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Training with the both regularization methods, a randomly selected subsets of activations/weights are dropped.
We prove that both regularization and iterative methods converge in norm.
They proved that both regularization and iterative methods converge in norm to a solution to MVIP (1.1) under some conditions.
Both regularization techniques are, however, likely to single out a possible mechanism (i.e. diffusion or advection) for the SV generation in the core.
Both regularization schemes, with (alpha ^2 = 6.4cdot 10^{-15}text 10^{-15}textt {nT}cdot) ((L_1)) and (alpha ^2 = 3.6cdotext^{A}^{-2}t {nT}cdot text {and{-2})((L_2)), alphable to produce an extremely good fit to the observed field intensity.
The non-linear estimation rules in (30) and (31) are depicted in Figure 2a,b, respectively, for λ = 2. Observe that both regularization functions effect sparsity, since coefficients with small amplitude are set to 0. The Schwarz-like regularization yields unbiased estimates, but the solution is not continuous with respect to y.
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The proposed algorithm takes the advantages of both regularization-based method and learning-based method.
In the nonword prime condition, by mixing dominant and non-dominant stress items, the proportion of stress errors considerably increased, partially replicating the effects of Experiments 1 and 2. In particular, the manipulation of mixing the stress patterns of both word and nonword primes yielded both "regularizations" and "irregularizations".
Volumetric image reconstruction was performed using both regularizations.
Note that both spatial regularization and temporal regularization have been used in this step, increasing the reliability of the segmentation procedure.
In this paper, we incorporate both Hessian regularization and sparsity constraints into auto-encoders and then propose a new auto-encoder algorithm called Hessian regularized sparse auto-encoders (HSAE).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com