2018
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
ICML 2018oral
This paper develops a novel methodology for using symbolic knowledge in deep learning. From first principles, we derive a semantic loss function that bridges between neural output vectors and logical constraints. This loss function captures how close the neural network is to satisfying the constrain…