Deep Learning with Logical Constraints

Eleonora Giunchiglia; Mihaela Catalina Stoian; Thomas Lukasiewicz

IJCAI 2022poster89 citations

Deep Learning with Logical Constraints

Eleonora Giunchiglia, Mihaela Catalina Stoian, Thomas Lukasiewicz

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.

Survey Track: -Survey Track: Machine LearningSurvey Track: Knowledge Representation and Reasoning

BibTeX

@inproceedings{ijcai2022p767,
  title     = {Deep Learning with Logical Constraints},
  author    = {Giunchiglia, Eleonora and Stoian, Mihaela Catalina and Lukasiewicz, Thomas},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {5478--5485},
  year      = {2022},
  month     = {7},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2022/767},
  url       = {https://doi.org/10.24963/ijcai.2022/767},
}