Empirically, we find that a variety of neural networks successfully learn sparse parities, with discontinuous phase transitions in the training curves.

Our theoretical results and experiments on static real-world datasets with simulated dynamics show that our framework can be used to facilitate social science studies.

The Hessian is ubiquitous in applied mathematics, statistics, and machine learning (ML).