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Finally, for both models, we evaluate our algorithms on pairs of plant genomes.
Using these models, we evaluate flexibility and design trade-offs of the presented enabling technologies with other key performance indicators such as spectral efficiency, lightpath reach, total capacity, normalized cost, connectivity and others.
By combining Bidirectional Long Short-Term Memory and Conditional Random Field (BLSTM-CRF) models, we evaluate our model on in-domain and out-of-domain datasets, and in both cases achieve (or close to) state-of-the-art results on CCG supertagging task.
With the trained models, we evaluate our method on the test data from both HumanEva and IXMAS datasets.
Based on our analytical models, we evaluate the delay and throughput performance of the real-time applications under various traffic load conditions and system parameter changes.
To test the modeling capabilities of the constructed language models, we evaluate perplexities on the test text in comparison with their perplexity on the training text.
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To compare the 2 lactation models, we evaluated the goodness of fit using 6 evaluation criteria.
Using simple empirical degradation models, we evaluated the effect of the crystallinity and the hydrophilicity.
For each of those models, we evaluated some metrics to compare our proposed protocol and other key management solutions.
Using propensity-score-weighted Poisson models, we evaluated 1-year cardiovascular (CV) outcome incidence among initiators of DPP-4 inhibitors versus comparators in subgroups with and without concurrent metformin use, and assessed the interaction between initiation drug and metformin.
The H4-APP cells and PSAPP mouse models we evaluated emulate the APP-induced amyloidogenic effects seen in individuals with trisomy 21.
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