Learn to Explain Efficiently via Neural Logic Inductive Learning
  The capability of making interpretable and self-explanatory decisions is
essential for developing responsible machine learning systems. In this work, we
study the learning to explain problem in the scope of inductive logic
programming (ILP). We propose Neural Logic Inductive Learning (NLIL), an
efficient differentiable ILP framework that learns first-order logic rules that
can explain the patterns in the data. In experiments, compared with the
state-of-the-art methods, we find NLIL can search for rules that are x10 times
longer while remaining x3 times faster. We also show that NLIL can scale to
large image datasets, i.e. Visual Genome, with 1M entities.
