Sentence examples for reduced model size from inspiring English sources

Reduced model size drastically decreased the printing time, which was 3, 10, and 22 h for B25, B50, and B100, respectively.

The advantage of the reduced model size comes at the price of a high computational effort if many simulation runs are necessary, e.g. for parameter estimation.

Furthermore, we evaluated reduced model sizes by stepwise elimination of context positions carrying no or little information for RTP prediction (feature selection).

The release also brings enhancements to reduce model size and ultimately memory demand.

To mitigate this problem, an adaptive floating node method (A-FNM) with potential to reduce model size and computational effort is proposed.

This LRT's null model is formed by imposing a single, non-boundary constraint on the alternative model (i.e., the constraint that ω3 = ω4), reducing model size by one estimated parameter.

In the formulation of the (scaled) reduced model used above, the size of the beta-cell population was assumed to be constant as an approximation, because it is slowly varying.

We used these 10 reduced models to compute the expected model size.

This has the added advantage of significantly reducing the model size to facilitate processing while maintaining image quality and also to reduce image noise.

The reduction of predictive power seen in removing siRNAs from the training set that overlap with the testing set is similar to the reduction of power seen in removing siRNAs in general from the training set, similar to the lower order folds in Table 9, not surprisingly reducing training data set size reduces model performance.

Subsequently, we exhibit an asymmetric encoder-decoder network for reducing the model size even more, unlike the conventional symmetric approach used by the previous methods, e.g., SegNet [8].