  Explaining neural network computation in terms of probabilistic/fuzzy logical
operations has attracted much attention due to its simplicity and high
interpretability. Different choices of logical operators such as AND, OR and
XOR give rise to another dimension for network optimization, and in this paper,
we study the open problem of learning a universal logical operator without
prescribing to any logical operations manually. Insightful observations along
this exploration furnish deep convolution networks with a novel logical
interpretation.
