The data analysis of the paper. Fine-grained Benchmark Subsetting for System Selection. Is available as a reproducible IPython Notebook. The notebook can be visualized. So you can regenerate the data analysis on your own computer.
http://benchmark-subsetting.github.io/
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benchmark-subsetting.github.io | benchmark-subsetting.github.io Reviews
https://benchmark-subsetting.github.io
The data analysis of the paper. Fine-grained Benchmark Subsetting for System Selection. Is available as a reproducible IPython Notebook. The notebook can be visualized. So you can regenerate the data analysis on your own computer.
benchmark-subsetting.github.io
Fine-grained Benchmark Subsetting
http://benchmark-subsetting.github.io/fgbs
Fine-grained Benchmark Subsetting for System Selection. P de Oliveira Castro, Y. Kashnikov, C. Akel, M. Popov, W. Jalby. Université de Versailles St-Quentin-en-Yvelines. Implementation of the Clustering and Prediction Methodology. Example: Using the Prediction Matrix. Information for Reproducing NAS Benchmarking. Function to generate the random clusterings in figure 7. Genetic Algorithm exploration for selecting the features. Allows to reproduce the results of the paper. PDF OUTPUT DIR -. In this section...
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benchmark-subsetting.github.io
benchmark-subsetting.github.io
The data analysis of the paper. Fine-grained Benchmark Subsetting for System Selection. Is available as a reproducible IPython Notebook. The notebook can be visualized. So you can regenerate the data analysis on your own computer.
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