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.

1147 entries « ‹ 4 of 23 › »

2021
997.	Mundt, Martin; Pliushch, Iuliia; Ramesh, Visvanathan Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-06788, title = {Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference}, author = {Martin Mundt and Iuliia Pliushch and Visvanathan Ramesh}, url = {https://arxiv.org/abs/2104.06788}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.06788}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.06788 Close
996.	Peter, David; Roth, Wolfgang; Pernkopf, Franz End-to-end Keyword Spotting using Neural Architecture Search and Quantization Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-06666, title = {End-to-end Keyword Spotting using Neural Architecture Search and Quantization}, author = {David Peter and Wolfgang Roth and Franz Pernkopf}, url = {https://arxiv.org/abs/2104.06666}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.06666}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.06666 Close
995.	Tashiro, Kyosuke; Takeda, Koji; Tanaka, Kanji; Tomoe, Hiroki TaylorMade VDD: Domain-adaptive Visual Defect Detector for High-mix Low-volume Production of Non-convex Cylindrical Metal Objects Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-04203, title = {TaylorMade VDD: Domain-adaptive Visual Defect Detector for High-mix Low-volume Production of Non-convex Cylindrical Metal Objects}, author = {Kyosuke Tashiro and Koji Takeda and Kanji Tanaka and Hiroki Tomoe}, url = {https://arxiv.org/abs/2104.04203}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.04203}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.04203 Close
994.	Liu, Aoming; Huang, Zehao; Huang, Zhiwu; Wang, Naiyan Direct Differentiable Augmentation Search Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-04282, title = {Direct Differentiable Augmentation Search}, author = {Aoming Liu and Zehao Huang and Zhiwu Huang and Naiyan Wang}, url = {https://arxiv.org/abs/2104.04282}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.04282}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.04282 Close
993.	Meng, Tianjian; Chen, Xiaohan; Jiang, Yifan; Wang, Zhangyang A Design Space Study for LISTA and Beyond Inproceedings International Conference on Learning Representations, 2021. Links \| BibTeX @inproceedings{<LineBreak>meng2021a, title = {A Design Space Study for LISTA and Beyond}, author = {Tianjian Meng and Xiaohan Chen and Yifan Jiang and Zhangyang Wang}, url = {https://openreview.net/forum?id=GMgHyUPrXa}, year = {2021}, date = {2021-01-01}, booktitle = {International Conference on Learning Representations}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close https://openreview.net/forum?id=GMgHyUPrXa Close
992.	Wang, Chunnan; Chen, Bozhou; Li, Geng; Wang, Hongzhi FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-04141, title = {FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search}, author = {Chunnan Wang and Bozhou Chen and Geng Li and Hongzhi Wang}, url = {https://arxiv.org/abs/2104.04141}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.04141}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.04141 Close
991.	Liu, Yukun; Li, Ta; Zhang, Pengyuan; Yan, Yonghong Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-05390, title = {Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search}, author = {Yukun Liu and Ta Li and Pengyuan Zhang and Yonghong Yan}, url = {https://arxiv.org/abs/2104.05390}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.05390}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.05390 Close
990.	Yu, Kaicheng; é, Ren; Salzmann, Mathieu Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-05309, title = {Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search}, author = {Kaicheng Yu and Ren é and Mathieu Salzmann}, url = {https://arxiv.org/abs/2104.05309}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.05309}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.05309 Close
989.	Ge, Wanying; Panariello, Michele; Patino, Jose; Todisco, Massimiliano; Evans, Nicholas W D Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-03123, title = {Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection}, author = {Wanying Ge and Michele Panariello and Jose Patino and Massimiliano Todisco and Nicholas W D Evans}, url = {https://arxiv.org/abs/2104.03123}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.03123}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.03123 Close
