Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models

ArXi:2506.13139v2 Announce Type: replace-cross Modern Machine Learning (ML) and Deep Neural Networks (DNNs) often operate on high-dimensional data and rely on overparameterized models, where classical low-dimensional intuitions break down. In particular, the proportional regime where the data dimension, sample size, and number of model parameters are all large and comparable, gives rise to novel and sometimes counterintuitive behaviors.