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.

4218 entries « ‹ 22 of 85 › »

2024

3168.

Lawton, Neal; Galstyan, Aram; Steeg, Greg Ver

Learning Morphisms with Gauss-Newton Approximation for Growing Networks Technical Report

2024.

3167.

Wu, Ning; Huang, Han; Xu, Yueting; Hao, Zhifeng

Zero-Shot NAS via the Suppression of Local Entropy Decrease Technical Report

2024.

3166.

Pan, Yuhan; Sun, Yanan; Gong, Wei

An Architectural Approach to Enhance Deep Long-Tailed Learning Technical Report

2024.

3165.

Wang, Hanming; Li, Yunlong; Wu, Zijun; Wang, Huifen; Zhang, Yuan

SASE: A Searching Architecture for Squeeze and Excitation Operations Miscellaneous

2024.

3164.

Mao, Wei; Liu, Dingbang; Zhou, Haoxiang; Li, Fuyi; Li, Kai; Wu, Qiuping; Yang, Jiaqi; Cheng, Quan; Zhang, Liuyang; Yu, Hao

A 28-nm 135.19 TOPS/W Bootstrapped-SRAM Compute-in-Memory Accelerator With Layer-Wise Precision and Sparsity Journal Article

In: IEEE Transactions on Circuits and Systems I: Regular Papers, pp. 1-11, 2024.

3163.

Wu, Tingting; Min, Ruyi; Song, Peixuan; Guo, Hengtao; Zeng, Tieyong; Fan, Feng-Lei

Retinal Vessel Segmentation via Neuron Programming Technical Report

2024.

3162.

Sridhar, Arjun; Chang, Chen-Chia; Zhang, Junyao; Chen, Yiran

Improving Routability Prediction via NAS Using a Smooth One-shot Augmented Predictor Technical Report

2024.

3161.

Wang, Yancheng; Sun, Dongfang; Yang, Yingzhen

Differentiable Attention Proceedings Article

In: Workshop on Machine Learning and Compression, NeurIPS 2024, 2024.

3160.

Sridhar, Arjun; Chen, Yiran

Delta-NAS: Difference of Architecture Encoding for Predictor-based Evolutionary Neural Architecture Search Technical Report

2024.

3159.

Wu, Yiliang; Luo, Xuliang; Guo, Fengchan; Lu, Tinghui; Liu, Cuimei

Research on multi-scenario adaptive acoustic encoders based on neural architecture search Journal Article

In: Frontiers in Physics, vol. 12, 2024, ISSN: 2296-424X.

3158.

Tzoumanekas, Georgios; Chatzianastasis, Michail; Ilias, Loukas; Kiokes, George; Psarras, John; Askounis, Dimitris

A Graph Neural Architecture Search Approach for Identifying Bots in Social Media Technical Report

2024.

3157.

Angeles-García, Yoqsan; Azueto-Ríos, Álvaro; Calvo, Hiram

Neural Architecture Search Using DyNNO for Air Pollution Forecasting Proceedings Article

In: Mata-Rivera, Miguel Félix; Zagal-Flores, Roberto; Barria-Huidobro, Cristian (Ed.): Telematics and Computing, pp. 80–93, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-77290-0.

Abstract | BibTeX

3156.

Cortês, Gabriel; Lourenço, Nuno; Machado, Penousal

GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NAS Miscellaneous

2024.

3155.

Achamrah, Fatima Ezzahra

Leveraging Transfer Learning in Deep Reinforcement Learning for Solving Combinatorial Optimization Problems Under Uncertainty Journal Article

In: IEEE Access, vol. 12, pp. 181477-181497, 2024.

3154.

Wang, Chao; Zhao, Jiaxuan; Li, Lingling; Jiao, Licheng; Liu, Fang; Liu, Xu; Yang, Shuyuan

Knowledge-aware Evolutionary Graph Neural Architecture Search Technical Report

2024.

3153.

Malettira, Prajna G.; Negi, Shubham; Ponghiran, Wachirawit; Roy, Kaushik

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Technical Report

2024.

3152.

Kuş, Zeki; Kiraz, Berna; Aydin, Musa; Kiraz, Alper

BioNAS: Neural Architecture Search for Multiple Data Modalities in Biomedical Image Classification Proceedings Article

In: Garcia, Fausto P.; Jamil, Akhtar; Hameed, Alaa Ali; Ortis, Alessandro; Ramirez, Isaac Segovia (Ed.): Recent Trends and Advances in Artificial Intelligence, pp. 539–550, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-70924-1.

