Sentence examples for less regularization from inspiring English sources

Since a model with more stable features should require less regularization, we expected that it would be useful to optimize σ and c together.

There we find that our adaptive-basis, fixed-quadrature algorithm uses less regularization than alternatives, although differences in the resulting numerical simulations are more sensitive to the regularization strategy than to the choice of basis.

With improved experimental control, signal acquisition, and imaging apparatus, we anticipate a decreased noise level in the optical signal and, as a result, significantly less regularization will be required in the reconstruction.

This provides slightly less overall regularization as the three priors are not coupled; however, it does not give rise to any overcounting, particularly as the output probabilities are used only as input features for the stage 1 LRC classifiers.

Results show that the Yuan et al. method overcomes some challenges of the original TV model (discourages piecewise constant solutions and is less sensitive to regularization parameters) and outperforms the Li et al. method.

We used the parameters p=0.1 and γ=10−4 as suggested (though we did try other combinations with less success), and regularization weight β∈{0.1,0.3,0.5,1,3,5}, gradient-optimized with learning rate 0.1 and momentum 0.5 over 500 iterations.

Regularization aims to make the problem less complex (more regular), i.e. to ensure the uniqueness of the solution [ 84], to reduce the ill-conditioning and to avoid model overfitting.

The results illustrate that the hybrid solution is a more flexible and accurate approach than the explicit solution based on regularization, and less time consuming than the implicit solution based solely on optimization.

With all of these studies, we can therefore conclude that TV regularization has less falloff, and shows better edge performance.

The proposed CMMS algorithm consumed about 0.06 ms, which is far less than 1 ms the regularization algorithm consumed 0.3 ms; the BGMP algorithm consumed approximately 5 ms; and ℓ1 consumed much more about 28 ms. We can see that the CMMS needs considerably less time than the other algorithms.

If we compare Fig. 5(c) with (e), we see that the reconstruction using the TV regularization presents much less noise residue than using NUDFT, consistent with the earlier experiment.