Approximation capabilities of measure-preserving neural networks
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Measure-preserving neural networks are well-developed invertible models, however, the approximation capabilities remain unexplored. This paper rigorously establishes the general sufficient conditions for approximating measure-preserving maps using measure-preserving neural networks. It is shown that for compact U ⊂^D with D≥ 2, every measure-preserving map ψ: U→^D which is injective and bounded can be approximated in the L^p-norm by measure-preserving neural networks. Specifically, the differentiable maps with ± 1 determinants of Jacobians are measure-preserving, injective and bounded on U, thus hold the approximation property.
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