Abstract | BibTeX

@inproceedings{10.1007/978-3-031-70924-1_41,

title = {BioNAS: Neural Architecture Search for Multiple Data Modalities in Biomedical Image Classification},

author = {Zeki Kuş and Berna Kiraz and Musa Aydin and Alper Kiraz},

editor = {Fausto P. Garcia and Akhtar Jamil and Alaa Ali Hameed and Alessandro Ortis and Isaac Segovia Ramirez},

isbn = {978-3-031-70924-1},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Recent Trends and Advances in Artificial Intelligence},

pages = {539–550},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Neural Architecture Search (NAS) for biomedical image classification has the potential to design highly efficient and accurate networks automatically for tasks from different modalities. This paper presents BioNAS, a new NAS approach designed for multi-modal biomedical image classification. Unlike other methods, BioNAS dynamically adjusts the number of stacks, modules, and feature maps in the network to improve both performance and complexity. The proposed approach utilizes an opposition-based differential evolution optimization technique to identify the optimal network structure. We have compared our methods on two public multi-class classification datasets with different data modalities: DermaMNIST and OrganCMNIST. BioNAS outperforms hand-designed networks, automatic machine learning frameworks, and most NAS studies in terms of accuracy (ACC) and area under the curve (AUC) on the OrganCMNIST and DermaMNIST datasets. The proposed networks significantly outperform all other methods on the DermaMNIST dataset, achieving accuracy improvements of up to 4.4 points and AUC improvements of up to 2.6 points, and also surpass other studies by up to 5.4 points in accuracy and 0.6 points in AUC on OrganCMNIST. Moreover, the proposed networks have fewer parameters than hand-designed architectures like ResNet-18 and ResNet-50. The results indicate that BioNAS has the potential to be an effective alternative to hand-designed networks and automatic frameworks, offering a competitive solution in the classification of biomedical images.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

3151.

Tang, Yi; Liu, Min; Li, Baopu; Wang, Yaonan; Ouyang, Wanli

NAS-PED: Neural Architecture Search for Pedestrian Detection Journal Article

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-18, 2024.

3150.

Pan, Wenxuan; Zhao, Feifei; Shen, Guobin; Han, Bing; Zeng, Yi

Brain-Inspired Multi-Scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks Journal Article

In: IEEE Transactions on Evolutionary Computation, pp. 1-1, 2024.

3149.

Li, Yuxuan; Guo, Yunhui

GradAlign for Training-free Model Performance Inference Technical Report

2024.

3148.

Bercovich, Akhiad; Ronen, Tomer; Abramovich, Talor; Ailon, Nir; Assaf, Nave; Dabbah, Mohammad; Galil, Ido; Geifman, Amnon; Geifman, Yonatan; Golan, Izhak; Haber, Netanel; Karpas, Ehud; Koren, Roi; Levy, Itay; Molchanov, Pavlo; Mor, Shahar; Moshe, Zach; Nabwani, Najeeb; Puny, Omri; Rubin, Ran; Schen, Itamar; Shahaf, Ido; Tropp, Oren; Argov, Omer Ullman; Zilberstein, Ran; El-Yaniv, Ran

Puzzle: Distillation-Based NAS for Inference-Optimized LLMs Technical Report

2024.

3147.

Cassimon, Amber; Mercelis, Siegfried; Mets, Kevin

Task Adaptation of Reinforcement Learning-based NAS Agents through Transfer Learning Technical Report

2024.

3146.

Wang, Yuzhan; Liu, Sicong; Guo, Bin; Zhang, Boqi; Ma, Ke; Ding, Yasan; Luo, Hao; Li, Yao; Yu, Zhiwen

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Technical Report

2024.

3145.

Zhu, Guanghui; Ji, Zipeng; Chen, Jingyan; Wang, Limin; Yuan, Chunfeng; Huang, Yihua

SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture Search Technical Report

2024.

3144.

Rahman, Md Hafizur; Rizvee, Md Mashfiq; Shomaji, Sumaiya; Chakraborty, Prabuddha

ILASH: A Predictive Neural Architecture Search Framework for Multi-Task Applications Technical Report

2024.

3143.

Huangfu, Hancong

Application Research of Neural Network Architecture Search in Big Data Analysis Proceedings Article

In: 2024 IEEE 7th International Conference on Information Systems and Computer Aided Education (ICISCAE), pp. 545-551, 2024.

3142.

Henwood, Sébastien; Savaria, Yvon; Leduc-Primeau, François

MemNAS: Super-net Neural Architecture Search for Memristor-based DNN Accelerators Proceedings Article

In: 2024 IEEE Workshop on Signal Processing Systems (SiPS), pp. 1-6, 2024.

