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Freiburg-Hannover-Tübingen

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.

3488 entries « ‹ 41 of 70 › »

2021

1488.

Zhou, Daquan; Jin, Xiaojie; Lian, Xiaochen; Yang, Linjie; Xue, Yujing; Hou, Qibin; Feng, Jiashi

AutoSpace: Neural Architecture Search with Less Human Interference Technical Report

2021.

1487.

Li, Changlin; Tang, Tao; Wang, Guangrun; Peng, Jiefeng; Wang, Bing; Liang, Xiaodan; Chang, Xiaojun

BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search Technical Report

2021.

1486.

Abed, H; Gyires-Tóth, B

Efficient Neural Architecture Search for Long Short-Term Memory Networks Proceedings Article

In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000287-000292, 2021.

1485.

Ai, Dige; Zhang, Hong

GNAS: A Generalized Neural Network Architecture Search Framework Technical Report

2021.

1484.

Zhang, Haichao; Hao, Kuangrong; Gao, Lei; -, Xue; Wei, Bing

Enhanced Gradient for Differentiable Architecture Search Technical Report

2021.

1483.

Su, Xiu; Huang, Tao; Li, Yanxi; You, Shan; Wang, Fei; Qian, Chen; Zhang, Changshui; Xu, Chang

Prioritized Architecture Sampling with Monto-Carlo Tree Search Technical Report

2021.

1482.

Wang, Hongbo; Pan, Xingyu; Fan, Lingyan; Zhao, Shuofeng

Steganalysis of convolutional neural network based on neural architecture search Journal Article

In: Multimedia Systems, 2021.

1481.

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

In: CoRR, vol. abs/2103.12995, 2021.

1480.

"Yang Zhao" "Hai Min" "Yan-Xiang Chen Wei Jia" "Wei Xia",

2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search Journal Article

In: International Journal of Automation and Computing, no. IJAC-2021-01-01, pp. 1, 2021, ISSN: 1476-8186.

1479.

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 Proceedings Article

In: Proceedings of Machine Learning and Systems 3 pre-proceedings (MLSys 2021), 2021.

1478.

Wang, Rui; Wei, Zhihua; Ji, Shouling; Hong, Zhen

EfficientTDNN: Efficient Architecture Search for Speaker Recognition in the Wild Technical Report

2021.

1477.

Ottelander, Tom Den; Dushatskiy, Arkadiy; Virgolin, Marco; Bosman, Peter A N

Local Search is a Remarkably Strong Baseline for Neural Architecture Search Proceedings Article

In: 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.

1476.

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.

1475.

Lankford, Séamus

Open-Source Neural Architecture Search with Ensemble and Pre-trained Networks Journal Article

In: International Journal of Modeling and Optimization, vol. 11, no. 2, pp. 33-41, 2021.

1474.

Li, Yanxi; Wen, Zean; Yunhe Wang,; Xu, Chang

One-shot Graph Neural Architecture Search with Dynamic Search Space Proceedings Article

In: AAAI 2021, 2021.

1473.

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.

1472.

Roberts, Nicholas; Khodak, Mikhail; Dao, Tri; Li, Liam; é, Christopher R; Talwalkar, Ameet

Rethinking Neural Operations for Diverse Tasks Technical Report

2021.

1471.

He, Yufan; Yang, Dong; Roth, Holger; Zhao, Can; Xu, Daguang

DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation Technical Report

2021.

1470.

Schmidgall, Samuel

Self-Constructing Neural Networks Through Random Mutation Technical Report

2021.

1469.

Guo, Shaopeng; Wang, Yujie; Yuan, Kun; Li, Quanquan

Differentiable Network Adaption with Elastic Search Space Technical Report

2021.

1468.

Zappella, Giovanni; Salinas, David; Archambeau, Cédric

A resource-efficient method for repeated HPO and NAS problems Technical Report

2021.

1467.

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

In: IEEE Network, vol. 35, no. 2, pp. 46-52, 2021.

1466.

Lee, Hayeon; Hyung, Eunyoung; Hwang, Sung Ju

Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets Proceedings Article

In: International Conference on Learning Representations, 2021.

1465.

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

In: IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15, 2021.

1464.

-, Tien; -, Yi; Sze, Vivienne

NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization Technical Report

2021.

1463.

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 Proceedings Article

In: ICASSP2021, 2021.

1462.

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

In: IEEE Access, vol. 9, pp. 52310-52325, 2021.

1461.

Wang, Guangrun; Lin, Liang; Chen, Rongcong; Wang, Guangcong; Zhang, Jiqi

Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition Journal Article

In: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021.

1460.

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

In: IEEE Internet of Things Journal, pp. 1-1, 2021.

1459.

Tan, Mingxing; Le, Quoc V

EfficientNetV2: Smaller Models and Faster Training Technical Report

2021.

1458.

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

In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp. 1-1, 2021.

1457.

ElSaid, AbdElRahman A.; Karns, Joshua; Lyu, Zimeng; II, Alexander G. Ororbia; Desell, Travis

Continuous Ant-Based Neural Topology Search Proceedings Article

In: Castillo, Pedro A.; Laredo, Juan Luis Jim'enez (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.

1456.

White, Colin; Zela, Arber; Ru, Binxin; Liu, Yang; Hutter, Frank

How Powerful are Performance Predictors in Neural Architecture Search? Technical Report

2021.

1455.

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.

1454.

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

In: 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}

}

1453.

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.

1452.

Ganepola, Vayangi Vishmi Vishara; Wirasingha, Torin

Automating Generative Adversarial Networks using Neural Architecture Search: A Review Proceedings Article

In: 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 577-582, 2021.

1451.

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

In: Remote Sensing, vol. 13, no. 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}

}

1450.

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.

1449.

Yu, Kaicheng; é, Ren; Salzmann, Mathieu

Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search Technical Report

2021.

1448.

Liu, Yukun; Li, Ta; Zhang, Pengyuan; Yan, Yonghong

Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search Technical Report

2021.

1447.

Wang, Chunnan; Chen, Bozhou; Li, Geng; Wang, Hongzhi

FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search Technical Report

2021.

1446.

Meng, Tianjian; Chen, Xiaohan; Jiang, Yifan; Wang, Zhangyang

A Design Space Study for LISTA and Beyond Proceedings Article

In: International Conference on Learning Representations, 2021.

1445.

Liu, Aoming; Huang, Zehao; Huang, Zhiwu; Wang, Naiyan

Direct Differentiable Augmentation Search Technical Report

2021.

1444.

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.

1443.

Peter, David; Roth, Wolfgang; Pernkopf, Franz

End-to-end Keyword Spotting using Neural Architecture Search and Quantization Technical Report

2021.

1442.

Mundt, Martin; Pliushch, Iuliia; Ramesh, Visvanathan

Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference Technical Report

2021.

1441.

Zhao, Huan; Yao, Quanming; -, Wei

Search to aggregate neighborhood for graph neural network Technical Report

2021.

1440.

Xue, Chao; Wang, Xiaoxing; Yan, Junchi; Hu, Yonggang; Yang, Xiaokang; Sun, Kewei

Rethinking Bi-Level Optimization in Neural Architecture Search: A Gibbs Sampling Perspective Proceedings Article

In: AAAI 2021, 2021.

1439.

Yin, Yihang; Huang, Siyu; Zhang, Xiang; Dou, Dejing

BM-NAS: Bilevel Multimodal Neural Architecture Search Technical Report

2021.

3488 entries « ‹ 41 of 70 › »