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NeuralArTS: Structuring Neural Architecture Search with Type Theory

arXiv 2110.08710 ↗First posted 17 Oct 2021cs.LG cs.LO cs.PLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 5 references that are in the map. First posted on arXiv 17 Oct 2021.

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References · 4

  1. 2016Neural Architecture Search with Reinforcement Learningcited by 1,499
  2. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
  3. 2019Evaluating the Search Phase of Neural Architecture Searchcited by 120
  4. 2021Bag of Tricks for Neural Architecture Searchcited by 4

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