  Interpreting neural networks is a crucial and challenging task in machine
learning. In this paper, we develop a novel framework for detecting statistical
interactions captured by a feedforward multilayer neural network by directly
interpreting its learned weights. Depending on the desired interactions, our
method can achieve significantly better or similar interaction detection
performance compared to the state-of-the-art without searching an exponential
solution space of possible interactions. We obtain this accuracy and efficiency
by observing that interactions between input features are created by the
non-additive effect of nonlinear activation functions, and that interacting
paths are encoded in weight matrices. We demonstrate the performance of our
method and the importance of discovered interactions via experimental results
on both synthetic datasets and real-world application datasets.
