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The model was benchmarked using experimental results.
Earlier results are visualised and benchmarked using multiple linear regression.
The code is benchmarked using linear and nonlinear problems.
The algorithm has been benchmarked using a database of 2000 images.
In total, 35 objective metrics were benchmarked using subjective scores as ground truth.
The effectiveness of the proposed method is benchmarked using several antibody antigen PDB complexes.
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Then benchmarking using datasets of various sizes generated by GeneNetWeaver [9] is presented.
Benchmarking using GeneNetWeaver datasets should provide strong evidence of the power of network inference algorithm in a controlled environment.
Results of benchmarks using real workloads are normally quite representative.
Checks are made routinely against benchmarks using classical imaging theory.
Some performance benchmarks using proton antiproton collision data are presented.
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