  While various complexity measures for deep neural networks exist, specifying
an appropriate measure capable of predicting and explaining generalization in
deep networks has proven challenging. We propose Neural Complexity (NC), a
meta-learning framework for predicting generalization. Our model learns a
scalar complexity measure through interactions with many heterogeneous tasks in
a data-driven way. The trained NC model can be added to the standard training
loss to regularize any task learner in a standard supervised learning scenario.
We contrast NC's approach against existing manually-designed complexity
measures and other meta-learning models, and we validate NC's performance on
multiple regression and classification tasks
