mlsanityData to 7 Oct 2026

Mutation is all you need

arXiv 2107.07343 ↗First posted 4 Jul 2021cs.LG cs.NEcited by 3

Not on the map. Why?

Abstract (arXiv)

Neural architecture search (NAS) promises to make deep learning accessible to non-experts by automating architecture engineering of deep neural networks. BANANAS is one state-of-the-art NAS method that is embedded within the Bayesian optimization framework. Recent experimental findings have demonstrated the strong performance of BANANAS on the NAS-Bench-101 benchmark being determined by its path encoding and not its choice of surrogate model. We present experimental results suggesting that the performance of BANANAS on the NAS-Bench-301 benchmark is determined by its acquisition function optimizer, which minimally mutates the incumbent.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 39 references that are in the map. First posted on arXiv 4 Jul 2021.

Builds on

18 of 39 references.

References · 18

  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. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 902
  5. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
  6. 2017Large-Scale Evolution of Image Classifierscited by 348
  7. 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 340
  8. 2018Neural Architecture Search with Bayesian Optimisation and Optimal Transportcited by 192
  9. 2017Hierarchical Representations for Efficient Architecture Searchcited by 188
  10. 2019BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Searchcited by 175
  11. 2015ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and Rcited by 171
  12. 2018Efficient Multi-objective Neural Architecture Search via Lamarckian Evolutioncited by 151
6 more
  1. 2021How Powerful are Performance Predictors in Neural Architecture Search?cited by 88
  2. 2019Best Practices for Scientific Research on Neural Architecture Searchcited by 53
  3. 2020A Study on Encodings for Neural Architecture Searchcited by 37
  4. 2019BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameterscited by 25
  5. 2021Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimizationcited by 19
  6. 2020A Unifying Framework for Parallel and Distributed Processing in R using Futurescited by 9

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1.

3 papers.

  1. 2023Neural Architecture Search: Insights from 1000 Paperscited by 136
  2. 2022Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammarscited by 9
  3. 2022Tackling Neural Architecture Search With Quality Diversity Optimizationcited by 3