AutoML

Summary: Documentation and files about the AutoML component, as part of the GUT-AI Initiative.

Table of Contents

• About
• Main papers
  • Selected publications
  • References
• Component files
  • Component page
  • Component DOI
  • Component dependencies
  • Environment simulators
  • Datasets
  • Model Zoo
  • Software tools
• Getting involved
• How to cite this
• License

About

The purpose of this component is to perform Automated Machine Learning (AutoML).

Main papers

Selected publications

• Kourouklides, I. (2022). Bayesian Deep Multi-Agent Multimodal Reinforcement Learning for Embedded Systems in Games, Natural Language Processing and Robotics. OSF Preprints. https://doi.org/10.31219/osf.io/sjrkh

References

See References.

Component files

Component page

Thanks to OSF (by the Center for Open Science), the project is temporarily hosted at:

• https://osf.io/fvndu/

Component DOI

Project identifier: https://doi.org/10.17605/OSF.IO/FVNDU

Component dependencies

This component depends on the following components of GUT-AI:

• Automated Data Preprocessing
• NAS

Environment simulators

See Simulators.

Datasets

See Datasets.

Model Zoo

See Model Zoo.

Software tools

See Software tools.

Getting involved

• Community Discord for collaboration and discussion.

How to cite this

If you want to do so, feel free to cite this component in your publications:

@article{kourouklides2022auto_ml,
  author = {Ioannis Kourouklides},
  journal = {OSF Preprints},
  title = {AutoML},
  year = {2022},
  doi = {10.17605/osf.io/fvndu},
  license = {Creative Commons Zero CC0 1.0}
}

License

Creative Commons Zero CC0 1.0 (Public Domain)