2 papers
cs.LG2026
Compositional Sparsity as an Inductive Bias for Neural Architecture Design
Hongyu Lin, Antonio Briola, Yuanrong Wang +1
Identifying the structural priors that enable Deep Neural Networks (DNNs) to overcome the curse of dimensionality is a fundamental challenge in machine learning theory. Existing li…
cond-mat.stat-mech2024
Spectral Coarse-Graining and Rescaling for Preserving Structural and Dynamical Properties in Graphs
M. Schmidt, F. Caccioli, T. Aste
We introduce a graph renormalization procedure based on the coarse-grained Laplacian, which generates reduced-complexity representations for characteristic scales identified throug…