Transformers and generalized neural integral operators are shown to universally approximate operators between Hölder and Banach spaces.
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cs.LG 2years
2024 2verdicts
UNVERDICTED 2representative citing papers
DeepONet learns the operator-to-function map from N-t-D data to conductivities in EIT, supported by a universal approximation theorem and numerical outperformance of IRGN.
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Universal Approximation of Operators with Transformers and Neural Integral Operators
Transformers and generalized neural integral operators are shown to universally approximate operators between Hölder and Banach spaces.
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A DeepONet for inverting the Neumann-to-Dirichlet Operator in Electrical Impedance Tomography: An approximation theoretic perspective and numerical results
DeepONet learns the operator-to-function map from N-t-D data to conductivities in EIT, supported by a universal approximation theorem and numerical outperformance of IRGN.