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We evaluate our approach on two well-known gender benchmarks, showing that our Local-DNN outperforms other deep learning methods also evaluated and obtains state-of-the-art results in both benchmarks.
There are benchmarks showing that Hadoop is capable of a very large amount of efficient data processing.
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Computational experiments based on 7 sets of 659 popular benchmarks show that TPS produces highly competitive results compared to the best performing approaches in the literature.
Empirical evidence on a variety of benchmarks shows that both methods are competitive.
Extensive experiments on CAD models from various benchmarks show that the algorithm is robust.
Results on well-known benchmarks show that both these techniques produce high quality solutions.
Extensive experiments on these benchmarks show that our method can achieve state-of-the-art results.
The government will reimburse them several years later, possibly with a bonus — but only if agreed-upon benchmarks show that the program is working.
Results on common Haplotype Inference benchmarks show that the approach achieves a good trade-off between solution quality and execution time.
Computational results based on 240 benchmarks show that BLS produces competitive results with respect to several previous approaches.
Macworld's speed benchmarks show that apart from the improved graphics performance, the new model is "roughly the same speed as the old one".
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com