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AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
[article]
2022
arXiv
pre-print
Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecting ML models/algorithms. Central to this drive is the appeal of engineering a computational system that both discovers and deploys high-performance solutions to arbitrary ML problems with minimal human interaction. Beyond this, an even loftier goal is the pursuit of autonomy,
arXiv:2012.12600v2
fatcat:6rj4ubhcjncvddztjs7tql3itq