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Parameter, Compute and Data Trends in Machine Learning
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CC-BY Epoch
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Welcome to Epoch's dataset of historically significant machine learning systems. It is currently (as of October 2023) maintained by Robi Rahman, robi@epochai.org. This dataset is used in many articles and research projects, including:
Machine Learning Model Sizes and the Parameter Gap
Compute Trends Across Three Eras of Machine Learning and the ML Trends Dashboard
The brief history of artificial intelligence by Our World In Data
The race of the AI labs heats up by The Economist

New machine learning papers and models are periodically added, and the dataset is expanding. If you want to suggest a paper that should be included in the database, you can do so using this form: https://airtable.com/shrM9gmqLu5m9wTNI
Leave a comment if you notice any incorrect data or missing values.

See the tab ALL ML SYSTEMS for the whole dataset, and NOTABLE ML SYSTEMS to find the systems that meet the notability criteria: high citation count, improvement over previous state of the art, or historical significance.

If you have any questions, see the helpful database documentation.
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Acknowledgements:
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These data have been collected by Epoch's employees and collaborators, including Jaime Sevilla, Pablo Villalobos, Juan Felipe Cerón, Matthew Burtell, Lennart Heim, Amogh B. Nanjajjar, Tilman Rauker, Nuño Sempere, Max Rauker, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, Jean-Stanislas Denain, Owen Dudney, David Atkinson, Ben Cottier, David Owen, Robi Rahman, Carl Guo, and Josh You.
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BibTeX for citations:
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@misc{epochMachineLearningData2022,
title = {Parameter, Compute and Data Trends in Machine Learning},
author = {Epoch},
year = {2022},
copyright = {CC-BY},
howpublished = {https://epochai.org/data/pcd},
}
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