mlsanityData to 7 Oct 2026

Long-term Reproducibility for Neural Architecture Search

arXiv 2207.04821 ↗First posted 11 Jul 2022cs.LGcited by 1

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Abstract (arXiv)

It is a sad reflection of modern academia that code is often ignored after publication -- there is no academic 'kudos' for bug fixes / maintenance. Code is often unavailable or, if available, contains bugs, is incomplete, or relies on out-of-date / unavailable libraries. This has a significant impact on reproducibility and general scientific progress. Neural Architecture Search (NAS) is no exception to this, with some prior work in reproducibility. However, we argue that these do not consider long-term reproducibility issues. We therefore propose a checklist for long-term NAS reproducibility. We evaluate our checklist against common NAS approaches along with proposing how we can retrospectively make these approaches more long-term reproducible.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 20 references that are in the map. First posted on arXiv 11 Jul 2022.

Builds on

16 of 20 references.

References · 16

  1. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  2. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  3. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  4. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
  5. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 666
  6. 2020Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)cited by 318
  7. 2019PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Searchcited by 265
  8. 2019Random Search and Reproducibility for Neural Architecture Searchcited by 253
  9. 2020Neural Architecture Search without Trainingcited by 241
  10. 2018Efficient Multi-objective Neural Architecture Search via Lamarckian Evolutioncited by 151
  11. 2019Blockwisely Supervised Neural Architecture Search with Knowledge Distillationcited by 81
  12. 2020DrNAS: Dirichlet Neural Architecture Searchcited by 73
4 more
  1. 2020Neural Architecture Transfercited by 59
  2. 2019Best Practices for Scientific Research on Neural Architecture Searchcited by 53
  3. 2020Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Prunerscited by 5
  4. 2022SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metricscited by 1

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Citing papers per year: 2023: 1.

1 paper.

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