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Their heuristic derivation involves two approximation steps.
In this article we make these two approximation steps explicit.
In this article we make the two approximation steps explicit.
The (heuristic) derivation of neural field equations involves two approximation steps.
While the underlying theory is completely in line with the principles of Bayesian inference, in practice this property is lost due to approximation steps in the GP inference.
Instead of realizing the double limit as in [13], we split up the limiting procedure, which in particular allows us to insert further approximation steps.
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Clearly, the surfaces which are obtained in this way are only approximately G n -smooth, since an approximation step is performed.
Indeed, an attempt to generalize the techniques [5] to nonlinear invariant subspace Problem 1.3 fails due to the lack of approximation step [5], Statement II.
Since within wKSVD the contribution of the constant low-rank atom counts in the sparse approximation step, we use input sparsity level S=8.
In the global approximation step for the control points our approach couples the standard linear approximation part with the reparameterization to heavily reduce the number of overall steps in the iteration process.
It starts by defining an approximation step Δf=BD/ 2K), where K is an arbitrary integer (here we assume that K is equal to 100), and computes the initial unambiguous shifts f d 1 0 and f d 2 0, respectively.
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