AutoML.org

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.

3282 entries « ‹ 42 of 66 › »

2021

1232.

Shi, Pengfei; Zhao, Zhongxin; Fan, Xinnan; Yan, Xijun; Yan, Wei; Xin, Yuanxue

Remote Sensing Image Object Detection Based on Angle Classification Journal Article

In: IEEE Access, vol. 9, pp. 118696-118707, 2021.

1231.

Lin, Jun-Liang; Sung, Yi-Lin; Hong, Cheng-Yao; Lee, Han-Hung; Liu, Tyng-Luh

The Maximum a Posterior Estimation of Darts Proceedings Article

In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 419-423, 2021.

1230.

Huynh, Lam; Nguyen, Phong; Matas, Jiri; Rahtu, Esa; Heikkilä, Janne

Lightweight Monocular Depth with a Novel Neural Architecture Search Method Technical Report

2021.

1229.

Chen, Wuyang; Gong, Xinyu; Wei, Yunchao; Shi, Humphrey; Yan, Zhicheng; Yang, Yi; Wang, Zhangyang

Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics Technical Report

2021.

1228.

Zhang, Yang; Zong, Ruohan; Kou, Ziyi; Shang, Lanyu; Wang, Dong

A Crowd-driven Dynamic Neural Architecture Searching Approach to Quality-aware Streaming Disaster Damage Assessment Technical Report

2021.

1227.

Wang, Huiqun; Yang, Ruijie; Huang, Di; Wang, Yunhong

iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization Technical Report

2021.

1226.

Lyu, Bo; Wen, Shiping; Shi, Kaibo; Huang, Tingwen

Multiobjective Reinforcement Learning-Based Neural Architecture Search for Efficient Portrait Parsing Journal Article

In: IEEE Transactions on Cybernetics, pp. 1-12, 2021.

1225.

Xu, Jin; Tan, Xu; Song, Kaitao; Luo, Renqian; Leng, Yichong; Qin, Tao; Liu, Tie-Yan; Li, Jian

Analyzing and Mitigating Interference in Neural Architecture Search Technical Report

2021.

1224.

Du, Mingyang; He, Xikai; Cai, Xiaohao; Bi, Daping

Balanced Neural Architecture Search and Its Application in Specific Emitter Identification Journal Article

In: IEEE Transactions on Signal Processing, vol. 69, pp. 5051-5065, 2021.

1223.

Huynh, Lam; Nguyen, Tri; Nguyen, Thu; Pirttikangas, Susanna; Siirtola, Pekka

StressNAS: Affect State and Stress Detection Using Neural Architecture Search Proceedings Article

In: Doryab, Afsaneh; Lv, Qin; Beigl, Michael (Ed.): UbiComp/ISWC '21: 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2021 ACM International Symposium on Wearable Computers, Virtual Event, September 21-25, 2021, pp. 121–125, ACM, 2021.

1222.

Liu, Zuhao; Wang, Huan; Gao, Yibo; Shi, Shunchen

Automatic Attention Learning Using Neural Architecture Search for Detection of Cardiac Abnormality in 12-Lead ECG Journal Article

In: IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-12, 2021.

1221.

Liu, Zhijian; Tang, Haotian; Zhao, Shengyu; Shao, Kevin; Han, Song

PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution Journal Article

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1, 2021.

1220.

Li, Yuening; Chen, Zhengzhang; Zha, Daochen; Zhou, Kaixiong; Jin, Haifeng; Chen, Haifeng; Hu, Xia

Automated Anomaly Detection via Curiosity-Guided Search and Self-Imitation Learning Journal Article

In: IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13, 2021.

1219.

Nie, Xing; Liu, Yongcheng; Chen, Shaohong; Chang, Jianlong; Huo, Chunlei; Meng, Gaofeng; Tian, Qi; Hu, Weiming; Pan, Chunhong

Differentiable Convolution Search for Point Cloud Processing Technical Report

2021.

1218.

Xue, Song; Zhao, Bo; Chen, Hanlin; Wang, Runqi; Zhang, Baochang

UCB-ENAS based on Reinforcement Learning Proceedings Article

In: 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), pp. 2008-2013, 2021.

1217.

