MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks

Understand

We propose a novel way to incorporate expert knowledge into the training of deep neural networks.

• Many approaches encode domain constraints directly into the network architecture, requiring non-trivial or domain-specific engineering.
• In contrast, our approach, called MultiplexNet, represents domain knowledge as a logical formula in disjunctive normal form (DNF) which is easy to encode and to elicit from human experts.
• It introduces a Categorical latent variable that learns to choose which constraint term optimizes the error function of the network and it compiles the constraints directly into the output of existing learning algorithms.