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Fully-converged ("implicit") coupling with Newton's method typically outperforms its Picard counterpart, especially at high noise levels.
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Some methods combine more than one approach where the combined methods typically outperform individual approaches.
As before, when the ICC was very small (that is, most of the variability was within clusters), the double bootstrap method typically only outperformed the robust method when the within-cluster variance increased.
The resulting generator is provably efficient, and typically outperforms the baseline algorithm.
While the direct encoding performs similarly well to the proposed method, indirect-based results typically outperform the direct-based results in aspects not directly linked to the nature of the problem itself, but to the emergence of properties found in biological organisms, like organicity, generalization capacity, or modularity aspects which are highly valuable in engineering.
Because our focus is on ensemble methods and ensembles of classifiers typically outperform single classifiers, the lower bound for our approaches is an ensemble of supervised classifiers.
Since the basic classification unit is a sentence fragment with a few words, leading to sparse representation, we use support vector machines (SVMs), which work well on sparse data and typically outperform other text classification methods.
Selfish incentives typically outperform prosocial incentives.
Students in the New Tech schools typically outperform comparable schools in standardized tests.
For high current pulsed applications the bi-material electrodes typically outperform both the battery and the capacitor electrode.
Megamalls typically outperform their 800 slightly smaller, "regional" counterparts, though size and financial health don't overlap entirely.
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