<div class="csl-bib-body">
<div class="csl-entry">Giunchiglia, E., Stoian, M. C., & Lukasiewicz, T. (2022). Deep Learning with Logical Constraints. In L. De Raedt (Ed.), <i>Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence</i> (pp. 5478–5485). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/767</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/192179
-
dc.description.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.
en
dc.language.iso
en
-
dc.subject
deep learning
en
dc.subject
logical constraints
en
dc.title
Deep Learning with Logical Constraints
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
University of Oxford, United Kingdom of Great Britain and Northern Ireland (the)
-
dc.contributor.affiliation
University of Oxford, United Kingdom of Great Britain and Northern Ireland (the)
-
dc.relation.isbn
978-1-956792-00-3
-
dc.description.startpage
5478
-
dc.description.endpage
5485
-
dc.type.category
Full-Paper Contribution
-
tuw.booktitle
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
-
tuw.relation.publisher
International Joint Conferences on Artificial Intelligence