3141.

Krstic, Lazar; Ivanovic, Milos; Simic, Visnja; Stojanovic, Boban

Evolutionary approach for composing a thoroughly optimized ensemble of regression neural networks Journal Article

In: Egyptian Informatics Journal, vol. 28, pp. 100581, 2024, ISSN: 1110-8665.

Abstract | Links | BibTeX

3140.

Rahman, Md Hafizur; Rizvee, Md Mashfiq; Shomaji, Sumaiya; Chakraborty, Prabuddha

ILASH: A Predictive Neural Architecture Search Framework for Multi-Task Applications Technical Report

2024.

3139.

Trirat, Patara; Lee, Jae-Gil

PASTA: Neural Architecture Search for Anomaly Detection in Multivariate Time Series Journal Article

In: IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-16, 2024.

3138.

Chen, Xingchi; Zhang, Shuanglong; Li, Qing; Zhu, Fa; Feng, Ansong; Nkenyereye, Lewis; Rani, Shalli

A NAS-Based TinyML for Secure Authentication Detection on SAGVN-Enabled Consumer Edge Devices Technical Report

2024.

3137.

Wang, Ti; Du, Zhongyan; Wang, Xiaobo; Yang, Shan; Ye, Haina

Research on Key Technologies of Visual Semantic Segmentation and Localization for Autonomous Vehicles Proceedings Article

In: 2024 5th International Conference on Computer Engineering and Intelligent Control (ICCEIC), pp. 308-312, 2024.

3136.

Liardo, Anthony; Ray, Aritra; Firouzi, Farshad; Lafata, Kyle; Chakrabarty, Krishnendu

Neural Architecture Search for Blood Glucose Prediction in Type-1 Diabetics Proceedings Article

In: 2024 IEEE 20th International Conference on Body Sensor Networks (BSN), pp. 1-4, 2024.

3135.

Franco-Gaona, Erick; Avila-Garcia, Maria-Susana; Cruz-Aceves, Ivan; Orocio-Garcia, Hiram-Efrain; Escobedo-Gordillo, Andres; Brieva, Jorge

Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis Proceedings Article

In: 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM), pp. 1-4, 2024.

3134.

Omole, Oluwatoyin Joy; Rosa, Renata Lopes; Saadi, Muhammad; Rodriguez, Demóstenes Zegarra

AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases Journal Article

In: AI, vol. 5, no. 4, pp. 2945–2966, 2024, ISSN: 2673-2688.

Abstract | Links | BibTeX

3133.

Li, Jianfeng; Zhang, Jiawen; Wang, Feng; Ma, Lianbo

HEP-NAS: Towards Efficient Few-shot Neural Architecture Search via Hierarchical Edge Partitioning Technical Report

2024.

3132.

SUZUKI, Shota; ONO, Satoshi

Self-supervised Neural Architecture Search for Multimodal Deep Neural Networks Journal Article

In: IEICE Transactions on Information and Systems, vol. advpub, pp. 2024EDL8018, 2024.

3131.

Ramírez-Gordillo, Tamai; Mora, Higinio; Pujol, Francisco A.; Maciá-Lillo, Antonio

Federated Neural Architecture Search for Efficient and Privacy-Preserving Model Training Proceedings Article

In: 2024 IEEE International Conference on Smart Internet of Things (SmartIoT), pp. 129-136, 2024.

3130.

Nieber, Christian; Dias, Douglas Mota; Garcia, Enrique Naredo; Ryan, Conor

Step Size Control in Evolutionary Algorithms for Neural Architecture Search Proceedings Article

In: Marcelloni, Francesco; Madani, Kurosh; Stein, Niki; Joaquim, Joaquim (Ed.): Proceedings of the 16th International Joint Conference on Computational Intelligence, IJCCI 2024, pp. 288–295, Science and Technology Publications, Lda, 2024, ISBN: 9789897587214, (Publisher Copyright: © 2024 by SCITEPRESS – Science and Technology Publications, Lda.; 16th International Joint Conference on Computational Intelligence, IJCCI 2024 ; Conference date: 20-11-2024 Through 22-11-2024).

