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Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Reasoning about time and knowledge in neural symbolic learning systems
Artur d’Avila Garcez and Luís Lamb · 2003
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Logic programs and connectionist networks
Pascal Hitzler, Steffen Hölldobler, and Anthony K. Seda · 2004
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Using matrices to model symbolic relationship
Ilya Sutskever and Geoffrey Hinton · 2008
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Neural-Symbolic Cognitive Reasoning
Artur d’Avila Garcez, Luís C. Lamb, and Dov M. Gabbay · 2009
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Learning structured embeddings of knowledge bases
Antoine Bordes, Jason Weston, Ronan Collobert, and Yoshua Bengio · 2011
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Neural-symbolic learning and reasoning: Contributions and challenges
Artur d’Avila Garcez, Tarek Besold, Luc de Raedt, Peter Földiák, Pascal Hitzler, Thomas Icard, Kai-Uwe Kühnberger, Luís C. Lamb, Risto Miikkulainen, and Daniel Silver · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
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Learning knowledge base inference with neural theorem provers
Tim Rocktäschel and Sebastian Riedel · 2016
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Logic tensor networks: Deep learning and logical reasoning from data and knowledge
Luciano Serafini and Artur d’Avila Garcez · 2016
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Semantic-based regularization for learning and inference
Michelangelo Diligenti, Marco Gori, and Claudio Saccà · 2017
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A neural-symbolic approach to natural language tasks
Qiuyuan Huang, Paul Smolensky, Xiaodong He, Li Deng, and Dapeng Oliver Wu · 2017
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Learning combinatorial optimization algorithms over graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Tim Lillicrap · 2017
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Combining symbolic expressions and black-box function evaluations in neural programs
Forough Arabshahi, Sameer Singh, and Animashree Anandkumar · 2018
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Relational inductive biases, deep learning, and graph networks
Peter Battaglia, Jessica Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Zambaldi et. al · 2018
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Machine learning for combinatorial optimization: a methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2018
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Learning explanatory rules from noisy data
Richard Evans and Edward Grefenstette · 2018
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Few-shot learning with graph neural networks
Victor Garcia and Joan Bruna · 2018
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Combinatorial optimization with graph convolutional networks and guided tree search
Zhuwen Li, Qifeng Chen, and Vladlen Koltun · 2018
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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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Recurrent relational networks
Rasmus Palm, Ulrich Paquet, and Ole Winther · 2018
Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP
Marcelo Prates, Pedro Avelar, Henrique Lemos, Luís Lamb, and Moshe Vardi · 2019
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Learning a SAT solver from single-bit supervision
Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, and David L. Dill · 2019
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Jan Toenshoff, Martin Ritzert, Hinrikus Wolf, and Martin Grohe · 2019
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Extracting relational explanations from deep neural networks: A survey from a neural-symbolic perspective
Joe Townsend, Thomas Chaton, and João Monteiro · 2019
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A boxology of design patterns for hybrid learning and reasoning systems
Frank van Harmelen and Annette ten Teije · 2019
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018
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Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
Sjoerd van Steenkiste, Michael Chang, Klaus Greff, and Jürgen Schmidhuber · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Deep learning on graphs: A survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2018
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Memory augmented recursive neural networks
Forough Arabshahi, Zhichu Lu, Sameer Singh, and Animashree Anandkumar · 2019
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Generative code modeling with graphs
Marc Brockschmidt, Miltiadis Allamanis, Alexander Gaunt, and Oleksandr Polozov · 2019
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Protein structure prediction beyond alphafold
Guo-Wei Wei · 2019
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 2019
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Graph convolutional networks for text classification
Liang Yao, Chengsheng Mao, and Yuan Luo · 2019
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Learning to represent edits
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, and Alexander Gaunt Marc Brockschmidt · 2019
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G2SAT: learning to generate SAT formulas
Jiaxuan You, Haoze Wu, Clark Barrett, Raghuram Ramanujan, and Jure Leskovec · 2019
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Learning to reason: Leveraging neural networks for approximate DNF counting
Ralph Abboud, Ismail Ceylan, and Thomas Lukasiewicz · 2020
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Predicting propositional satisfiability via end-to-end learning
Chris Cameron, Rex Chen, Jason Hartford, and Kevin Leyton-Brown · 2020
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Machine learning on graphs: A model and comprehensive taxonomy
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, and Kevin Murphy · 2020
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Neural-symbolic argumentation mining: An argument in favor of deep learning and reasoning
Andrea Galassi, Kristian Kersting, Marco Lippi, Xiaoting Shao, and Paolo Torroni · 2020
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AAAI20 fireside chat with Daniel Kahneman
Daniel Kahneman, Francesca Rossi, Geoffrey Hinton, Yoshua Bengio, and Yann LeCun · 2020
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Deep learning for symbolic mathematics
Guillaume Lample and François Charton · 2020
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The next decade in AI: Four steps towards robust artificial intelligence
Gary Marcus · 2020
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2020 AI predictions from IBM research
Sriram Raghavan · 2020
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A survey on the expressive power of graph neural networks
Ryoma Sato · 2020
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A deep learning approach to antibiotic discovery
Jonathan M. Stokes, Kevin Yang, Kyle Swanson, and Wengong Jin et al · 2020
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