988.	Liu, Hongying; Xu, Derong; Zhu, Tianwen; Shang, Fanhua; Liu, Yuanyuan; Lu, Jianhua; Yang, Ri Graph Convolutional Networks by Architecture Search for PolSAR Image Classification Journal Article Remote Sensing, 13 (7), 2021, ISSN: 2072-4292. Abstract \| Links \| BibTeX @article{rs13071404, title = {Graph Convolutional Networks by Architecture Search for PolSAR Image Classification}, author = {Hongying Liu and Derong Xu and Tianwen Zhu and Fanhua Shang and Yuanyuan Liu and Jianhua Lu and Ri Yang}, url = {https://www.mdpi.com/2072-4292/13/7/1404}, doi = {10.3390/rs13071404}, issn = {2072-4292}, year = {2021}, date = {2021-01-01}, journal = {Remote Sensing}, volume = {13}, number = {7}, abstract = {Classification of polarimetric synthetic aperture radar (PolSAR) images has achieved good results due to the excellent fitting ability of neural networks with a large number of training samples. However, the performance of most convolutional neural networks (CNNs) degrades dramatically when only a few labeled training samples are available. As one well-known class of semi-supervised learning methods, graph convolutional networks (GCNs) have gained much attention recently to address the classification problem with only a few labeled samples. As the number of layers grows in the network, the parameters dramatically increase. It is challenging to determine an optimal architecture manually. In this paper, we propose a neural architecture search method based GCN (ASGCN) for the classification of PolSAR images. We construct a novel graph whose nodes combines both the physical features and spatial relations between pixels or samples to represent the image. Then we build a new searching space whose components are empirically selected from some graph neural networks for architecture search and develop the differentiable architecture search method to construction our ASGCN. Moreover, to address the training of large-scale images, we present a new weighted mini-batch algorithm to reduce the computing memory consumption and ensure the balance of sample distribution, and also analyze and compare with other similar training strategies. Experiments on several real-world PolSAR datasets show that our method has improved the overall accuracy as much as 3.76% than state-of-the-art methods.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Classification of polarimetric synthetic aperture radar (PolSAR) images has achieved good results due to the excellent fitting ability of neural networks with a large number of training samples. However, the performance of most convolutional neural networks (CNNs) degrades dramatically when only a few labeled training samples are available. As one well-known class of semi-supervised learning methods, graph convolutional networks (GCNs) have gained much attention recently to address the classification problem with only a few labeled samples. As the number of layers grows in the network, the parameters dramatically increase. It is challenging to determine an optimal architecture manually. In this paper, we propose a neural architecture search method based GCN (ASGCN) for the classification of PolSAR images. We construct a novel graph whose nodes combines both the physical features and spatial relations between pixels or samples to represent the image. Then we build a new searching space whose components are empirically selected from some graph neural networks for architecture search and develop the differentiable architecture search method to construction our ASGCN. Moreover, to address the training of large-scale images, we present a new weighted mini-batch algorithm to reduce the computing memory consumption and ensure the balance of sample distribution, and also analyze and compare with other similar training strategies. Experiments on several real-world PolSAR datasets show that our method has improved the overall accuracy as much as 3.76% than state-of-the-art methods. Close https://www.mdpi.com/2072-4292/13/7/1404 doi:10.3390/rs13071404 Close
987.	Ganepola, Vayangi Vishmi Vishara; Wirasingha, Torin Automating Generative Adversarial Networks using Neural Architecture Search: A Review Inproceedings 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 577-582, 2021. Links \| BibTeX @inproceedings{9396991, title = {Automating Generative Adversarial Networks using Neural Architecture Search: A Review}, author = {Vayangi Vishmi Vishara Ganepola and Torin Wirasingha}, url = {https://ieeexplore.ieee.org/abstract/document/9396991}, doi = {10.1109/ESCI50559.2021.9396991}, year = {2021}, date = {2021-01-01}, booktitle = {2021 International Conference on Emerging Smart Computing and Informatics (ESCI)}, pages = {577-582}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close https://ieeexplore.ieee.org/abstract/document/9396991 doi:10.1109/ESCI50559.2021.9396991 Close
