Orthogonal greedy training of shallow ReLU networks is adapted to kernel estimation for linear operators, with stated convergence rates and large accuracy gains over neural operator baselines.
Stuart, and Anima Anandkumar
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Orthogonal greedy algorithm for linear operator learning with shallow neural network
Orthogonal greedy training of shallow ReLU networks is adapted to kernel estimation for linear operators, with stated convergence rates and large accuracy gains over neural operator baselines.