Most neural architectures admit a GSVD representation making the nonlinear portion left-invertible and norm-preserving before the final linear layer.
Withp= 2, the construction in Algorithm 1 yields singular values: σ1 = 10 r 2 0.9 ≈14.91, σ 2 = 0.5 r 2 0.9 ≈0.745
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A Generalized Singular Value Theory for Neural Networks
Most neural architectures admit a GSVD representation making the nonlinear portion left-invertible and norm-preserving before the final linear layer.