Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization
Not on the map. Why?
Abstract (arXiv)
The paper provides a comprehensive overview of Neural Architecture Search (NAS), emphasizing its evolution from manual design to automated, computationally-driven approaches. It covers the inception and growth of NAS, highlighting its application across various domains, including medical imaging and natural language processing. The document details the shift from expert-driven design to algorithm-driven processes, exploring initial methodologies like reinforcement learning and evolutionary algorithms. It also discusses the challenges of computational demands and the emergence of efficient NAS methodologies, such as Differentiable Architecture Search and hardware-aware NAS. The paper further elaborates on NAS's application in computer vision, NLP, and beyond, demonstrating its versatility and potential for optimizing neural network architectures across different tasks. Future directions and challenges, including computational efficiency and the integration with emerging AI domains, are addressed, showcasing NAS's dynamic nature and its continued evolution towards more sophisticated and efficient architecture search methods.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 30 references that are in the map. First posted on arXiv 11 Feb 2024.
Builds on
17 of 30 references.
References · 17
- 2014Going Deeper with Convolutionscited by 4,231
- 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 340
- 2015LSTM: A Search Space Odysseycited by 315
- 2019Random Search and Reproducibility for Neural Architecture Searchcited by 253
- 2019Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Searchcited by 129
- 2019DARTS+: Improved Differentiable Architecture Search with Early Stoppingcited by 105
- 2020A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithmscited by 9
- 2022A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalitiescited by 6
- 2018Searching Toward Pareto-Optimal Device-Aware Neural Architecturescited by 5
- 2021Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-levelcited by 5
- 2023Sentiment analysis with adaptive multi-head attention in Transformercited by 5
- 2020Superkernel Neural Architecture Search for Image Denoisingcited by 4
5 more
- 2021A Novel Framework for Neural Architecture Search in the Hill Climbing Domaincited by 2
- 2023FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregationcited by 2
- 2023Joint Detection Algorithm for Multiple Cognitive Users in Spectrum Sensingcited by 1
- 2023Sample-based Dynamic Hierarchical Transformer with Layer and Head Flexibility via Contextual Banditcited by 1
- 2023Optimizing the Passenger Flow for Airport Security Checkcited by 1
Built on by
Citing papers per year: 2025: 1; 2026: 2.
3 papers.