Presents an active-sampling method that approximates the weight subspace from Hessian finite differences, recovers the rank-1 tensors by robust nonlinear programming, and attributes layers with gradient descent, yielding stable recovery under a-posteriori verifiable conditions.
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Robust and Resource Efficient Identification of Two Hidden Layer Neural Networks
Presents an active-sampling method that approximates the weight subspace from Hessian finite differences, recovers the rank-1 tensors by robust nonlinear programming, and attributes layers with gradient descent, yielding stable recovery under a-posteriori verifiable conditions.