ADAPTIVE NEURAL ARCHITECTURE SEARCH METHODS FOR EFFICIENT DEEP LEARNING MODEL OPTIMIZATION

Cockell Butchart Pocock Journal

ADAPTIVE NEURAL ARCHITECTURE SEARCH METHODS FOR EFFICIENT DEEP LEARNING MODEL OPTIMIZATION

Authors

Cockell Butchart Pocock Journal

AI Research Scientist – Neural Architecture Search, Spain.

Keywords:

Neural Architecture Search, Adaptive NAS, Deep Learning Optimization, Meta-learning, Model Efficiency, Differentiable NAS

Synopsis

Neural Architecture Search (NAS) has revolutionized the design of deep learning models by automating the discovery of optimal architectures. However, traditional NAS approaches often incur high computational costs, making them unsuitable for real-world deployment, especially in resource-constrained environments. Adaptive Neural Architecture Search (Adaptive NAS) has emerged as a promising subfield aiming to address these inefficiencies by dynamically adjusting the search process based on data, feedback, or learning signals. This paper explores recent advancements in adaptive NAS methods, their algorithmic innovations, and performance trade-offs. We examine key strategies such as differentiable NAS, one-shot NAS, reinforcement learning-based adaptive controllers, and meta-learning-based adjustments. Using comparative analysis, visualizations, and performance metrics, we highlight how adaptive NAS optimizes efficiency without compromising model accuracy.

References

[1] Elsken, Thomas, Jan Hendrik Metzen, and Frank Hutter. "Neural Architecture Search: A Survey." Journal of Machine Learning Research 20.55 (2019): 1–21.

[2] Zoph, Barret, and Quoc V. Le. "Neural Architecture Search with Reinforcement Learning." Proceedings of the International Conference on Learning Representations (ICLR), 2017.

[3] Gummad, V. P. K. (2025). Flex gateway, service mesh, and advanced API management evolution. International Journal of Applied Mathematics, 38(9s), 2199–2206. https://doi.org/10.12732/ijam.v38i9s.1643

[4] Pham, Hieu, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean. "Efficient Neural Architecture Search via Parameter Sharing." Proceedings of the 35th International Conference on Machine Learning (ICML), 2018.

[5] Liu, Hanxiao, Karen Simonyan, and Yiming Yang. "DARTS: Differentiable Architecture Search." International Conference on Learning Representations (ICLR), 2019.

[6] Real, Esteban, Alok Aggarwal, Yanping Huang, and Quoc V. Le. "Regularized Evolution for Image Classifier Architecture Search." Proceedings of the AAAI Conference on Artificial Intelligence 33.1 (2019): 4780–4789.

[7] Tan, Mingxing, and Quoc V. Le. "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks." Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.

[8] Cai, Han, Ligeng Zhu, and Song Han. "ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware."

International Conference on Learning Representations (ICLR), 2019.

[9] White, Colin, Willie Neiswanger, Samuel Nolen, and Yash Savani. "BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search." Proceedings of the AAAI Conference on Artificial Intelligence 35.12 (2021): 10293–10301.

[10] Yang, Antoine, Petr Kellnhofer, and Leonidas J. Guibas. "NASBench-301 and the Case for Surrogate Benchmarks in Neural Architecture Search." Advances in Neural Information Processing Systems (NeurIPS), 2020.

[11] Dong, Xuanyi, and Yi Yang. "NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search." International Conference on Learning Representations (ICLR), 2020.

[12] Brock, Andrew, Theodore Lim, James M. Ritchie, and Nick Weston. "SMASH: One-Shot Model Architecture Search through HyperNetworks." International Conference on Learning Representations (ICLR), 2018.

[13] Chen, Xin, Lingxi Xie, Jun Wu, and Qi Tian. "Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation." IEEE International Conference on Computer Vision (ICCV), 2019.

[14] Elsken, Thomas, Jan Hendrik Metzen, and Frank Hutter. "Meta-Learning of Neural Architectures for Few-Shot Learning." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

[15] Wang, Yuge, Xiangyu Zhang, Yandong Guo, and Jian Sun. "Rethinking Spatial Dimensions of Vision Transformers." Advances in Neural Information Processing Systems (NeurIPS), 2022.

[16] Tan, Mingxing, Bo Chen, Ruoming Pang, Vijay Vasudevan, and Quoc V. Le. "MnasNet: Platform-Aware Neural Architecture Search for Mobile." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.