Over-parameterized shallow neural operators trained by gradient descent converge linearly to the global minimum of the empirical loss under mild sample conditions.
Optimizatio n for neural operator learn- ing: Wider networks are better
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Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel
Over-parameterized shallow neural operators trained by gradient descent converge linearly to the global minimum of the empirical loss under mild sample conditions.