Deep Learning with Logical Constraints
50 Citations•2022•
Eleonora Giunchiglia, Mihaela C. Stoian, Thomas Lukasiewicz
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This survey retrace such works and categorizes them based on (i) the logical language that they use to express the background knowledge and (ii) the goals that they achieve.
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.