Sentence examples for smooth regularization from inspiring English sources

Neighborhood alignment is performed by adding piece-wise smooth regularization constraints to an energy function.

These discrepancies between the estimations and the true value result from excessive homogenization of the original heterogeneity of the slip area, which in turn results from the smooth regularization.

G-Lasso outperforms SIOL and mtlasso2G, indicating that the graph regularizer provides a smoother regularization than the hard clustering based penalty.

Non-smooth regularization terms are introduced to tradeoff network performance with sparsity of the feedback-gain matrix.

(1.2) The smoothing regularization problem (1.1) has numerous applications in many fields, including mathematical programs with vanishing constraints [2], maximum-likelihood estimation problem [3], language modeling [4], and so on.

To tackle the problem of noisy and incomplete prior networks, we exploit the duality between learning the associations and refining the prior networks to achieve smoother regularization.

Compared with the previous group penalty-based methods, our method does not need to pre-cluster the networks and thus may obtain smoother regularization.

A relatively large smoothing regularization term (λ = 0.1) is used so that the 3-D spline-fit surface thus created is relatively smooth and approximates the overall curvature of the retinal surfaces seen in the B f -scans.

Accordingly, phase stability profiles were constructed between 0 and 20 Hz in 0.1 Hz increments, applying 1 Hz smoothing regularization, for each stimulation block, and compared with analogously constructed profiles for the sham stimulation condition.

For the ring-shaped slip distribution test using the smoothness regularization, the estimated spatial distribution showed a wider slip area with a smoother boundary compared to the true slip distribution (Fig. 4a and b).

To quantify the improvement in the smoothness of the CGM time series thanks to the denoising module, we resorted to the energy of the second order differences (ESOD) of the CGM time series, a quantity widely used in the smoothing and regularization signal-processing literature to evaluate the smoothness of a profile (26, 34).