A paper titled dynamics in deep classifiers trained with the square loss normalization low rank neural collapse and generalization bounds. They are able to do some more theoretical analysis and basically understand why certain things happen when we train classifiers. It also introduced interesting to look at like the efficiency gains that they're reporting so you know that in many cases they see that this is actually more computational efficient than perfect hash functions.