986.	Zhang, Haichao; Hao, Kuangrong; Gao, Lei; -, Xue; Wei, Bing Enhanced Gradient for Differentiable Architecture Search Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2103-12529, title = {Enhanced Gradient for Differentiable Architecture Search}, author = {Haichao Zhang and Kuangrong Hao and Lei Gao and Xue - and Bing Wei}, url = {https://arxiv.org/abs/2103.12529}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2103.12529}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2103.12529 Close
985.	Wang, Hongbo ; Pan, Xingyu ; Fan, Lingyan ; Zhao, Shuofeng Steganalysis of convolutional neural network based on neural architecture search Journal Article Multimedia Systems, 2021. Links \| BibTeX @article{WangMS2021, title = {Steganalysis of convolutional neural network based on neural architecture search}, author = {Wang, Hongbo and Pan, Xingyu and Fan, Lingyan and Zhao, Shuofeng}, url = {https://doi.org/10.1007/s00530-021-00779-5}, doi = {10.1007/s00530-021-00779-5}, year = {2021}, date = {2021-01-01}, journal = {Multimedia Systems}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://doi.org/10.1007/s00530-021-00779-5 doi:10.1007/s00530-021-00779-5 Close
984.	Lu, Zexin; Xia, Wenjun; Huang, Yongqiang; Shan, Hongming; Chen, Hu; Zhou, Jiliu; Zhang, Yi MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising Journal Article CoRR, abs/2103.12995 , 2021. Links \| BibTeX @article{DBLP:journals/corr/abs-2103-12995, title = {MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising}, author = {Zexin Lu and Wenjun Xia and Yongqiang Huang and Hongming Shan and Hu Chen and Jiliu Zhou and Yi Zhang}, url = {https://arxiv.org/abs/2103.12995}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2103.12995}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://arxiv.org/abs/2103.12995 Close
983.	Xia", "Yang Zhao" "Hai Min" "Yan-Xiang Chen Wei Jia" "Wei 2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search Journal Article International Journal of Automation and Computing, (IJAC-2021-01-01), pp. 1, 2021, ISSN: 1476-8186. Links \| BibTeX @article{IJAC-2021-01-012, title = {2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search}, author = {"Yang Zhao" "Hai Min" "Yan-Xiang Chen Wei Jia" "Wei Xia"}, url = {http://ijac.xml-journal.net//article/id/094571fe-2667-4a30-9e76-0203e1577d23}, doi = {10.1007/s11633-021-1292-1}, issn = {1476-8186}, year = {2021}, date = {2021-01-01}, journal = {International Journal of Automation and Computing}, number = {IJAC-2021-01-01}, pages = {1}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close http://ijac.xml-journal.net//article/id/094571fe-2667-4a30-9e76-0203e1577d23 doi:10.1007/s11633-021-1292-1 Close
982.	Lin, Yunfeng ; Li, Guilin ; Zhang, Xing ; Zhang, Weinan ; Chen, BO ; Tang, Ruiming ; Li, Zhenguo ; Feng, Jiashi ; Yu, YOng ModularNAS: Towards Modularized and Reusable Neural Architecture Search Inproceedings Proceedings of Machine Learning and Systems 3 pre-proceedings (MLSys 2021), 2021. Links \| BibTeX @inproceedings{LinModularNAS, title = {ModularNAS: Towards Modularized and Reusable Neural Architecture Search}, author = {Lin, Yunfeng and Li, Guilin and Zhang, Xing and Zhang, Weinan and Chen, BO and Tang, Ruiming and Li, Zhenguo and Feng, Jiashi and Yu, YOng}, url = {https://proceedings.mlsys.org/paper/2021/hash/8f14e45fceea167a5a36dedd4bea2543-Abstract.html}, year = {2021}, date = {2021-01-01}, booktitle = {Proceedings of Machine Learning and Systems 3 pre-proceedings (MLSys 2021)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close https://proceedings.mlsys.org/paper/2021/hash/8f14e45fceea167a5a36dedd4bea2543-A[...] Close
981.	Wang, Rui; Wei, Zhihua; Ji, Shouling; Hong, Zhen EfficientTDNN: Efficient Architecture Search for Speaker Recognition in the Wild Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2103-13581, title = {EfficientTDNN: Efficient Architecture Search for Speaker Recognition in the Wild}, author = {Rui Wang and Zhihua Wei and Shouling Ji and Zhen Hong}, url = {https://arxiv.org/abs/2103.13581}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2103.13581}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2103.13581 Close
980.	Shi, Xian; Zhou, Pan; Chen, Wei; Xie, Lei Darts-Conformer: Towards Efficient Gradient-Based Neural Architecture Search For End-to-End ASR Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-02868, title = {Darts-Conformer: Towards Efficient Gradient-Based Neural Architecture Search For End-to-End ASR}, author = {Xian Shi and Pan Zhou and Wei Chen and Lei Xie}, url = {https://arxiv.org/abs/2104.02868}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.02868}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.02868 Close
