Generating Interpretable Networks Using Hypernetworks

Researchers used hypernetworks to generate neural networks that are interpretable, meaning humans can understand the logic they use, without knowing those algorithms in advance. Unlike past work that encoded known algorithms into networks, this approach discovered three new methods for computing L1 norms, only one of which was expected. The trained hypernetworks also generalized to input sizes they had never seen during training.