Mitigating the Curse of Detail: Scaling Arguments for Feature Learning and Sample Complexity

ArXi:2512.04165v4 Announce Type: replace Two pressing topics in the theory of deep learning are the interpretation of feature learning (FL) mechanisms and the determination of implicit bias of networks in the rich regime. Current theories of rich FL often appear in the form of high-dimensional non-linear equations, which require computationally intensive numerical solutions. Given the many details that go into defining a deep learning problem, this analytical complexity is a significant and often unavoidable challenge.