979.	Dong, Heyou; Chen, Dan; Zhang, Lei; Ke, Hengjin; Li, Xiaoli Subject Sensitive EEG Discrimination with Fast Reconstructable CNN Driven by Reinforcement Learning: A Case Study of ASD Evaluation Journal Article Neurocomputing, 2021, ISSN: 0925-2312. Abstract \| Links \| BibTeX @article{DONG2021, title = {Subject Sensitive EEG Discrimination with Fast Reconstructable CNN Driven by Reinforcement Learning: A Case Study of ASD Evaluation}, author = {Heyou Dong and Dan Chen and Lei Zhang and Hengjin Ke and Xiaoli Li}, url = {https://www.sciencedirect.com/science/article/pii/S0925231221005270}, doi = {https://doi.org/10.1016/j.neucom.2021.04.009}, issn = {0925-2312}, year = {2021}, date = {2021-01-01}, journal = {Neurocomputing}, abstract = {Recent Electroencephalogram (EEG) analysis in connection with brain disorders has been tremendously benefiting from the (Deep) Neural Network technology in neuroscience research and neuro-engineering practices. However, the performance of existing hand-crafted models, such as the stability, has largely been refrained. This is the case especially in the paradigms that sensitive to the individuality of subjects and the non-stationarity of cognitive dynamics, such as Autism Spectrum Disorder (ASD) evaluation. Aiming at this problem, this study develops a Q-Learning method to enable fast reconstruction of Convolutional Neural Network (CNN) thus to support EEG discrimination adapting to the individuality of subjects under examination. The proposed method first generates a CNN model with the structure and hyper-parameters determined (i.e., Neural Architecture Search) by the customized Q-Learning algorithm, where the CNN model is treated as a discrete system to be optimized. With the sharp shift of subjects, the Q-Learning algorithm reconstructs the CNN model to reach optimization reusing the tacit knowledge learned from the previous trials. A case study has been performed to check the proposed method versus state-of-the-art counterparts based on resting-state EEG collected from 175 ASD-suspicious children with a diverse geological distribution. The observations in the case study indicate that: 1) the method outperforms the counterparts with an individual/sample accuracy of 92.63%/83.23% achieved; 2) the method can quickly reconstruct the CNN model with the group of subjects shifting from one region to another to maintain an encouraging performance while the counterparts without reconstruction may drop by about 12%.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Recent Electroencephalogram (EEG) analysis in connection with brain disorders has been tremendously benefiting from the (Deep) Neural Network technology in neuroscience research and neuro-engineering practices. However, the performance of existing hand-crafted models, such as the stability, has largely been refrained. This is the case especially in the paradigms that sensitive to the individuality of subjects and the non-stationarity of cognitive dynamics, such as Autism Spectrum Disorder (ASD) evaluation. Aiming at this problem, this study develops a Q-Learning method to enable fast reconstruction of Convolutional Neural Network (CNN) thus to support EEG discrimination adapting to the individuality of subjects under examination. The proposed method first generates a CNN model with the structure and hyper-parameters determined (i.e., Neural Architecture Search) by the customized Q-Learning algorithm, where the CNN model is treated as a discrete system to be optimized. With the sharp shift of subjects, the Q-Learning algorithm reconstructs the CNN model to reach optimization reusing the tacit knowledge learned from the previous trials. A case study has been performed to check the proposed method versus state-of-the-art counterparts based on resting-state EEG collected from 175 ASD-suspicious children with a diverse geological distribution. The observations in the case study indicate that: 1) the method outperforms the counterparts with an individual/sample accuracy of 92.63%/83.23% achieved; 2) the method can quickly reconstruct the CNN model with the group of subjects shifting from one region to another to maintain an encouraging performance while the counterparts without reconstruction may drop by about 12%. Close https://www.sciencedirect.com/science/article/pii/S0925231221005270 doi:https://doi.org/10.1016/j.neucom.2021.04.009 Close
978.	Ren, Pengzhen; Xiao, Gang; Chang, Xiaojun; Xiao, Yun; Li, Zhihui; Chen, Xiaojiang NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-01110, title = {NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition}, author = {Pengzhen Ren and Gang Xiao and Xiaojun Chang and Yun Xiao and Zhihui Li and Xiaojiang Chen}, url = {https://arxiv.org/abs/2104.01110}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.01110}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.01110 Close
