… such a benchmarking approach and identifying metrics for the application of machine learning
… Here, we introduce the concept of machine learning benchmarks for science and review …

… with regard prediction error on a suite of benchmark datasets. As the base constituents of
the … of those benchmarking datasets extracted from the UCI machine learning repository for …

… We then surveyed and analyzed machine learning processors categorized into six regions
… Finally, we presented benchmarking results for two low power machine learning accelerator …

… number of established machine learning methods to the entire benchmark suite and analyze
… existing benchmarks lack the diversity to properly benchmark machine learning algorithms, …

… Our findings outline recommendations to improve reliability of machine learning benchmarks:
1) randomize as many sources of variations as possible in the performance estimation; 2) …

… a series of benchmark results of implemented machine learning algorithms … machine learning
models included as part of MoleculeNet. The results section will analyze the benchmarking …

… machine learning applications automatically without extensive knowledge of statistics and
machine learning. … current AutoML methods and a benchmark of popular AutoML frameworks …

… benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning
(… In addition to building the datasets, we also perform extensive benchmark experiments for …

… in these benchmarks’ test data, we find that relative rankings of models in benchmarks
are … However, we find that these benchmark results are unstable: higher-capacity models (like …

… are natural candidates for synthetic benchmark generation. In … of machine learning in the
prominent CLgen benchmark generator. We re-evaluate CLgen by comparing the benchmarks …