Unlock: Neural Tangent Kernel: Lazy Training, Kernel Equivalence, μP, and the Limits of Width

In the infinite-width NTK parameterization, training a neural network with gradient descent is mathematically equivalent to kernel regression. The same limit suppresses feature learning, which is why μP and mean-field parameterizations exist. NTK is the precise boundary between the kernel and feature-learning regimes.