977.	Ottelander, Tom Den; Dushatskiy, Arkadiy; Virgolin, Marco; Bosman, Peter A N Local Search is a Remarkably Strong Baseline for Neural Architecture Search Inproceedings Ishibuchi, Hisao; Zhang, Qingfu; Cheng, Ran; Li, Ke; Li, Hui; Wang, Handing; Zhou, Aimin (Ed.): Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Shenzhen, China, March 28-31, 2021, Proceedings, pp. 465–479, Springer, 2021. Links \| BibTeX @inproceedings{DBLP:conf/emo/OttelanderDVB21, title = {Local Search is a Remarkably Strong Baseline for Neural Architecture Search}, author = {Tom Den Ottelander and Arkadiy Dushatskiy and Marco Virgolin and Peter A N Bosman}, editor = {Hisao Ishibuchi and Qingfu Zhang and Ran Cheng and Ke Li and Hui Li and Handing Wang and Aimin Zhou}, url = {https://doi.org/10.1007/978-3-030-72062-9_37}, doi = {10.1007/978-3-030-72062-9_37}, year = {2021}, date = {2021-01-01}, booktitle = {Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Shenzhen, China, March 28-31, 2021, Proceedings}, volume = {12654}, pages = {465--479}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close https://doi.org/10.1007/978-3-030-72062-9_37 doi:10.1007/978-3-030-72062-9_37 Close
976.	Ding, Mingyu; Huo, Yuqi; Lu, Haoyu; Yang, Linjie; Wang, Zhe; Lu, Zhiwu; Wang, Jingdong; Luo, Ping Learning Versatile Neural Architectures by Propagating Network Codes Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2103-13253, title = {Learning Versatile Neural Architectures by Propagating Network Codes}, author = {Mingyu Ding and Yuqi Huo and Haoyu Lu and Linjie Yang and Zhe Wang and Zhiwu Lu and Jingdong Wang and Ping Luo}, url = {https://arxiv.org/abs/2103.13253}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2103.13253}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2103.13253 Close
975.	Lankford, Séamus Open-Source Neural Architecture Search with Ensemble and Pre-trained Networks Journal Article International Journal of Modeling and Optimization, 11 (2), pp. 33-41, 2021. Links \| BibTeX @article{lankford2021IJMO, title = {Open-Source Neural Architecture Search with Ensemble and Pre-trained Networks}, author = {Séamus Lankford}, url = {http://www.ijmo.org/vol11/774-Q304.pdf}, year = {2021}, date = {2021-01-01}, journal = {International Journal of Modeling and Optimization}, volume = {11}, number = {2}, pages = {33-41}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close http://www.ijmo.org/vol11/774-Q304.pdf Close
974.	White, Colin; Zela, Arber; Ru, Binxin; Liu, Yang; Hutter, Frank How Powerful are Performance Predictors in Neural Architecture Search? Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-01177, title = {How Powerful are Performance Predictors in Neural Architecture Search?}, author = {Colin White and Arber Zela and Binxin Ru and Yang Liu and Frank Hutter}, url = {https://arxiv.org/abs/2104.01177}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.01177}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.01177 Close
973.	ElSaid, AbdElRahman A; Karns, Joshua; Lyu, Zimeng; II, Alexander Ororbia G; Desell, Travis Continuous Ant-Based Neural Topology Search Inproceedings Castillo, Pedro A; e}, Juan Luis Jim{' (Ed.): Applications of Evolutionary Computation: 24th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Virtual Event, April 7--9, 2021, Proceedings, pp. 291, Springer Nature 2021. Links \| BibTeX @inproceedings{desell2021continuous, title = {Continuous Ant-Based Neural Topology Search}, author = {AbdElRahman A. ElSaid and Joshua Karns and Zimeng Lyu and Alexander G. Ororbia II and Travis Desell}, editor = {Pedro A. Castillo and Juan Luis Jim{'{e}}nez Laredo}, url = {https://doi.org/10.1007/978-3-030-72699-7\\_19}, year = {2021}, date = {2021-01-01}, booktitle = {Applications of Evolutionary Computation: 24th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Virtual Event, April 7--9, 2021, Proceedings}, volume = {12694}, pages = {291}, organization = {Springer Nature}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close https://doi.org/10.1007/978-3-030-72699-7\\\\_19 Close
972.	Li, Yanxi ; Wen, Zean ; Wang Yunhe, ; Xu, Chang One-shot Graph Neural Architecture Search with Dynamic Search Space Inproceedings AAAI 2021, 2021. Links \| BibTeX @inproceedings{Li2021-aaai, title = {One-shot Graph Neural Architecture Search with Dynamic Search Space}, author = {Li, Yanxi and Wen, Zean and Wang, Yunhe, and Xu, Chang}, url = {https://www.aaai.org/AAAI21Papers/AAAI-3441.LiY.pdf}, year = {2021}, date = {2021-01-01}, booktitle = {AAAI 2021}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close https://www.aaai.org/AAAI21Papers/AAAI-3441.LiY.pdf Close
971.	Cai, Shaofei; Li, Liang; Deng, Jincan; Zhang, Beichen; Zha, Zheng-Jun; Su, Li; Huang, Qingming Rethinking Graph Neural Network Search from Message-passing Technical Report 2021. Links \| BibTeX @techreport{cai2021rethinking, title = {Rethinking Graph Neural Network Search from Message-passing}, author = {Shaofei Cai and Liang Li and Jincan Deng and Beichen Zhang and Zheng-Jun Zha and Li Su and Qingming Huang}, url = {https://arxiv.org/abs/2103.14282}, year = {2021}, date = {2021-01-01}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2103.14282 Close
