Sentence examples for models can extract from inspiring English sources

Different from traditional methods, our color and texture models can extract robust information that is insensitive to illumination variations.

In fact, hybrid wavelet-artificial intelligence models can extract the characteristics of the original signals (i.e. model inputs) accurately through decomposing the non-stationary and complex signals into several stationary and simpler signals.

The resulting structure of the PLS model can extract the strongest relationship between the input and the output variables.

Through pre-training the multi-group descriptors discriminatively, our model can extract specific aesthetic features for various scenes, and reduce the impact of noise data when building the model.

We fit our model to a very large data set to explore whether our model can extract the unique impact of demographics and those of the two types of mail on the two aspects of donation behavior.

Experimental results demonstrate that the proposed background updating method can update the background exactly and rapidly along with the variance of illumination, the self-adaptive thresholding segmentation method based on the Gaussian model can extract the moving object regions accurately and completely, and the shadow can be eliminated accurately.

The experimental results proved that our WELM LAG model can extract the hidden key information beyond the sequence itself and obtain much better prediction results than previous method.

Although the RSF model can extract part of the objects accurately, it fails to segment the white disc and is sensitive to noise (see Figure 4c).

Also, by dividing total signal into these four distinct classes, it is possible the model can extract more information and produce higher accuracy in individual event calls.

When combining such boundary probes together with mid-nucleosome probes, as done by Yuan et al. we get a wide distribution of probe values, which reduces the information the model can extract from these values.

A nascent line of work in this area aims to use unsupervised learning methods on abundant unannotated text (e.g., Kiros et al. '15, Hill et al. '16, Peters et al. '17) or supervised learning methods on data-rich tasks like translation (e.g., Conneau et al. '17, McCann et al. '17) to train compositional encoder models that can extract general-purpose distributed representations for sentences.