  We present a deep learning architecture for learning fuzzy logic expressions.
Our model uses an innovative, parameterized, differentiable activation function
that can learn a number of logical operations by gradient descent. This
activation function allows a neural network to determine the relationships
between its input variables and provides insight into the logical significance
of learned network parameters. We provide a theoretical basis for this
parameterization and demonstrate its effectiveness and utility by successfully
applying our model to five classification problems from the UCI Machine
Learning Repository.
