AutoML | Literature on Neural Architecture Search

Maintained by Difan Deng and Marius Lindauer.

The following list considers papers related to neural architecture search. It is by no means complete. If you miss a paper on the list, please let us know.

Please note that although NAS methods steadily improve, the quality of empirical evaluations in this field are still lagging behind compared to other areas in machine learning, AI and optimization. We would therefore like to share some best practices for empirical evaluations of NAS methods, which we believe will facilitate sustained and measurable progress in the field. If you are interested in a teaser, please read our blog post or directly jump to our checklist.

Transformers have gained increasing popularity in different domains. For a comprehensive list of papers focusing on Neural Architecture Search for Transformer-Based spaces, the awesome-transformer-search repo is all you need.

4015 entries « ‹ 51 of 81 › »

2021

1515.

Zhang, Kaiyu; Chen, Jinglong; He, Shuilong; Xu, Enyong; Li, Fudong; Zhou, Zitong

Differentiable neural architecture search augmented with pruning and multi-objective optimization for time-efficient intelligent fault diagnosis of machinery Journal Article

In: Mechanical Systems and Signal Processing, vol. 158, pp. 107773, 2021, ISSN: 0888-3270.

Abstract | Links | BibTeX

@article{ZHANG2021107773,

title = {Differentiable neural architecture search augmented with pruning and multi-objective optimization for time-efficient intelligent fault diagnosis of machinery},

author = {Kaiyu Zhang and Jinglong Chen and Shuilong He and Enyong Xu and Fudong Li and Zitong Zhou},

url = {https://www.sciencedirect.com/science/article/pii/S0888327021001680},

doi = {https://doi.org/10.1016/j.ymssp.2021.107773},

issn = {0888-3270},

year  = {2021},

date = {2021-01-01},

journal = {Mechanical Systems and Signal Processing},

volume = {158},

pages = {107773},

abstract = {Intelligent fault diagnosis, which is mainly based on neural network, has been widely used in machinery monitoring. Although such deep learning methods are effective, the new architectures are mainly handcrafted by series of experiments that require ample time and substantial efforts. To automate process of building neural networks and save designing time, a novel differentiable neural architecture search method is proposed. By gradually reducing candidate operations while retaining trained parameters during pruning, computation consumed by each stage of neural architecture search is decreased, which accelerates search process. To improve inferential efficiency of subnetworks, specially designed penalty terms are introduced into the objective function for searching optimal numbers of layers and nodes, which can reduce complexity of subnetworks and save calculation time of signal analysis. In addition, exclusive competition between candidate operations is broken by changing discretization and selection methods of operations, which provides a basis for channel fusion. Effectiveness of the proposed method is verified by two datasets. Experiments show that this method can generate subnetworks of lower complexity and less computational cost than other state-of-art neural architecture search techniques, while achieving competitive result.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

1514.

Liang, Tingting; Wang, Yongtao; Tang, Zhi; Hu, Guosheng; Ling, Haibin

OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection Technical Report

2021.