  In this paper, we introduce a novel approach to neural learning: the
Feature-Imitating-Network (FIN). A FIN is a neural network with weights that
are initialized to reliably approximate one or more closed-form statistical
features, such as Shannon's entropy. In this paper, we demonstrate that FINs
(and FIN ensembles) provide best-in-class performance for a variety of
downstream signal processing and inference tasks, while using less data and
requiring less fine-tuning compared to other networks of similar (or even
greater) representational power. We conclude that FINs can help bridge the gap
between domain experts and machine learning practitioners by enabling
researchers to harness insights from feature-engineering to enhance the
performance of contemporary representation learning approaches.