Yu, Kaicheng; Ranftl, Rene; Salzmann, Mathieu

An Analysis of Super-Net Heuristics in Weight-Sharing NAS Journal Article

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1, 2021.

1216.

Gong, Xinyu; Wang, Heng; Shou, Zheng; Feiszli, Matt; Wang, Zhangyang; Yan, Zhicheng

Searching for Two-Stream Models in Multivariate Space for Video Recognition Technical Report

2021.

1215.

Deng, Jiajun; Gutierrez, Fabian Ritter; Hu, Shoukang; Geng, Mengzhe; Xie, Xurong; Ye, Zi; Liu, Shansong; Yu, Jianwei; Liu, Xunying; Meng, Helen

Bayesian Parametric and Architectural Domain Adaptation of LF-MMI Trained TDNNs for Elderly and Dysarthric Speech Recognition Proceedings Article

In: Proc. Interspeech 2021, pp. 4818–4822, 2021.

1214.

Shu, Yao; Cai, Shaofeng; Dai, Zhongxiang; Ooi, Beng Chin; Low, Bryan Kian Hsiang

NASI: Label- and Data-agnostic Neural Architecture Search at Initialization Technical Report

2021.

1213.

Umamageswari, A.; Bharathiraja, N.; Irene, D. Shiny

A Novel Fuzzy C-Means based Chameleon Swarm Algorithm for Segmentation and Progressive Neural Architecture Search for Plant Disease Classification Journal Article

In: ICT Express, 2021, ISSN: 2405-9595.

Abstract | Links | BibTeX

1212.

Liu, Jinyuan; Wu, Yuhui; Huang, Zhanbo; Liu, Risheng; Fan, Xin

SMoA: Searching a Modality-Oriented Architecture for Infrared and Visible Image Fusion Journal Article

In: IEEE Signal Processing Letters, vol. 28, pp. 1818-1822, 2021.

1211.

Liao, Yi-Lun; Karaman, Sertac; Sze, Vivienne

Searching for Efficient Multi-Stage Vision Transformers Technical Report

2021.

1210.

Jlassi, Sinda; Jdey, Imen; Ltifi, Hela

Bayesian Hyperparameter Optimization of Deep Neural Network Algorithms Based on Ant Colony Optimization Proceedings Article

In: Lladós, Josep; Lopresti, Daniel; Uchida, Seiichi (Ed.): Document Analysis and Recognition -- ICDAR 2021, pp. 585–594, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-86334-0.

Abstract | BibTeX

1209.

Cai, Shaofei; Li, Liang; Han, Xinzhe; Zha, Zhengjun; Huang, Qingming

Edge-featured Graph Neural Architecture Search Technical Report

2021.

1208.

Shu, Yao; Chen, Yizhou; Dai, Zhongxiang; Low, Bryan Kian Hsiang

Going Beyond Neural Architecture Search with Sampling-based Neural Ensemble Search Technical Report

2021.

1207.

Bai, Haoyue; Zhou, Fengwei; Hong, Lanqing; Ye, Nanyang; Chan, S. -H. Gary; Li, Zhenguo

NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization Technical Report

2021.

1206.

Letunovskiy, Alexey; Korviakov, Vladimir; Polovnikov, Vladimir; Kargapoltseva, Anastasiia; Mazurenko, Ivan; Xiong, Yepan

ISyNet: Convolutional Neural Networks design for AI accelerator Technical Report

2021.

1205.

Shi, Xiaohu; Guo, Hongyan; Wu, Chunguo; Liang, Yanchun; Chang, Zhiyong

A block-encoding method for evolving neural network architecture Journal Article

In: Int. J. Bio Inspired Comput., vol. 18, no. 1, pp. 27–37, 2021.

1204.

Rawat, Ambrish; Sinn, Mathieu; Buesser, Beat

Automated Robustness with Adversarial Training as a Post-Processing Step Technical Report

2021.

1203.

Yu, Ke; Li, Zexian; Peng, Yue; Loy, Chen Change; Gu, Jinwei

ReconfigISP: Reconfigurable Camera Image Processing Pipeline Technical Report

2021.

1202.

