Title: Eigenspace Restructuring:  a Principle of Space and Frequency in Neural Networks. Abstract: Understanding the fundamental principles behind the massive success of neural networks is one of the most important open questions in deep learning. However, due to the highly complex nature of the problem, progress has been relatively slow.In this note, through the lens of infinite-width networks, a.k.a. neural kernels, we present one such principle resulting from hierarchical locality. It is well-known that the eigenstructure of infinite-width multilayer perceptrons (MLPs) depends solely on the concept frequency, which measures the order of interactions.  We show that the topologies from convolutional networks (CNNs) restructure the associated eigenspaces into finer subspaces. In addition to frequency, the new structure also depends on the concept space— the distance among interaction terms, defined via the length of a minimum spanning tree containing them.  The resulting fine-grained eigenstructure dramatically improves the network’s learnability, empowering them to simultaneously model a much richer class of interactions, including long-range-low-frequency interactions, short-range-high-frequency interactions, and various interpolations and extrapolations in-between.  Finally, we show that increasing the depth of a CNN can improve the inter/extrapolation resolution and, therefore, the network’s learnability.