970.	Liang, Y; Lu, L; Jin, Y; Xie, J; Huang, R; Zhang, J; Lin, W An Efficient Hardware Design for Accelerating Sparse CNNs with NAS-based Models Journal Article IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp. 1-1, 2021. Links \| BibTeX @article{9380321, title = {An Efficient Hardware Design for Accelerating Sparse CNNs with NAS-based Models}, author = {Y Liang and L Lu and Y Jin and J Xie and R Huang and J Zhang and W Lin}, url = {https://ieeexplore.ieee.org/abstract/document/9380321}, doi = {10.1109/TCAD.2021.3066563}, year = {2021}, date = {2021-01-01}, journal = {IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems}, pages = {1-1}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://ieeexplore.ieee.org/abstract/document/9380321 doi:10.1109/TCAD.2021.3066563 Close
969.	Tan, Mingxing; Le, Quoc V EfficientNetV2: Smaller Models and Faster Training Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-00298, title = {EfficientNetV2: Smaller Models and Faster Training}, author = {Mingxing Tan and Quoc V Le}, url = {https://arxiv.org/abs/2104.00298}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.00298}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.00298 Close
968.	Roberts, Nicholas; Khodak, Mikhail; Dao, Tri; Li, Liam; é, Christopher R; Talwalkar, Ameet Rethinking Neural Operations for Diverse Tasks Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2103-15798, title = {Rethinking Neural Operations for Diverse Tasks}, author = {Nicholas Roberts and Mikhail Khodak and Tri Dao and Liam Li and Christopher R é and Ameet Talwalkar}, url = {https://arxiv.org/abs/2103.15798}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2103.15798}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2103.15798 Close
967.	He, Yufan; Yang, Dong; Roth, Holger; Zhao, Can; Xu, Daguang DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2103-15954, title = {DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation}, author = {Yufan He and Dong Yang and Holger Roth and Can Zhao and Daguang Xu}, url = {https://arxiv.org/abs/2103.15954}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2103.15954}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2103.15954 Close
966.	Prabakaran, B S; Akhtar, A; Rehman, S; Hasan, O; Shafique, M BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables Journal Article IEEE Internet of Things Journal, pp. 1-1, 2021. Links \| BibTeX @article{9377449, title = {BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables}, author = {B S Prabakaran and A Akhtar and S Rehman and O Hasan and M Shafique}, url = {https://ieeexplore.ieee.org/abstract/document/9377449}, doi = {10.1109/JIOT.2021.3065815}, year = {2021}, date = {2021-01-01}, journal = {IEEE Internet of Things Journal}, pages = {1-1}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://ieeexplore.ieee.org/abstract/document/9377449 doi:10.1109/JIOT.2021.3065815 Close
965.	Schmidgall, Samuel Self-Constructing Neural Networks Through Random Mutation Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2103-15692, title = {Self-Constructing Neural Networks Through Random Mutation}, author = {Samuel Schmidgall}, url = {https://arxiv.org/abs/2103.15692}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2103.15692}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2103.15692 Close
964.	Guo, Shaopeng; Wang, Yujie; Yuan, Kun; Li, Quanquan Differentiable Network Adaption with Elastic Search Space Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2103-16350, title = {Differentiable Network Adaption with Elastic Search Space}, author = {Shaopeng Guo and Yujie Wang and Kun Yuan and Quanquan Li}, url = {https://arxiv.org/abs/2103.16350}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2103.16350}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2103.16350 Close
963.	Zappella, Giovanni; Salinas, David; Archambeau, Cédric A resource-efficient method for repeated HPO and NAS problems Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2103-16111, title = {A resource-efficient method for repeated HPO and NAS problems}, author = {Giovanni Zappella and David Salinas and Cédric Archambeau}, url = {https://arxiv.org/abs/2103.16111}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2103.16111}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2103.16111 Close
