快速访问

📚
参考文献
📄
完整PDF
ℹ️
文章信息
查看PDF

文章信息

收稿日期 2024年4月29日
发表日期 2024年4月29日
DOI https://doi.org/10.54254/3050-2187/1/2024001
文章类型 研究论文

参考文献

[1]Klaus Greff et al. "LSTM: A Search Space Odyssey." IEEE Transactions on Neural Networks and Learning Systems, 28 (2015): 2222-2232. https://doi.org/10.1109/tnnls.2016.2582924.
[2]Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-9. https://doi.org/10.1109/CVPR.2015.7298594.
[3]Lee, J., Jun, S., Cho, Y., Lee, H., Kim, G., Seo, J., & Kim, N. (2017). Deep Learning in Medical Imaging: General Overview. Korean Journal of Radiology, 18, 570 - 584. https://doi.org/10.3348/kjr.2017.18.4.570.
[4]Klyuchnikov, N., Trofimov, I., Artemova, E., Salnikov, M., Fedorov, M., & Burnaev, E. (2020). NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing. IEEE Access, PP, 1-1. https://doi.org/10.1109/access.2022.3169897.
[5]Lu, Z., Whalen, I., Dhebar, Y., Deb, K., Goodman, E., Banzhaf, W., & Boddeti, V. (2019). Multiobjective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification. IEEE Transactions on Evolutionary Computation, 25, 277-291. https://doi.org/10.1109/TEVC.2020.3024708.
[6]Zhang, T., Lei, C., Zhang, Z., Meng, X., & Chen, C. (2021). AS-NAS: Adaptive Scalable Neural Architecture Search With Reinforced Evolutionary Algorithm for Deep Learning. IEEE Transactions on Evolutionary Computation, 25, 830-841. https://doi.org/10.1109/TEVC.2021.3061466.
[7]Sun, Y., Sun, X., Fang, Y., Yen, G., & Liu, Y. (2020). A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms. IEEE Transactions on Evolutionary Computation, 25, 524-536. https://doi.org/10.1109/TEVC.2021.3055076.
[8]Verma, M., Sinha, P., Goyal, K., Verma, A., & Susan, S. (2019). A Novel Framework for Neural Architecture Search in the Hill Climbing Domain. 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), 1-8. https://doi.org/10.1109/AIKE.2019.00009.
[9]Zhang, H., Jin, Y., Cheng, R., & Hao, K. (2020). Efficient Evolutionary Search of Attention Convolutional Networks via Sampled Training and Node Inheritance. IEEE Transactions on Evolutionary Computation, 25, 371-385. https://doi.org/10.1109/TEVC.2020.3040272.
[10]Liang, H., Zhang, S., Sun, J., He, X., Huang, W., Zhuang, K., & Li, Z. (2019). DARTS+: Improved Differentiable Architecture Search with Early Stopping. ArXiv, abs/1909.06035.
[11]Li, L., & Talwalkar, A. (2019). Random Search and Reproducibility for Neural Architecture Search. ArXiv, abs/1902.07638.
[12]Chu, X., Zhou, T., Zhang, B., & Li, J. (2019). Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search. ArXiv, abs/1911.12126. https://doi.org/10.1007/978-3-030-58555-6_28.
[13]Heuillet, A., Tabia, H., Arioui, H., & Youcef-Toumi, K. (2021). D-DARTS: Distributed Differentiable Architecture Search. ArXiv, abs/2108.09306.
[14]Cummings, D., Sarah, A., Sridhar, S., Szankin, M., Muñoz, J., & Sundaresan, S. (2022). A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities. ArXiv, abs/2205.10358.https://doi.org/10.48550/arXiv.2205.10358.
[15]Ying, C., Klein, A., Real, E., Christiansen, E., Murphy, K., & Hutter, F. (2019). NAS-Bench-101: Towards Reproducible Neural Architecture Search. ArXiv, abs/1902.09635.
[16]Cassimon, T., Vanneste, S., Bosmans, S., Mercelis, S., & Hellinckx, P. (2019). Using Neural Architecture Search to Optimize Neural Networks for Embedded Devices. , 684-693. https://doi.org/10.1007/978-3-030-33509-0_64.
[17]Cheng, A., Dong, J., Hsu, C., Chang, S., Sun, M., Chang, S., Pan, J., Chen, Y., Wei, W., & Juan, D. (2018). Searching Toward Pareto-Optimal Device-Aware Neural Architectures. 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 1-7. https://doi.org/10.1145/3240765.3243494.
[18]Mo.zejko, M., Latkowski, T., Treszczotko, L., Szafraniuk, M., & Trojanowski, K. (2020). Superkernel Neural Architecture Search for Image Denoising. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2002-2011. https://doi.org/10.1109/cvprw50498.2020.00250.
[19]Lu, Z., Whalen, I., Dhebar, Y., Deb, K., Goodman, E., Banzhaf, W., & Boddeti, V. (2019). Multiobjective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification. IEEE Transactions on Evolutionary Computation, 25, 277-291. https://doi.org/10.1109/TEVC.2020.3024708.
[20]Meng, F., & Wang, Y. (2023). Transformers: Statistical interpretation, architectures and applications. Authorea Preprints.