Yang, Ji; Sang, Lu; Cremers, Daniel

Dive into Layers: Neural Network Capacity Bounding using Algebraic Geometry Technical Report

2021.

1201.

Yang, Fan; Qiao, Yanan; Huang, Cheng; Wang, Shan; Wang, Xiao

An Automatic Credit Scoring Strategy (ACSS) using memetic evolutionary algorithm and neural architecture search Journal Article

In: Applied Soft Computing, vol. 113, pp. 107871, 2021, ISSN: 1568-4946.

Abstract | Links | BibTeX

@article{YANG2021107871,

title = {An Automatic Credit Scoring Strategy (ACSS) using memetic evolutionary algorithm and neural architecture search},

author = {Fan Yang and Yanan Qiao and Cheng Huang and Shan Wang and Xiao Wang},

url = {https://www.sciencedirect.com/science/article/pii/S1568494621007936},

doi = {https://doi.org/10.1016/j.asoc.2021.107871},

issn = {1568-4946},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Applied Soft Computing},

volume = {113},

pages = {107871},

abstract = {Credit scoring is playing an increasingly critical role with the rising number of lending operations for micro and small enterprises as well as individuals. A large number of research is primarily based on the combination and construction methods of credit scoring models by the experts. However, different credit data have distinct requirements for the models, and how to automatically search and construct credit scoring models according to credit data has become essential, that is the main concern of this paper. In response to the current challenges for credit scoring research, we proposed an Automatic Credit Scoring Strategy (ACSS), designed a credit assessment platform which includes data import, classification model automatic search, feature selection, hyperparameter optimization, data mining, classification output and other modules. Aiming at the problem of substantial imbalance in credit data, we propose an improved SMOTE algorithm that is capable of generating supplementary data for the lack of minority in credit data, thereby making the credit data distribution well balanced. As for classification model selection, features engineering, parameter optimization and other parts, we further incorporate automatic search ways to reduce manual interaction. We utilize public and self-owned credit data sets to conduct experiments and compare them with the latest credit assessment methods. Extensive experiments have demonstrated that our ACSS method achieves relatively noticeable performance improvements using the German credit dataset, the Taiwan credit dataset and the personal credit dataset for credit scoring. The best results achieved by our proposed method are 0.98, 0.99, 0.895 and 0.901 for MAE, RMSE, accuracy and precision respectively. In addition, the experimental results also show that our proposed improved SMOTE algorithm contributes to the credit scoring performance enhancement. The experimental findings suggest that our proposed ACSS balances automation and accuracy, which can be implemented to the consumption industry to enhance the reliability of credit assessments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Credit scoring is playing an increasingly critical role with the rising number of lending operations for micro and small enterprises as well as individuals. A large number of research is primarily based on the combination and construction methods of credit scoring models by the experts. However, different credit data have distinct requirements for the models, and how to automatically search and construct credit scoring models according to credit data has become essential, that is the main concern of this paper. In response to the current challenges for credit scoring research, we proposed an Automatic Credit Scoring Strategy (ACSS), designed a credit assessment platform which includes data import, classification model automatic search, feature selection, hyperparameter optimization, data mining, classification output and other modules. Aiming at the problem of substantial imbalance in credit data, we propose an improved SMOTE algorithm that is capable of generating supplementary data for the lack of minority in credit data, thereby making the credit data distribution well balanced. As for classification model selection, features engineering, parameter optimization and other parts, we further incorporate automatic search ways to reduce manual interaction. We utilize public and self-owned credit data sets to conduct experiments and compare them with the latest credit assessment methods. Extensive experiments have demonstrated that our ACSS method achieves relatively noticeable performance improvements using the German credit dataset, the Taiwan credit dataset and the personal credit dataset for credit scoring. The best results achieved by our proposed method are 0.98, 0.99, 0.895 and 0.901 for MAE, RMSE, accuracy and precision respectively. In addition, the experimental results also show that our proposed improved SMOTE algorithm contributes to the credit scoring performance enhancement. The experimental findings suggest that our proposed ACSS balances automation and accuracy, which can be implemented to the consumption industry to enhance the reliability of credit assessments.

1200.

