mlsanityData to 7 Oct 2026

Insights from the Use of Previously Unseen Neural Architecture Search Datasets

arXiv 2404.02189 ↗First posted 2 Apr 2024cs.LG cs.AI cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

The boundless possibility of neural networks which can be used to solve a problem -- each with different performance -- leads to a situation where a Deep Learning expert is required to identify the best neural network. This goes against the hope of removing the need for experts. Neural Architecture Search (NAS) offers a solution to this by automatically identifying the best architecture. However, to date, NAS work has focused on a small set of datasets which we argue are not representative of real-world problems. We introduce eight new datasets created for a series of NAS Challenges: AddNIST, Language, MultNIST, CIFARTile, Gutenberg, Isabella, GeoClassing, and Chesseract. These datasets and challenges are developed to direct attention to issues in NAS development and to encourage authors to consider how their models will perform on datasets unknown to them at development time. We present experimentation using standard Deep Learning methods as well as the best results from challenge participants.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 28 of its 38 references that are in the map. First posted on arXiv 2 Apr 2024.

Builds on

28 of 38 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  5. 2016Densely Connected Convolutional Networkscited by 6,691
  6. 2022A ConvNet for the 2020scited by 3,545
  7. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  8. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  9. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  10. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  11. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 937
  12. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
16 more
  1. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 723
  2. 2020NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Searchcited by 412
  3. 2017Large-Scale Evolution of Image Classifierscited by 348
  4. 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 340
  5. 2019PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Searchcited by 265
  6. 2019Random Search and Reproducibility for Neural Architecture Searchcited by 253
  7. 2020Neural Architecture Search without Trainingcited by 241
  8. 2019BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understandingcited by 231
  9. 2023Neural Architecture Search: Insights from 1000 Paperscited by 136
  10. 2019Evaluating the Search Phase of Neural Architecture Searchcited by 120
  11. 2020NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Sizecited by 98
  12. 2020DrNAS: Dirichlet Neural Architecture Searchcited by 73
  13. 2022β-DARTS: Beta-Decay Regularization for Differentiable Architecture Searchcited by 63
  14. 2022NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easycited by 36
  15. 2022An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systemscited by 16
  16. 2020Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Prunerscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2024einspace: Searching for Neural Architectures from Fundamental Operationscited by 5
  2. 2025Transferrable Surrogates in Expressive Neural Architecture Search Spacescited by 5
  3. 2025ONNX-Net: Towards Universal Representations and Instant Performance Prediction for Neural Architecturescited by 1
  4. 2026Agentic Neural Architecture Searchcited by 1
  5. 2025Evolutionary Architecture Search through Grammar-Based Sequence Alignmentcited by 0