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The sparse autoencoder is trained using a large set of true disparities.

Each combination of these parameters is analyzed using a large set of (random) microstructures.

Using a large set of templates can be computationally expensive but still cannot guarantee to detect all cavities.

Using a large set of experimentally confirmed eukaryotic N-glycoproteins we analyzed the relative position and distribution of sequons.

The multi-level hierarchy is designed and constructed using a large set of signature-based reasoning and grading mechanisms.

This is a statistical tool designed to match "treated" individuals (early school leavers) and "control" individuals (high school graduates) using a large set of observables.

We mimicked olfactory coding by modeling responses of primary olfactory neurons to small molecules using a large set of physicochemical molecular descriptors and artificial neural networks.

The first one was carried out by using a large set of faces (more than 55,000) built by combining images available from different public datasets.

To address these problems, we conduct human subject studies using a large set of super-resolution images and propose a no-reference metric learned from visual perceptual scores.

The VISICON solution has been validated by using a large set of data and statistical analysis for the detection of joint buttons and for their quality assessment.

It then tests these models against the known behavior of the target system using a large set of more-general mathematical rules.

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