mlsanityData to 14 Oct 2026

Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture

arXiv 2406.15132 ↗First posted 20 Jun 2024cs.LG cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

Designing and optimizing neural network architectures typically requires extensive expertise, starting with handcrafted designs and then manual or automated refinement. This dependency presents a significant barrier to rapid innovation. Recognizing the complexity of automatically generating neural network architecture from scratch, we introduce Younger, a pioneering dataset to advance this ambitious goal. Derived from over 174K real-world models across more than 30 tasks from various public model hubs, Younger includes 7,629 unique architectures, and each is represented as a directed acyclic graph with detailed operator-level information. The dataset facilitates two primary design paradigms: global, for creating complete architectures from scratch, and local, for detailed architecture component refinement. By establishing these capabilities, Younger contributes to a new frontier, Artificial Intelligence-Generated Neural Network Architecture (AIGNNA). Our experiments explore the potential and effectiveness of Younger for automated architecture generation and, as a secondary benefit, demonstrate that Younger can serve as a benchmark dataset, advancing the development of graph neural networks. We release the dataset and code publicly to lower the entry barriers and encourage further research in this challenging area.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 32 of its 41 references that are in the map. First posted on arXiv 20 Jun 2024.

Builds on

32 of 41 references.

References · 32

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  6. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  7. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  8. 2017Inductive Representation Learning on Large Graphscited by 6,320
  9. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,152
  10. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  11. 2018DARTS: Differentiable Architecture Searchcited by 1,798
  12. 2016Variational Graph Auto-Encoderscited by 1,382
20 more
  1. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,101
  2. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,051
  3. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 976
  4. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 943
  5. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 909
  6. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
  7. 2021How Attentive are Graph Attention Networks?cited by 835
  8. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 765
  9. 2017Progressive Neural Architecture Searchcited by 487
  10. 2020NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Searchcited by 416
  11. 2023A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPTcited by 368
  12. 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 344
  13. 2020Design Space for Graph Neural Networkscited by 182
  14. 2020NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Sizecited by 98
  15. 2021Parameter Prediction for Unseen Deep Architecturescited by 89
  16. 2020NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processingcited by 60
  17. 2019Tabular Benchmarks for Joint Architecture and Hyperparameter Optimizationcited by 58
  18. 2022Vision-and-Language Pretrained Models: A Surveycited by 50
  19. 2023DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Modelscited by 16
  20. 2023AGIBench: A Multi-granularity, Multimodal, Human-referenced, Auto-scoring Benchmark for Large Language Modelscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025LEMUR Neural Network Dataset: Towards Seamless AutoMLcited by 20
  2. 2026GraDE: A Graph Diffusion Estimator for Frequent Subgraph Discovery in Neural Architecturescited by 0
  3. 2026On the use of LLMs to generate a dataset of Neural Networkscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026NGN: Learning Neural Network Size as a Differentiable Count
  2. 2024Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization
  3. 2025Intelligent Neural Networks: From Layered Architectures to Graph-Organized Intelligence
  4. 2024Conditional computation in neural networks: principles and research trends
  5. 2024Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models
  6. 2024Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
  7. 2024A Decade of Deep Learning: A Survey on The Magnificent Seven
  8. 2026DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning