Prior Knowledge Guided Neural Architecture Generation

Jingrong Xie, Han Ji, Yanan Sun
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:68671-68686, 2025.

Abstract

Automated architecture design methods, especially neural architecture search, have attracted increasing attention. However, these methods naturally need to evaluate numerous candidate architectures during the search process, thus computationally extensive and time-consuming. In this paper, we propose a prior knowledge guided neural architecture generation method to generate high-performance architectures without any search and evaluation process. Specifically, in order to identify valuable prior knowledge for architecture generation, we first quantify the contribution of each component within an architecture to its overall performance. Subsequently, a diffusion model guided by prior knowledge is presented, which can easily generate high-performance architectures for different computation tasks. Extensive experiments on new search spaces demonstrate that our method achieves superior accuracy over state-of-the-art methods. For example, we only need $0.004$ GPU Days to generate architecture with $76.1%$ top-1 accuracy on ImageNet and $97.56%$ on CIFAR-10. Furthermore, we can find competitive architecture for more unseen search spaces, such as TransNAS-Bench-101 and NATS-Bench, which demonstrates the broad applicability of the proposed method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-xie25f, title = {Prior Knowledge Guided Neural Architecture Generation}, author = {Xie, Jingrong and Ji, Han and Sun, Yanan}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {68671--68686}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/xie25f/xie25f.pdf}, url = {https://proceedings.mlr.press/v267/xie25f.html}, abstract = {Automated architecture design methods, especially neural architecture search, have attracted increasing attention. However, these methods naturally need to evaluate numerous candidate architectures during the search process, thus computationally extensive and time-consuming. In this paper, we propose a prior knowledge guided neural architecture generation method to generate high-performance architectures without any search and evaluation process. Specifically, in order to identify valuable prior knowledge for architecture generation, we first quantify the contribution of each component within an architecture to its overall performance. Subsequently, a diffusion model guided by prior knowledge is presented, which can easily generate high-performance architectures for different computation tasks. Extensive experiments on new search spaces demonstrate that our method achieves superior accuracy over state-of-the-art methods. For example, we only need $0.004$ GPU Days to generate architecture with $76.1%$ top-1 accuracy on ImageNet and $97.56%$ on CIFAR-10. Furthermore, we can find competitive architecture for more unseen search spaces, such as TransNAS-Bench-101 and NATS-Bench, which demonstrates the broad applicability of the proposed method.} }
Endnote
%0 Conference Paper %T Prior Knowledge Guided Neural Architecture Generation %A Jingrong Xie %A Han Ji %A Yanan Sun %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-xie25f %I PMLR %P 68671--68686 %U https://proceedings.mlr.press/v267/xie25f.html %V 267 %X Automated architecture design methods, especially neural architecture search, have attracted increasing attention. However, these methods naturally need to evaluate numerous candidate architectures during the search process, thus computationally extensive and time-consuming. In this paper, we propose a prior knowledge guided neural architecture generation method to generate high-performance architectures without any search and evaluation process. Specifically, in order to identify valuable prior knowledge for architecture generation, we first quantify the contribution of each component within an architecture to its overall performance. Subsequently, a diffusion model guided by prior knowledge is presented, which can easily generate high-performance architectures for different computation tasks. Extensive experiments on new search spaces demonstrate that our method achieves superior accuracy over state-of-the-art methods. For example, we only need $0.004$ GPU Days to generate architecture with $76.1%$ top-1 accuracy on ImageNet and $97.56%$ on CIFAR-10. Furthermore, we can find competitive architecture for more unseen search spaces, such as TransNAS-Bench-101 and NATS-Bench, which demonstrates the broad applicability of the proposed method.
APA
Xie, J., Ji, H. & Sun, Y.. (2025). Prior Knowledge Guided Neural Architecture Generation. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:68671-68686 Available from https://proceedings.mlr.press/v267/xie25f.html.

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