Benchmarks
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- Evaluation Measures - Classification Performance - Accuracy, Precision & Recall (Sensitivity), and Specificity
- Datasets
- Gaming
- Machine Learning Benchmarks and AI Self-Driving Cars | Lance Eliot - AItrends
- Benchmarking simple models with feature extraction against modern black-box methods | Martin Dittgen - Towards Data Science
- DAWNBench | Stanford - an End-to-End Deep Learning Benchmark and Competition
- Benchmarking 20 Machine Learning Models Accuracy and Speed | Marc Borowczak - Data Science Central
- Benchmarking deep learning models on large healthcare datasets | S. Purushotham, C. Meng, Z. Chea, and Y. Liu
GLUE
The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems. GLUE consists of: A benchmark of nine sentence- or sentence-pair language understanding tasks built on established existing datasets and selected to cover a diverse range of dataset sizes, text genres, and degrees of difficulty, A diagnostic dataset designed to evaluate and analyze model performance with respect to a wide range of linguistic phenomena found in natural language, and A public leaderboard for tracking performance on the benchmark and a dashboard for visualizing the performance of models on the diagnostic set.
MLPerf
- MLPerf benchmarks for measuring training and inference performance of ML hardware, software, and services.
