Extracting Formulae in Many-Valued Logic from Deep Neural Networks
Yani Zhang Helmut Bölcskei
We propose a new perspective on deep ReLU networks, namely as circuit
counterparts of Lukasiewicz infinite-valued logic -- a many-valued (MV)
generalization of Boolean logic. An algorithm for extracting formulae in MV
logic from deep ReLU networks is presented. As the algorithm applies to
networks with general, in particular also real-valued, weights, it can be used
to extract logical formulae from deep ReLU networks trained on data.