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Fourier Neural Operators with rank-1 lattice points and hyperbolic cross

Announce Type: new Abstract: The \emph{Fourier neural operator} (FNO) is a neural network architecture that learns mappings between function spaces. Its efficient implementation is based on the multi-dimensional Fourier transform. By deriving general regularity bounds for the FNO with respect to both the spatial and parametric variables, we prove that the generalization error of the FNO can be improved by replacing spatial tensor product grids with purpose-built rank-1 lattice points, and by...

arXiv CS 1d ago

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction

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Collision Resistance of Single-Layer Neural Nets

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