Benchmarks

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• Evaluation
  • Evaluation - Measures
    • Accuracy
    • Precision & Recall (Sensitivity)
    • Specificity
  • Bias and Variances
  • Explainable Artificial Intelligence (XAI)
  • Train, Validate, and Test
  • Model Monitoring
• Datasets
• Case Studies
  • 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

General Language Understanding Evaluation (GLUE)

• General Language Understanding Evaluation (GLUE)
• Natural Language Processing (NLP)

The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems... like picking out the names of people and organizations in a sentence and figuring out what a pronoun like “it” refers to when there are multiple potential antecedents. 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.

The Stanford Question Answering Dataset (SQuAD)

• The Stanford Question Answering Dataset (SQuAD)

ReAding Comprehension (RACE)

• ReAding Comprehension {RACE)

MLPerf

• MLPerf benchmarks for measuring training and inference performance of ML hardware, software, and services.

Procgen

• OpenAI
• OpenAI’s Procgen Benchmark prevents AI model overfitting | Kyle Wiggers - VentureBeat a set of 16 procedurally generated environments that measure how quickly a model learns generalizable skills. It builds atop the startup’s CoinRun toolset, which used procedural generation to construct sets of training and test levels.

OpenAI previously released Neural MMO, a “massively multiagent” virtual training ground that plops agents in the middle of an RPG-like world, and Gym, a proving ground for algorithms for reinforcement learning (which involves training machines to do things based on trial and error). More recently, it made available SafetyGym, a suite of tools for developing AI that respects safety constraints while training, and for comparing the “safety” of algorithms and the extent to which those algorithms avoid mistakes while learning.