[21]Fanfei Meng, Branden Ghena. (2023) Research on Text Recognition Methods Based on Artificial In-telligence and Machine Learning. Advances in Computer and Communication, 4(5), 340-344.
[22]Meng, F., & Demeter, D. (2023). Sentiment analysis with adaptive multi-head attention in Transformer. arXiv preprint arXiv:2310.14505.
[23]Manijeh Razeghi, Arash Dehzangi, Donghai Wu, Ryan McClintock, Yiyun Zhang, Quentin Durlin, Jiakai Li, and Fanfei Meng. Antimonite-based gap-engineered type-ii superlattice materials grown by mbe and mocvd for the third generation of infrared imagers. In Infrared Technology and Applications XLV, volume 11002, pages 108–125. SPIE, 2019.
[24]Meng, F., Zhang, L., Chen, Y., & Wang, Y. (2023). FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation. Authorea Preprints.
[25]Meng, F., Zhang, L., Chen, Y., & Wang, Y. (2023). Sample-based Dynamic Hierarchical Transformer with Layer and Head Flexibility via Contextual Bandit. Authorea Preprints.
[26]Meng, F., & Wang, C. A. (2023). A Dynamic Interactive Learning Interface for Computer Science Education: Programming Decomposition Tool. Authorea Preprints.
[27]Chang Ling, Chonglei Zhang, Mingqun Wang, Fanfei Meng, Luping Du, and Xiaocong Yuan, "Fast structured illumination microscopy via deep learning," Photon. Res. 8, 1350-1359 (2020)
[28]Meng, F., Jagadeesan, L., & Thottan, M. (2021). Model-based reinforcement learning for service mesh fault resiliency in a web application-level. arXiv preprint arXiv:2110.13621.
[29]Wang, Y., Meng, F., Wang, X., & Xie, C. (2023). Optimizing the Passenger Flow for Airport Security Check. arXiv preprint arXiv:2312.05259.
[30]Chen, Jin-Jin, et al. "A dataset of diversity and distribution of rodents and shrews in China." Scientific Data 9.1 (2022): 304
[31]Meng, F., Zhang, L., Wang, Y., & Zhao, Y. (2023). Joint detection algorithm for multiple cognitive users in spectrum sensing. Authorea Preprints.
[32]Fanfei Meng, Branden Ghena. (2023) Research on Text Recognition Methods Based on Artificial In-telligence and Machine Learning. Advances in Computer and Communication, 4(5), 340-344.
[33]Meng, F., & Demeter, D. (2023). Sentiment analysis with adaptive multi-head attention in Transformer. arXiv preprint arXiv:2310.14505.
[34]Manijeh Razeghi, Arash Dehzangi, Donghai Wu, Ryan McClintock, Yiyun Zhang, Quentin Durlin, Jiakai Li, and Fanfei Meng. Antimonite-based gap-engineered type-ii superlattice materials grown by mbe and mocvd for the third generation of infrared imagers. In Infrared Technology and Applications XLV, volume 11002, pages 108–125. SPIE, 2019.
[35]Meng, F., Zhang, L., Chen, Y., & Wang, Y. (2023). FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation. Authorea Preprints.
[36]Meng, F., Zhang, L., Chen, Y., & Wang, Y. (2023). Sample-based Dynamic Hierarchical Transformer with Layer and Head Flexibility via Contextual Bandit. Authorea Preprints.
[37]Meng, F., & Wang, C. A. (2023). A Dynamic Interactive Learning Interface for Computer Science Education: Programming Decomposition Tool. Authorea Preprints.
[38]Chang Ling, Chonglei Zhang, Mingqun Wang, Fanfei Meng, Luping Du, and Xiaocong Yuan, "Fast structured illumination microscopy via deep learning," Photon. Res. 8, 1350-1359 (2020)
[39]Meng, F., Jagadeesan, L., & Thottan, M. (2021). Model-based reinforcement learning for service mesh fault resiliency in a web application-level. arXiv preprint arXiv:2110.13621.
[40]Wang, Y., Meng, F., Wang, X., & Xie, C. (2023). Optimizing the Passenger Flow for Airport Security Check. arXiv preprint arXiv:2312.05259.
[41]Chen, Jin-Jin, et al. "A dataset of diversity and distribution of rodents and shrews in China." Scientific Data 9.1 (2022): 304
[42]Meng, F., Zhang, L., Wang, Y., & Zhao, Y. (2023). Joint detection algorithm for multiple cognitive users in spectrum sensing. Authorea Preprints.
[43]Meng, F., & Wang, Y. (2023). Transformers: Statistical interpretation, architectures and applications. Authorea Preprints.
[44]Fanfei Meng, Chen-Ao Wang, Alexander Brown. Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization. TechRxiv. February 14, 2024.

版权与开放获取声明

作为一本开放获取的学术期刊，所有文章均遵循 Creative Commons Attribution 4.0 International License (CC BY 4.0) 协议发布，允许用户在署名原作者的前提下自由共享与再利用内容。所有文章均可免费供读者和机构阅读、下载、引用与传播，EWA Publishing 不会通过期刊的出版发行向读者或机构收取任何费用。