962.	Wang, J; Zhao, J; An, J; Wang, D; Qiu, M A Linear NAS Service of ConvNets for Fast Deployment in the Edge of 5G Networks Journal Article IEEE Network, 35 (2), pp. 46-52, 2021. Links \| BibTeX @article{9387697, title = {A Linear NAS Service of ConvNets for Fast Deployment in the Edge of 5G Networks}, author = {J Wang and J Zhao and J An and D Wang and M Qiu}, url = {https://ieeexplore.ieee.org/abstract/document/9387697}, doi = {10.1109/MNET.011.1900336}, year = {2021}, date = {2021-01-01}, journal = {IEEE Network}, volume = {35}, number = {2}, pages = {46-52}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://ieeexplore.ieee.org/abstract/document/9387697 doi:10.1109/MNET.011.1900336 Close
961.	Lee, Hayeon; Hyung, Eunyoung; Hwang, Sung Ju Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets Inproceedings International Conference on Learning Representations, 2021. Links \| BibTeX @inproceedings{<LineBreak>lee2021rapid, title = {Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets}, author = {Hayeon Lee and Eunyoung Hyung and Sung Ju Hwang}, url = {https://openreview.net/forum?id=rkQuFUmUOg3}, year = {2021}, date = {2021-01-01}, booktitle = {International Conference on Learning Representations}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close https://openreview.net/forum?id=rkQuFUmUOg3 Close
960.	Ding, Z; Chen, Y; Li, N; Zhao, D; Sun, Z; Chen, C L P BNAS: Efficient Neural Architecture Search Using Broad Scalable Architecture Journal Article IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15, 2021. Links \| BibTeX @article{9392299, title = {BNAS: Efficient Neural Architecture Search Using Broad Scalable Architecture}, author = {Z Ding and Y Chen and N Li and D Zhao and Z Sun and C L P Chen}, url = {https://ieeexplore.ieee.org/abstract/document/9392299}, doi = {10.1109/TNNLS.2021.3067028}, year = {2021}, date = {2021-01-01}, journal = {IEEE Transactions on Neural Networks and Learning Systems}, pages = {1-15}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://ieeexplore.ieee.org/abstract/document/9392299 doi:10.1109/TNNLS.2021.3067028 Close
959.	Wang, Guangrun; Lin, Liang; Chen, Rongcong; Wang, Guangcong; Zhang, Jiqi Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition Journal Article IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021. Links \| BibTeX @article{DBLP:journals/corr/abs-2103-16889, title = {Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition}, author = {Guangrun Wang and Liang Lin and Rongcong Chen and Guangcong Wang and Jiqi Zhang}, url = {https://arxiv.org/abs/2103.16889}, year = {2021}, date = {2021-01-01}, journal = {IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://arxiv.org/abs/2103.16889 Close
958.	-, Tien; -, Yi; Sze, Vivienne NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization Technical Report , 2021. Links \| BibTeX @techreport{DBLP:journals/corr/abs-2104-00031, title = {NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization}, author = {Tien - and Yi - and Vivienne Sze}, url = {https://arxiv.org/abs/2104.00031}, year = {2021}, date = {2021-01-01}, journal = {CoRR}, volume = {abs/2104.00031}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2104.00031 Close
957.	Hu, Shoukang ; Xie, Xurong ; Liu, Shansong ; Cui, Mingyu ; Geng, Mengzhe ; Liu, Xunying ; Meng, Helen Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks Inproceedings ICASSP2021, 2021. Links \| BibTeX @inproceedings{HuNeural, title = {Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks}, author = {Hu, Shoukang and Xie, Xurong and Liu, Shansong and Cui, Mingyu and Geng, Mengzhe and Liu, Xunying and Meng, Helen}, url = {http://www1.se.cuhk.edu.hk/~hccl/publications/pub/ICASSP2021_NAS.pdf}, year = {2021}, date = {2021-01-01}, booktitle = {ICASSP2021}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close http://www1.se.cuhk.edu.hk/~hccl/publications/pub/ICASSP2021_NAS.pdf Close
956.	Wang, X; Wang, X; Jin, L; Lv, R; Dai, B; He, M; Lv, T Evolutionary Algorithm-Based and Network Architecture Search-Enabled Multiobjective Traffic Classification Journal Article IEEE Access, 9 , pp. 52310-52325, 2021. Links \| BibTeX @article{9383257, title = {Evolutionary Algorithm-Based and Network Architecture Search-Enabled Multiobjective Traffic Classification}, author = {X Wang and X Wang and L Jin and R Lv and B Dai and M He and T Lv}, url = {https://ieeexplore.ieee.org/abstract/document/9383257}, doi = {10.1109/ACCESS.2021.3068267}, year = {2021}, date = {2021-01-01}, journal = {IEEE Access}, volume = {9}, pages = {52310-52325}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://ieeexplore.ieee.org/abstract/document/9383257 doi:10.1109/ACCESS.2021.3068267 Close
955.	Huang, Yufang; Axsom, Kelly M; Lee, John; Subramanian, Lakshminarayanan; Zhang, Yiye DICE: Deep Significance Clustering for Outcome-Aware Stratification Technical Report 2021. Links \| BibTeX @techreport{YufangHuang2021_xxi, title = {DICE: Deep Significance Clustering for Outcome-Aware Stratification}, author = {Yufang Huang and Kelly M Axsom and John Lee and Lakshminarayanan Subramanian and Yiye Zhang}, url = {https://arxiv.org/abs/2101.02344}, year = {2021}, date = {2021-01-01}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2101.02344 Close
