Record link: 
http://hdl.handle.net/20.500.12708/192179
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Title: 
Deep Learning with Logical Constraints
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Citation: 
Giunchiglia, E., Stoian, M. C., & Lukasiewicz, T. (2022). Deep Learning with Logical Constraints. In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 5478–5485). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/767
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Publisher DOI: 
10.24963/ijcai.2022/767
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Giunchiglia, Eleonora 
Stoian, Mihaela Cătălina 
Lukasiewicz, Thomas 
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Organisational Unit: 
E192-07 - Forschungsbereich Artificial Intelligence Techniques 
E192-03 - Forschungsbereich Knowledge Based Systems 
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Published in: 
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence 
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ISBN: 
978-1-956792-00-3
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Date (published): 
2022
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Event name: 
31st International Joint Conference on Artificial Intelligence (IJCAI 2022) 
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Event date: 
23-Jul-2022 - 29-Jul-2022
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Event place: 
Wien, Austria
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Number of Pages: 
8
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Publisher: 
International Joint Conferences on Artificial Intelligence
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Keywords: 
deep learning; logical constraints
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Abstract: 
In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant with the background knowledge itself, e.g., for safety-critical applications. In this survey, we retrace such works and categorize them based on (i) the logical language that they use to express the background knowledge and (ii) the goals that they achieve.
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Research Areas: 
Logic and Computation: 50%
Information Systems Engineering: 50%
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Science Branch: 
1020 - Informatik: 80%
1010 - Mathematik: 20%
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Appears in Collections:
Conference Paper

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