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Discover LudwigThe phrase "a significantly different performance" is correct and usable in written English.
It can be used when comparing the outcomes or results of different performances, highlighting a notable distinction between them.
Example: "After analyzing the results, we found that the new strategy led to a significantly different performance compared to the previous approach."
Alternatives: "a markedly different performance" or "a notably different performance".
Exact(1)
Remarkably, despite being very similar, the analyzed coatings produce a significantly different performance, especially when data dispersion is incorporated and design limits are considered.
Similar(59)
Shortly after the state report's publication, National Grid filed a rate case proposing significantly different performance incentives than those recommended by the state's report.
The EFMT and CT groups did not demonstrate significantly different numbers of sessions completed (EFMT: 14.46 (SD = 5.27), CT: 14.91 (4.33); t(46) = 0.318, p = 0.752) or significantly different performance on the training task (mean N-level achieved; EFMT: 3.37 (1.73), CT: 4.40 (2.03); t(46) = 1.892, p = .065) although the CT group appeared to show an estimated greater performance on the CT task.
For a therapeutic product to be effective, it is important that it is in its native form as slight modifications have been known to result in significantly different performance in the clinic.
Different settings of the AMG and RAS methods, such as a grid coarsening strategy, interpolation and smoothing in the AMG method, and a subdomain solver and an overlap level in the RAS method, as well as different CPR-type preconditioners, yield significantly different performance.
In other words, generally, students from different schools demonstrated significantly different performance on the items.
Majority of the algorithms could find the optimum; however, they showed significantly different performance.
If NCL-NNE exhibits significantly different performance in the cross-validation and testing procedure, then its performance highly depends on the training data.
However, building neural networks of high quality requires heavy human effort, since different hyperparameter configurations lead to significantly different performances.
These analyses showed significantly different performances, in terms of per-node throughput and energy consumption rate.
Nevertheless, it must still be noted that there might be pathological examples (see e.g., [37]) for which these algorithms would yield significantly different performances especially during mode transitions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com