954.	Ma, Ailong; Wan, Yuting; Zhong, Yanfei; Wang, Junjue; Zhang, Liangpei SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search Technical Report , 2021, ISSN: 0924-2716. Links \| BibTeX @techreport{AilongMa2021_voj, title = {SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search}, author = {Ailong Ma and Yuting Wan and Yanfei Zhong and Junjue Wang and Liangpei Zhang}, url = {https://www.sciencedirect.com/science/article/pii/S0924271620303361}, doi = {https://doi.org/10.1016/j.isprsjprs.2020.11.025}, issn = {0924-2716}, year = {2021}, date = {2021-01-01}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {172}, pages = {171-188}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://www.sciencedirect.com/science/article/pii/S0924271620303361 doi:https://doi.org/10.1016/j.isprsjprs.2020.11.025 Close
953.	Yang, Hansi; Yao, Quanming; Kwok, James T Tensorizing Subgraph Search in the Supernet Technical Report , 2021. Links \| BibTeX @techreport{Yang2021_atf, title = {Tensorizing Subgraph Search in the Supernet}, author = {Hansi Yang and Quanming Yao and James T Kwok}, url = {https://arxiv.org/abs/2101.01078}, year = {2021}, date = {2021-01-01}, volume = {abs/2101.01078}, key = {journals/corr/abs-2101-01078}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2101.01078 Close
952.	Syed, Muhtadyuzzaman; Srinivasan, Arvind Akpuram Generalized Latency Performance Estimation for Once-For-All Neural Architecture Search Technical Report , 2021. Links \| BibTeX @techreport{Syed2021_kud, title = {Generalized Latency Performance Estimation for Once-For-All Neural Architecture Search}, author = {Muhtadyuzzaman Syed and Arvind Akpuram Srinivasan}, url = {https://arxiv.org/abs/2101.00732}, year = {2021}, date = {2021-01-01}, volume = {abs/2101.00732}, key = {journals/corr/abs-2101-00732}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2101.00732 Close
951.	Huang, Hanxun; Ma, Xingjun; Erfani, Sarah M; Bailey, James Neural Architecture Search via Combinatorial Multi-Armed Bandit Technical Report , 2021. Links \| BibTeX @techreport{Huang2021_aks, title = {Neural Architecture Search via Combinatorial Multi-Armed Bandit}, author = {Hanxun Huang and Xingjun Ma and Sarah M Erfani and James Bailey}, url = {https://arxiv.org/abs/2101.00336}, year = {2021}, date = {2021-01-01}, volume = {abs/2101.00336}, key = {journals/corr/abs-2101-00336}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2101.00336 Close
950.	Tang, Tianqi; Yu, Xin; Dong, Xuanyi; Yang, Yi Auto-Navigator: Decoupled Neural Architecture Search for Visual Navigation Technical Report 2021. Links \| BibTeX @techreport{Tang2021_szq, title = {Auto-Navigator: Decoupled Neural Architecture Search for Visual Navigation}, author = {Tianqi Tang and Xin Yu and Xuanyi Dong and Yi Yang}, url = {https://openaccess.thecvf.com/content/WACV2021/html/Tang_Auto-Navigator_Decoupled_Neural_Architecture_Search_for_Visual_Navigation_WACV_2021_paper.html}, year = {2021}, date = {2021-01-01}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, pages = {3743-3752}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://openaccess.thecvf.com/content/WACV2021/html/Tang_Auto-Navigator_Decouple[...] Close
949.	Zheng, Shuai; Wang, Yabin; Li, Baotong; Li, Xin A Hardware-adaptive Deep Feature Matching Pipeline for Real-time 3D Reconstruction Journal Article 132 , pp. 102984, 2021. Links \| BibTeX @article{Zheng2021_mdc, title = {A Hardware-adaptive Deep Feature Matching Pipeline for Real-time 3D Reconstruction}, author = {Shuai Zheng and Yabin Wang and Baotong Li and Xin Li}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0010448520301779}, doi = {10.1016/J.CAD.2020.102984}, year = {2021}, date = {2021-01-01}, volume = {132}, pages = {102984}, key = {journals/cad/ZhengWLL21}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close https://www.sciencedirect.com/science/article/abs/pii/S0010448520301779 doi:10.1016/J.CAD.2020.102984 Close
948.	Pham, Hieu; Le, Quoc V AutoDropout - Learning Dropout Patterns to Regularize Deep Networks Technical Report , 2021. Links \| BibTeX @techreport{Pham2021_okt, title = {AutoDropout - Learning Dropout Patterns to Regularize Deep Networks}, author = {Hieu Pham and Quoc V Le}, url = {https://arxiv.org/abs/2101.01761}, year = {2021}, date = {2021-01-01}, volume = {abs/2101.01761}, key = {journals/corr/abs-2101-01761}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } Close https://arxiv.org/abs/2101.01761 Close

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Literature on Neural Architecture Search

2021