Unsupervised Neural-Symbolic Integration
  Symbolic has been long considered as a language of human intelligence while
neural networks have advantages of robust computation and dealing with noisy
data. The integration of neural-symbolic can offer better learning and
reasoning while providing a means for interpretability through the
representation of symbolic knowledge. Although previous works focus intensively
on supervised feedforward neural networks, little has been done for the
unsupervised counterparts. In this paper we show how to integrate symbolic
knowledge into unsupervised neural networks. We exemplify our approach with
knowledge in different forms, including propositional logic for DNA promoter
prediction and first-order logic for understanding family relationship.
