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Shallow neural network yields regularization for ill-posed inverse problems
In this paper, we develop regularization theory for neural network approximations of general ill-posed operator equations with noisy data. Within the framework of iterative regularization, we propose two expanding neural network (ENN) based schemes under different a priori assumptions. Instead of pr
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