside, same era.
Differentiable learning of logical rules for knowledge base reasoning
Fan Yang, Zhilin Yang, and William W Cohen · 2017
Cited alongside, same era.
Later among the works it cites.
Learning relational representations with auto-encoding logic programs
Sebastijan Dumančić, Tias Guns, Wannes Meert, and Hendrik Blockeel · 2019
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Artur d’Avila Garcez, Marco Gori, Luis C Lamb, Luciano Serafini, Michael Spranger, and Son N Tran · 2019
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The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision
Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, and Jiajun Wu · 2019
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Giuseppe Marra and Ondrej Kuželka · 2019
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Synthesizing datalog programs using numerical relaxation
Xujie Si, Mukund Raghothaman, Kihong Heo, and Mayur Naik · 2019
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Integrating deep learning with logic fusion for information extraction
Wenya Wang and Sinno Jialin Pan · 2019
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Nlprolog: Reasoning with weak unification for question answering in natural language
Leon Weber, Pasquale Minervini, Jannes Münchmeyer, Ulf Leser, and Tim Rocktäschel · 2019
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Transforming probabilistic programs into algebraic circuits for inference and learning
Pedro Zuidberg Dos Martires, Vincent Derkinderen, Robin Manhaeve, Wannes Meert, Angelika Kimmig, and Luc De Raedt · 2019
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Learning to reason: Leveraging neural networks for approximate DNF counting
Ralph Abboud, İsmail İlkan Ceylan, and Thomas Lukasiewicz · 2020
Closest in time.
Relational neural machines
Giuseppe Marra, Michelangelo Diligenti, Francesco Giannini, Marco Gori, and Marco Maggini · 2020
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Differentiable reasoning on large knowledge bases and natural language
Pasquale Minervini, Matko Bošnjak, Tim Rocktäschel, Sebastian Riedel, and Edward Grefenstette · 2020
Closest in time.