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As expected, all methods perform well when the SNR is high, but exhibit marked differences in performance for finite SNR.
In 'Flixster', all the methods perform well.
Though these methods perform well in practice, they use a linear structure.
We then show that our methods perform well for sparse data sets.
Giraitis et al. (2013) find that such methods perform well in forecasting several US macroeconomic series.
All methods perform well when a diverse set of ligands which covers a range of pKa values is considered.
While such patch-based methods perform well in noise reduction, most are not capable of addressing blur and noise jointly.
By providing an accurate depth map, the LMH and GP methods perform well in both precision and recall.
Most of the counterpart methods perform well when there are no pathological regions or exudates in retinal images.
It is found that both asymptotic methods perform well for small sample sizes despite being approximate procedures.
However, these methods perform well on the images without noise, and their results on the noisy images are not good.
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