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Bayesian learning provides model regularization for NMF.
However, the Tikhonov regularization for traveltime tomography often produces a low-resolution velocity model.
Algorithm 1 A generalized PRSM with LQP regularization for (operatorname{VI}(mathcal{W},Q)).
Regularization for both the first and the second problems is achieved by a Krylov subspace method.
Now we present the generalized PRSM with LQP regularization for solving the Problem (2).
The ℓ2-norm regularization for basis parameters Ar and A h ( l ) was considered.
Fig. 2 The denoised results of TV regularization for noisy piecewise constant functions.
Based on Kuwatani et al. (2014a), we used first-derivative regularization for the smoothness constraint.
Fig. 3 The denoised results of TV regularization for noisy monotonic step image.
A careful analysis shows that a rigorous connection between learning and regularization for inverse problem is not straightforward.
It is claimed that in order to complete the rigorous lattice approach to the chiral anomaly, the heat kernel regularization for the integrand should be introduced independently of the lattice regularization for the path measure.
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