Page 48 - 《软件学报》2025年第5期
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[65] Gupta A, Levin R. The online submodular cover problem. In: Proc. of the 2020 ACM-SIAM Symp. on Discrete Algorithms. Salt Lake
City: SIAM, 2020. 1525–1537. [doi: 10.1137/1.9781611975994.94]
[66] Iyer RK, Khargoankar N, Bilmes JA, Asanani H. Submodular combinatorial information measures with applications in machine learning.
In: Proc. of the 32nd Algorithmic Learning Theory. ALT, 2021. 722–754.
[67] Kothawade S, Savarkar A, Iyer V, Ramakrishnan G, Iyer R. CLINICAL: Targeted active learning for imbalanced medical image
classification. In: Proc. of the 1st Workshop on Medical Image Learning with Limited and Noisy Data. Singapore: Springer, 2022.
119–129. [doi: 10.1007/978-3-031-16760-7_12]
[68] Chen H, Tao R, Fan Y, Wang YD, Wang JD, Schiele B, Xie X, Raj B, Savvides M. SoftMatch: Addressing the quantity-quality trade-off
in semi-supervised learning. arXiv:2301.10921, 2023.
[69] Sener O, Savarese S. Active learning for convolutional neural networks: A core-set approach. In: Proc. of the 6th Int’l Conf. on Learning
Representations. Vancouver: OpenReview.net, 2018.
[70] Belouadah E, Popescu A, Aggarwal U, Saci L. Active class incremental learning for imbalanced datasets. In: Proc. of the 2020 European
Conf. on Computer Vision. Glasgow: Springer, 2020. 146–162. [doi: 10.1007/978-3-030-65414-6_12]
[71] Kaplan J, McCandlish S, Henighan T, Brown TB, Chess B, Child R, Gray S, Radford A, Wu J, Amodei D. Scaling laws for neural
language models. arXiv:2001.08361, 2020.
[72] Cao KD, Wei C, Gaidon A, Arechiga N, Ma TY. Learning imbalanced datasets with label-distribution-aware margin loss. In: Proc. of the
33rd Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2019. 140.
[73] Goh HW, Mueller J. ActiveLab: Active learning with re-labeling by multiple annotators. arXiv:2301.11856, 2023.
[74] Goh HW, Tkachenko U, Mueller J. CROWDLAB: Supervised learning to infer consensus labels and quality scores for data with multiple
annotators. arXiv:2210.06812, 2022.
[75] Northcutt C, Jiang L, Chuang I. Confident learning: Estimating uncertainty in dataset labels. Journal of Artificial Intelligence Research,
2021, 70: 1373–1411. [doi: 10.1613/jair.1.12125]
[76] Chong D, Hong J, Manning C. Detecting label errors by using pre-trained language models. In: Proc. of the 2022 Conf. on Empirical
Methods in Natural Language Processing. Abu Dhabi: ACL, 2022. 9074–9091. [doi: 10.18653/v1/2022.emnlp-main.618]
附中文参考文献:
[34] 翟宇鹏, 洪玫, 杨秋辉. 功能需求到测试用例的可追溯性研究. 计算机科学, 2017, 44(11A): 480–484.
董黎明(1994-), 女, 博士, 主要研究领域为软件 孟庆龙(1999-), 男, 硕士生, 主要研究领域为软
工程, 软件研发效能, 软件过程, 软件可追踪性, 件工程, 软件研发效能.
可信人工智能.
张贺(1971-), 男, 博士, 教授, 博士生导师, CCF 匡宏宇(1985-), 男, 博士, 助理研究员, CCF 专
高级会员, 主要研究领域为软件工程, 开发运维 业会员, 主要研究领域为软件可追踪性, 自动化
一体化, 软件研发效能, 软件安全, 经验及循证软 软件可追踪分析, 情绪分析, 文本分析, 程序分析.
件工程, 区块链.