Abstract | Links | BibTeX

@inproceedings{8de6b06884fb47c39c21435afd300c51,

title = {Step Size Control in Evolutionary Algorithms for Neural Architecture Search},

author = {Christian Nieber and Douglas Mota Dias and Enrique Naredo Garcia and Conor Ryan},

editor = {Francesco Marcelloni and Kurosh Madani and Niki Stein and Joaquim Joaquim},

url = {https://pure-test.ul.ie/en/publications/step-size-control-in-evolutionary-algorithms-for-neural-architect},

doi = {10.5220/0013013800003837},

isbn = {9789897587214},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 16th International Joint Conference on Computational Intelligence, IJCCI 2024},

pages = {288–295},

publisher = {Science and Technology Publications, Lda},

series = {International Joint Conference on Computational Intelligence},

abstract = {This work examines how evolutionary Neural Architecture Search (NAS) algorithms can be improved by controlling the step size of the mutation of numerical parameters. The proposed NAS algorithms are based on F-DENSER, a variation of Dynamic Structured Grammatical Evolution (DSGE). Overall, a (1+5) Evolutionary Strategy is used. Two methods of controlling the step size of mutations of numeric values are compared to Random Search and F-DENSER: Decay of the step size over time and adaptive step size for mutations. The search for lightweight, LeNet-like CNN architectures for MNIST classification is used as a benchmark, optimizing for both accuracy and small architectures. An architecture is described by about 30 evolvable parameters. Experiments show that with step size control, convergence is faster, better performing neural architectures are found on average, and with lower variance. The smallest architecture found during the experiments reached an accuracy of 98.8% on MNIST with only 5,450 free parameters, compared to the 62,158 parameters of LeNet-5.},

note = {Publisher Copyright: © 2024 by SCITEPRESS – Science and Technology Publications, Lda.; 16th International Joint Conference on Computational Intelligence, IJCCI 2024 ; Conference date: 20-11-2024 Through 22-11-2024},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

3129.

Mo, Shibing; Wu, Kai; Gao, Qixuan; Teng, Xiangyi; Liu, Jing

AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural Networks Technical Report

2024.

3128.

Lee, Matthew; Bragman, Felix John Samuel; Sanchez-Matilla, Ricardo; Luengo, Imanol; Stoyanov, Danail

Spatial-Temporal NAS for Fast Surgical Segmentation Proceedings Article

In: 35th British Machine Vision Conference 2024, BMVC 2024, Glasgow, UK, November 25-28, 2024, BMVA, 2024.

3127.

Fang, Yujing; Lu, Hang; Xiao, Dongming; Li, Jiebo

Attention-boosted path exploration neural architecture search for fault diagnosis of rotating machinery Journal Article

In: IEEE Sensors Journal, pp. 1-1, 2024.

3126.

Muscalagiu, Anca-Ioana

Quantum Neural Network Design via Quantum Deep Reinforcement Learning Conference

Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA, INSTICC SciTePress, 2024, ISBN: 978-989-758-721-4.

3125.

Yang, Yuqian; Zhao, Cong

KNAS-TP: Domain Knowledge-driven Automated Construction of Uplink and Downlink Throughput Predictor in 5G Proceedings Article

In: 2024 16th International Conference on Communication Software and Networks (ICCSN), pp. 173-178, 2024.

3124.

Oh, Youngmin; Lee, Hyunju; Ham, Bumsub

Efficient Few-Shot Neural Architecture Search by Counting the Number of Nonlinear Functions Technical Report

2024.

3123.

Yaoming, Wang; Yuchen, Liu; Wenrui, Dai; Chenglin, Li; Xiaopeng, Zhang; Junni, Zou; Hongkai, Xiong

Variational Dropout for Differentiable Neural Architecture Search Journal Article

In: Chinese Journal of Electronics, vol. 34, pp. 1-18, 2024.

3122.

Sarmiento-Rosales, Sergio; Escalona-Llaguno, Martha

Comparative study on predicting dose equivalent rate using advanced machine learning techniques and time series models Proceedings Article

In: 2024.

3121.

Zhang, Renjie; Lin, Di; Wang, Xin; Baciu, George; Chen, C. L. Philip; Li, Ping

Accurate-PGNet: Learning to Assemble Perceptual Body Parts for Accurate Human Skeleton Establishment Journal Article

In: IEEE Transactions on Multimedia, pp. 1-16, 2024.

3120.

Mao, Shouren; Qin, Minghao; Dong, Wei; Liu, Huajian; Gao, Yongzhuo

RAM-NAS: Resource-aware Multiobjective Neural Architecture Search Method for Robot Vision Tasks Proceedings Article

In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2712-2719, 2024.

3119.

RV, Rashmitha

Automated ASIC Design Optimization Using Neural Architecture Search Techniques Journal Article

In: International Journal of Current Research in Science, Engineering & Technology, vol. 7, pp. 95-102, 2024.

4218 entries « ‹ 22 of 85 › »