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Neuro-Symbolic AI (NeSy) holds promise to ensure the safe deployment of AI systems, as interpretable symbolic techniques provide formal behaviour guarantees.
Reducibility among combinatorial problems
Richard M Karp · 1972
Earlier work this paper cites.
The symbol grounding problem
Stevan Harnad · 1990
Earlier work this paper cites.
Knowledge representation, reasoning, and the design of intelligent agents: The answer-set programming approach
Michael Gelfond and Yulia Kahl · 2014
Earlier work this paper cites.
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Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt · 2018
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Wang-Zhou Dai, Qiuling Xu, Yang Yu, and Zhi-Hua Zhou · 2019
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Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning
Artur d’Avila Garcez, Marco Gori, Luís C. Lamb, Luciano Serafini, Michael Spranger, and Son N. Tran · 2019
Earlier work this paper cites.
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Nora Kassner and Hinrich Schütze · 2019
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Logic-based learning of answer set programs
Mark Law, Alessandra Russo, and Krysia Broda · 2019
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Fastlas: Scalable inductive logic programming incorporating domain-specific optimisation criteria
Mark Law, Alessandra Russo, Elisa Bertino, Krysia Broda, and Jorge Lobo · 2020
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The ilasp system for inductive learning of answer set programs
Mark Law, Alessandra Russo, and Krysia Broda · 2020
Earlier work this paper cites.
Ryan Riegel, Alexander G. Gray, Francois P. S. Luus, Naweed Khan, Ndivhuwo Makondo, Ismail Yunus Akhalwaya, Haifeng Qian, Ronald Fagin, Francisco Barahona, Udit Sharma, Shajith Ikbal, Hima Karanam, Sumit Neelam, Ankita Likhyani, and Santosh K. Srivastava · 2020
Earlier work this paper cites.
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Davinder Singh, Naman Jain, Pranjali Jain, Pratik Kayal, Sudhakar Kumawat, and Nipun Batra · 2020
Earlier work this paper cites.
Neurasp: Embracing neural networks into answer set programming
Zhun Yang, Adam Ishay, and Joohyung Lee · 2020
Earlier work this paper cites.
No routing needed between capsules
Adam Byerly, Tatiana Kalganova, and Ian Dear · 2021
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Abductive knowledge induction from raw data
Wang-Zhou Dai and Stephen Muggleton · 2021
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Making sense of raw input
Richard Evans, Matko Bošnjak, Lars Buesing, Kevin Ellis, David Pfau, Pushmeet Kohli, and Marek Sergot · 2021
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Improving coherence and consistency in neural sequence models with dual-system, neuro-symbolic reasoning
Maxwell Nye, Michael Tessler, Josh Tenenbaum, and Brenden M Lake · 2021
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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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Embed2sym-scalable neuro-symbolic reasoning via clustered embeddings
Yaniv Aspis, Krysia Broda, Jorge Lobo, and Alessandra Russo · 2022
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Logic tensor networks
Samy Badreddine, Artur d’Avila Garcez, Luciano Serafini, and Michael Spranger · 2022
Deep symbolic learning: Discovering symbols and rules from perceptions
Alessandro Daniele, Tommaso Campari, Sagar Malhotra, and Luciano Serafini · 2023
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Scalable coupling of deep learning with logical reasoning
Marianne Defresne, Sophie Barbe, and Thomas Schiex · 2023
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Leveraging Large Language Models to Generate Answer Set Programs
Adam Ishay, Zhun Yang, and Joohyung Lee · 2023
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Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
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Not all neuro-symbolic concepts are created equal: Analysis and mitigation of reasoning shortcuts
Emanuele Marconato, Stefano Teso, Antonio Vergari, and Andrea Passerini · 2023
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Neural-symbolic learning and reasoning: A survey and interpretation
Tarek R Besold, Artur d’Avila Garcez, Sebastian Bader, Howard Bowman, Luis C Lamb, Leo de Penning, BV Illuminoo, Hoifung Poon, and COPPE Gerson Zaverucha · 2022
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A neuro-symbolic asp pipeline for visual question answering
Thomas Eiter, Nelson Higuera, Johannes Oetsch, and Michael Pritz · 2022
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Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami, and Kristian Kersting · 2023
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Scalable neural-probabilistic answer set programming
Arseny Skryagin, Daniel Ochs, Devendra Singh Dhami, and Kristian Kersting · 2023
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Dídac Surís, Sachit Menon, and Carl Vondrick · 2023
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Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, and Noah D Goodman · 2023
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Coupling large language models with logic programming for robust and general reasoning from text
Zhun Yang, Adam Ishay, and Joohyung Lee · 2023
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A normalized levenshtein distance metric
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