Neural ODE flow maps composed with embedding and projection yield shallow networks with universal approximation in C^0, preserved under separate Lipschitz or norm constraints but with quantified loss when both are imposed.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.NA 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Approximation properties of neural ODEs
Neural ODE flow maps composed with embedding and projection yield shallow networks with universal approximation in C^0, preserved under separate Lipschitz or norm constraints but with quantified loss when both are imposed.