AI RESEARCH
Compositional Sparsity as an Inductive Bias for Neural Architecture Design
arXiv CS.LG
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ArXi:2605.14764v1 Announce Type: new 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 literature suggests that effective high-dimensional learning is driven by compositional sparsity, where target functions decompose into constituents ed on low-dimensional variable subsets.