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Neural symbolic processing aims to combine the generalization of logical learning approaches and the performance of neural networks.
Inductive logic programming: Theory and methods
Stephen Muggleton and Luc De Raedt · 1994
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Lise Getoor, Nir Friedman, Daphne Koller, and Benjamin Taskar · 2007
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Introduction to statistical relational learning
Daphne Koller, Nir Friedman, Sašo Džeroski, Charles Sutton, Andrew McCallum, Avi Pfeffer, Pieter Abbeel, Ming-Fai Wong, David Heckerman, Chris Meek, et al · 2007
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Probabilistic inductive logic programming
Luc De Raedt and Kristian Kersting · 2008
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Ilp turns 20
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An introduction to conditional random fields
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Quickfoil: Scalable inductive logic programming
Qiang Zeng, Jignesh M Patel, and David Page · 2014
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Artificial intelligence: a modern approach
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Logical rule induction and theory learning using neural theorem proving
Andres Campero, Aldo Pareja, Tim Klinger, Josh Tenenbaum, and Sebastian Riedel · 2018
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Learning explanatory rules from noisy data
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End-to-end differentiable proving
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Differentiable learning of logical rules for knowledge base reasoning
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