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The goal of combining the robustness of neural networks and the expressivity of symbolic methods has rekindled the interest in neuro-symbolic AI.
Programming in Prolog
W. F. Clocksin and Chris Mellish · 1981
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Connectionism and cognitive architecture: A critical analysis
Jerry A Fodor and Zenon W Pylyshyn · 1988
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The birth of prolog
Alain Colmerauer and Philippe Roussel · 1993
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Encoding planning problems in nonmonotonic logic programs
Yannis Dimopoulos, Bernhard Nebel, and Jana Koehler · 1997
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Stable models and an alternative logic programming paradigm
Victor W. Marek and Miroslaw Truszczynski · 1999
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Developing a declarative rule language for applications in product configuration
Timo Soininen and Ilkka Niemelä · 1999
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Markov logic networks
Matthew Richardson and Pedro M. Domingos · 2006
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Neural-Symbolic Cognitive Reasoning
Artur S. d’Avila Garcez, Luís C. Lamb, and Dov M. Gabbay · 2009
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SDD: A new canonical representation of propositional knowledge bases
Adnan Darwiche · 2011
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Sum-product networks: A new deep architecture
Hoifung Poon and Pedro M. Domingos · 2011
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Weighted rules under the stable model semantics
Joohyung Lee and Yi Wang · 2016
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CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross B. Girshick · 2017
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Shapeworld - A new test methodology for multimodal language understanding
Alexander Kuhnle and Ann A. Copestake · 2017
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Deep probabilistic programming
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, and David M. Blei · 2017
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Deepproblog: Neural probabilistic logic programming
Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt · 2018
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Neural-symbolic VQA: disentangling reasoning from vision and language understanding
Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Josh Tenenbaum · 2018
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From System 1 Deep Learning to System 2 Deep Learning
Yoshua Bengio · 2019
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Pyro: Deep universal probabilistic programming
Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul A. Szerlip, Paul Horsfall, and Noah D. Goodman · 2019
Asp-core-2 input language format
Francesco Calimeri, Wolfgang Faber, Martin Gebser, Giovambattista Ianni, Roland Kaminski, Thomas Krennwallner, Nicola Leone, Marco Maratea, Francesco Ricca, and Torsten Schaub · 2020
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Probabilistic circuits: A unifying framework for tractable probabilistic models
YooJung Choi, Antonio Vergari, and Guy Van den Broeck · 2020
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Human-driven FOL explanations of deep learning
Gabriele Ciravegna, Francesco Giannini, Marco Gori, Marco Maggini, and Stefano Melacci · 2020
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Neurosymbolic AI: the 3rd wave
Artur d’Avila Garcez and Luís C. Lamb · 2020
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GENESIS: generative scene inference and sampling with object-centric latent representations
Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2020
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On the binding problem in artificial neural networks
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Monet: Unsupervised scene decomposition and representation
Christopher P. Burgess, Loïc Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matthew Botvinick, and Alexander Lerchner · 2019
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Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning
Artur S. d’Avila Garcez, Marco Gori, Luís C. Lamb, Luciano Serafini, Michael Spranger, and Son N. Tran · 2019
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Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
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Learning by abstraction: The neural state machine
Drew A. Hudson and Christopher D. Manning · 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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The algebraic mind: Integrating connectionism and cognitive science
Gary F Marcus · 2019
Cited alongside, same era.
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2020
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Generative neurosymbolic machines
Jindong Jiang and Sungjin Ahn · 2020
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SPACE: unsupervised object-oriented scene representation via spatial attention and decomposition
Zhixuan Lin, Yi-Fu Wu, Skand Vishwanath Peri, Weihao Sun, Gautam Singh, Fei Deng, Jindong Jiang, and Sungjin Ahn · 2020
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Einsum networks: Fast and scalable learning of tractable probabilistic circuits
Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, and Zoubin Ghahramani · 2020
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Splog: Sum-product logic
Arseny Skryagin, Karl Stelzner, Alejandro Molina, Fabrizio Ventola, and Kristian Kersting · 2020
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Neurasp: Embracing neural networks into answer set programming
Zhun Yang, Adam Ishay, and Joohyung Lee · 2020
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Right for the right concept: Revising neuro-symbolic concepts by interacting with their explanations
Wolfgang Stammer, Patrick Schramowski, and Kristian Kersting · 2021
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