Neural Architecture Search (NAS) algorithms automate the task of finding optimal deep learning architectures given an initial search space of possible operations. Developing these search spaces is usually a manual affair with pre-optimized search spaces being more efficient, rather than searching from scratch. In this paper we present a new framework called Neural Architecture Type System (NeuralArTS) that categorizes the infinite set of network operations in a structured type system. We further demonstrate how NeuralArTS can be applied to convolutional layers and propose several future directions.
@article{arxiv.2110.08710,
title = {NeuralArTS: Structuring Neural Architecture Search with Type Theory},
author = {Robert Wu and Nayan Saxena and Rohan Jain},
journal= {arXiv preprint arXiv:2110.08710},
year = {2021}
}
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(Student Abstract) In Proceedings of the 36th AAAI Conference on Artificial Intelligence, Vancouver, BC,Canada, 2022