Leveraging Sparse Linear Layers for Debuggable Deep Networks

As machine learning (ML) models find wide-spread application, there is a growing demand for interpretability: access to tools that help people see why the model made its decision. There are still many obstacles towards achieving this goal though, particularly in the context of deep learning. These obstacles stem from the scale of modern deep networks, as well as the complexity of even defining and assessing the (often context-dependent) desiderata of interpretability. Existing work on deep network interpretability has largely approached this problem from two perspectives. The first one seeks to uncover the concepts associated with specific neurons in the network, for example through visualization [Yos 15] or semantic labeling [Bau 17].