Semantic Code Classification for Automated Machine Learning
Machine Learning
2022-01-28 v1 Artificial Intelligence
Abstract
A range of applications for automatic machine learning need the generation process to be controllable. In this work, we propose a way to control the output via a sequence of simple actions, that are called semantic code classes. Finally, we present a semantic code classification task and discuss methods for solving this problem on the Natural Language to Machine Learning (NL2ML) dataset.
Cite
@article{arxiv.2201.11252,
title = {Semantic Code Classification for Automated Machine Learning},
author = {Polina Guseva and Anastasia Drozdova and Natalia Denisenko and Daria Sapozhnikova and Ivan Pyaternev and Anna Scherbakova and Andrey Ustuzhanin},
journal= {arXiv preprint arXiv:2201.11252},
year = {2022}
}
Comments
15 pages including references, New In ML workshop at NeurIPS'21