2025
From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning
ICML 2025poster
Feature learning in neural networks is crucial for their expressive power and inductive biases, motivating various theoretical approaches. Some approaches describe network behavior after training through a change in kernel scale from initialization, resulting in a generalization power comparable to…