Difference between revisions of "Benchmarks"
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* [[Evaluation Measures - Classification Performance]] - [[Evaluation Measures - Classification Performance#Accuracy|Accuracy]], [[Evaluation Measures - Classification Performance#Precision & Recall (Sensitivity)|Precision & Recall (Sensitivity)]], and [[Evaluation Measures - Classification Performance#Specificity|Specificity]] | * [[Evaluation Measures - Classification Performance]] - [[Evaluation Measures - Classification Performance#Accuracy|Accuracy]], [[Evaluation Measures - Classification Performance#Precision & Recall (Sensitivity)|Precision & Recall (Sensitivity)]], and [[Evaluation Measures - Classification Performance#Specificity|Specificity]] | ||
* [[Datasets]] | * [[Datasets]] | ||
| − | * [[Gaming]] | + | * [[Case Studies]] |
| + | ** [[Gaming]] | ||
* [http://www.aitrends.com/ai-insider/machine-learning-benchmarks-and-ai-self-driving-cars/ Machine Learning Benchmarks and AI Self-Driving Cars | Lance Eliot - AItrends] | * [http://www.aitrends.com/ai-insider/machine-learning-benchmarks-and-ai-self-driving-cars/ Machine Learning Benchmarks and AI Self-Driving Cars | Lance Eliot - AItrends] | ||
* [http://towardsdatascience.com/benchmarking-simple-machine-learning-models-with-feature-extraction-against-modern-black-box-80af734b31cc Benchmarking simple models with feature extraction against modern black-box methods | Martin Dittgen - Towards Data Science] | * [http://towardsdatascience.com/benchmarking-simple-machine-learning-models-with-feature-extraction-against-modern-black-box-80af734b31cc Benchmarking simple models with feature extraction against modern black-box methods | Martin Dittgen - Towards Data Science] | ||
Revision as of 14:49, 31 December 2019
YouTube search... ...Google search
- Evaluation Measures - Classification Performance - Accuracy, Precision & Recall (Sensitivity), and Specificity
- Datasets
- Case Studies
- 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
Contents
GLUE
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)
ReAding Comprehension (RACE)
MLPerf
- MLPerf benchmarks for measuring training and inference performance of ML hardware, software, and services.
Procgen
- 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.
