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We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning.
On sentences which are true of direct unions of algebras
Alfred Horn · 1951
Earlier work this paper cites.
Principles of Artificial Intelligence
Nils J Nilsson · 1982
Earlier work this paper cites.
Connectionism and cognitive architecture: A critical analysis
Jerry A Fodor and Zenon W Pylyshyn · 1988
Earlier work this paper cites.
Computing with Logic: Logic Programming with Prolog
David Maier and David S. Warren · 1988
Earlier work this paper cites.
Connectionism and the problem of systematicity: Why smolensky’s solution doesn’t work
Jerry Fodor and Brian P McLaughlin · 1990
Earlier work this paper cites.
Inductive logic programming
Stephen Muggleton · 1991
Earlier work this paper cites.
Function optimization using connectionist reinforcement learning algorithms
Ronald J Williams and Jing Peng · 1991
Earlier work this paper cites.
On the complexity of blocks-world planning
Naresh Gupta and Dana S Nau · 1992
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
Earlier work this paper cites.
Systematicity in connectionist language learning
Robert F Hadley · 1994
Earlier work this paper cites.
Stochastic logic programs
Stephen Muggleton · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Reinforcement learning: An introduction , volume 1
Richard S Sutton and Andrew G Barto · 1998
Earlier work this paper cites.
Learning probabilistic relational models
Nir Friedman, Lise Getoor, Daphne Koller, and Avi Pfeffer · 1999
Earlier work this paper cites.
Interpreting bayesian logic programs
Kristian Kersting, Luc De Raedt, and Stefan Kramer · 2000
Earlier work this paper cites.
Complexity and expressive power of logic programming
Evgeny Dantsin, Thomas Eiter, Georg Gottlob, and Andrei Voronkov · 2001
Earlier work this paper cites.
Markov logic networks
Matthew Richardson and Pedro Domingos · 2006
Earlier work this paper cites.
Learning symbolic models of stochastic domains
Hanna M Pasula, Luke S Zettlemoyer, and Leslie Pack Kaelbling · 2007
Earlier work this paper cites.
An object-oriented representation for efficient reinforcement learning
Carlos Diuk, Andre Cohen, and Michael L Littman · 2008
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
Prediction of protein β \beta -residue contacts by markov logic networks with grounding-specific weights
Marco Lippi and Paolo Frasconi · 2009
Earlier work this paper cites.
The Logic of Adaptive Behavior
Martijn Van Otterlo · 2009
Earlier work this paper cites.
Generalized planning: Synthesizing plans that work for multiple environments
Yuxiao Hu and Giuseppe De Giacomo · 2011
Earlier work this paper cites.
A new representation and associated algorithms for generalized planning
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Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 2012
Earlier work this paper cites.
Strong systematicity through sensorimotor conceptual grounding: an unsupervised, developmental approach to connectionist sentence processing
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Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, and Pushmeet Kohli · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Densely connected convolutional networks
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Reasoning about Object Affordances in a Knowledge Base Representation
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End-to-end differentiable proving
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A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Tim Lillicrap · 2017
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Jiajun Wu, Joshua B Tenenbaum, and Pushmeet Kohli · 2017
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Differentiable learning of logical rules for knowledge base reasoning
Fan Yang, Zhilin Yang, and William W Cohen · 2017
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Incorporating relation paths in neural relation extraction
Wenyuan Zeng, Yankai Lin, Zhiyuan Liu, and Maosong Sun · 2017
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Leveraging grammar and reinforcement learning for neural program synthesis
Rudy R Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, and Pushmeet Kohli · 2018
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Logical rule induction and theory learning using neural theorem proving
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Tunneling neural perception and logic reasoning through abductive learning
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Learning explanatory rules from noisy data
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Deepproblog: Neural probabilistic logic programming
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Transparency by design: Closing the gap between performance and interpretability in visual reasoning
David Mascharka, Philip Tran, Ryan Soklaski, and Arjun Majumdar · 2018
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Neural program synthesis from diverse demonstration videos
Shao-Hua Sun, Hyeonwoo Noh, Sriram Somasundaram, and Joseph Lim · 2018
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A review of generalized planning
Sergio Jiménez, Javier Segovia-Aguas, and Anders Jonsson · 2019
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