Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.

Researchers have uncovered deep connections among different types of random objects, illuminating hidden geometric structures. These are not the kinds of objects that concern Scott Sheffield.