Sentence examples for regularization schemes from inspiring English sources

Corresponding regularization schemes have been proposed to produce stable results.

Many types of regularization schemes have been employed in statistical learning, each one motivated by some assumption about the problem domain.

Given this setup, we develop a framework for low-rank matrix estimation that allows us to transform noise models into regularization schemes via a simple parametric bootstrap.

Although regularization schemes or post signal processing can be used to treat the drifts, they are not suitable for the real-time identification of structural systems and unknown inputs.

The paper provides a comparative investigation on accuracy and conservation properties of two particle regularization schemes, namely, the Dynamic Stabilization (DS) [1] and generalized Particle Shifting (PS) [2] schemes in simulations of both internal and free-surface flows in ISPH (Incompressible SPH) context.

Proof: Two regularization schemes are equivalent if they have the same filter factors.

We empirically explore the power of this regularization scheme for improving training performance in various GAN formulations.

During the optimization, a mesh regularization scheme is employed to avoid excessive mesh distortion, which comes from significant design changes.

The problem adopts convex sparsity-based regularization scheme for decomposition, and non-convex regularization is used to further promote the sparsity but preserving the global convexity.

By reducing the analysis of generic group symmetries to per- mutation symmetries, we devise an analytic expression for a regularization scheme and a permutation invariant metric on the representation space.

The regularization scheme we developed for neural control policies enabled extensive and robust control ability that compares with cutting edge parametric control strategies despite that no preliminary calibration is needed with our method.