Zhang, Mingyang; Yu, Xinyi; Rong, Jingtao; Ou, Linlin; Gao, Feng

RepNAS: Searching for Efficient Re-parameterizing Blocks Technical Report

2021.

1199.

Zhao, Yiren; Gao, Xitong; Shumailov, Ilia; Fusi, Nicolò; Mullins, Robert D.

Rapid Model Architecture Adaption for Meta-Learning Technical Report

2021.

1198.

Yue, Zhixiong; Guo, Pengxin; Zhang, Yu

Domain Adaptation by Maximizing Population Correlation with Neural Architecture Search Technical Report

2021.

1197.

Yan, Zheyu; Jiang, Weiwen; Hu, Xiaobo Sharon; Shi, Yiyu

RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search Technical Report

2021.

1196.

Liu, Hongyuan; Nicolae, Bogdan; Di, Sheng; Cappello, Franck; Jog, Adwait

Accelerating DNN Architecture Search at Scale Using Selective Weight Transfer Proceedings Article

In: IEEE International Conference on Cluster Computing, CLUSTER 2021, Portland, OR, USA, September 7-10, 2021, pp. 82–93, IEEE, 2021.

1195.

Zhou, Kaichen; Hong, Lanqing; Hu, Shoukang; Zhou, Fengwei; Ru, Binxin; Feng, Jiashi; Li, Zhenguo

DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture Technical Report

2021.

1194.

Operiano, Kevin Richard G.; Pora, Wanchalerm; Iba, Hitoshi; Kera, Hiroshi

Evolving Architectures with Gradient Misalignment toward Low Adversarial Transferability Technical Report

2021.

1193.

Hu, Chi; Wang, Chenglong; Ma, Xiangnan; Meng, Xia; Li, Yinqiao; Xiao, Tong; Zhu, Jingbo; Li, Changliang

RankNAS: Efficient Neural Architecture Search by Pairwise Ranking Technical Report

2021.

1192.

Ma, Lianbo; Li, Nan; Yu, Guo; Geng, Xiaoyu; Huang, Min; Wang, Xingwei

How to Simplify Search: Classification-wise Pareto Evolution for One-shot Neural Architecture Search Technical Report

2021.

1191.

Timofeev, Aleksandr; Chrysos, Grigorios G.; Cevher, Volkan

Self-Supervised Neural Architecture Search for Imbalanced Datasets Technical Report

2021.

1190.

Zeng, Dewen; Ding, Yukun; Yuan, Haiyun; Huang, Meiping; Xu, Xiaowei; Zhuang, Jian; Hu, Jingtong; Shi, Yiyu

Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography Technical Report

2021.

1189.

Pellatt, Lloyd; Roggen, Daniel

Fast Deep Neural Architecture Search for Wearable Activity Recognition by Early Prediction of Converged Performance Proceedings Article

In: 2021 International Symposium on Wearable Computers, pp. 1–6, Association for Computing Machinery, Virtual, USA, 2021, ISBN: 9781450384629.

Abstract | Links | BibTeX

1188.

Pan, Chao; Yao, Xin

Neural Architecture Search Based on Evolutionary Algorithms with Fitness Approximation Proceedings Article

In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, 2021.

1187.

Bingham, Garrett; Miikkulainen, Risto

AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks Technical Report

2021.

1186.

Feng, Guosheng; Wang, Chunnan; Wang, Hongzhi

Search For Deep Graph Neural Networks Technical Report

2021.

1185.

Zhu, Wentao; Kong, Tianlong; Lu, Shun; Li, Jixiang; Zhang, Dawei; Deng, Feng; Wang, Xiaorui; Yang, Sen; Liu, Ji

SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification Proceedings Article

In: In Proceedings of IEEE Automatic Speech Recognition and Understanding (ASRU 2021), 2021, 2021.

1184.

Zhang, Yongan; You, Haoran; Fu, Yonggan; Geng, Tong; Li, Ang; Lin, Yingyan

G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency Technical Report

2021.

1183.

So, David R.; Manke, Wojciech; Liu, Hanxiao; Dai, Zihang; Shazeer, Noam; Le, Quoc V.

Primer: Searching for Efficient Transformers for Language Modeling Technical Report

2021.

3282 entries « ‹ 42 of 66 › »