Exact(2)
We empirically show the quality and robustness of the topological representation of our proposed algorithm using both synthetic and real benchmarks datasets.
When considering the variation benchmarks, datasets should be large enough to cover variations related to a certain feature or mechanism.
Similar(58)
Some benchmark datasets are used to evaluate the proposed algorithm.
We used two benchmark datasets to evaluate our system.
We have conducted the experiments on several benchmark datasets.
This gives researchers quick access to benchmarking datasets like SQuAD, bAbI tasks and WebQuestions.
Algorithms are only as good as their benchmark datasets, and those datasets reflect their creators' biases (conscious or not).
Additionally, we perform an extrinsic evaluation by computing semantic similarity between words in benchmarking datasets.
We then study their behavior through simulations using the MNIST and CIFAR-10 benchmark datasets.
We extensively evaluate our approach on 13 benchmark datasets and a fault diagnosis dataset.
Datasets We have ran experiments on three real and benchmark datasets with different structural properties.
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