An abstract framework for neural flows with composition and separation structures is proven to universally approximate any operator, recovering ResNet and plain architectures via discretization.
Yehyun Kwon and Sanghyuk Lee
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Derives explicit approximation rates for shallow ReLU^s networks in L^p and Sobolev spaces and shows path-norm regularized networks achieve minimax-optimal generalization rates with matching lower bounds.
citing papers explorer
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Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations
An abstract framework for neural flows with composition and separation structures is proven to universally approximate any operator, recovering ResNet and plain architectures via discretization.
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Shallow ReLU$^s$ Networks in $L^p$-Type and Sobolev Spaces: Approximation and Path-Norm Controlled Generalization
Derives explicit approximation rates for shallow ReLU^s networks in L^p and Sobolev spaces and shows path-norm regularized networks achieve minimax-optimal generalization rates with matching lower bounds.