[18] Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael Witbrock, Mark A Hasegawa-Johnson, and Thomas S Huang. Dilated recurrent neural networks. Advances in neural information processing systems, 30, 2017.
[19] Zipeng Chen, Qianli Ma, and Zhenxi Lin. Time-aware multi-scale rnns for time series modeling. In IJCAI, pages 2285-2291, 2021.
[20] Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Michael Blumenstein, and Jing Jiang. Omni-scale cnns: a simple and effective kernel size configuration for time series classification. In International Conference on Learning Representations, 2022.
[21] Víctor Campos, Brendan Jou, Xavier Giro-i Nieto, Jordi Torres, and Shih-Fu Chang. Skip rnn: Learning to skip state updates in recurrent neural networks. arXiv preprint arXiv:1708.06834, 2017.
[22] Hoang Anh Dau, Anthony Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, and Eamonn Keogh. The ucr time series archive. IEEE/CAA Journal of Automatica Sinica, 6(6):1293-1305, 2019.
[23] Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, and Masashi Sugiyama. Sample selection with uncertainty of losses for learning with noisy labels. In International Conference on Learning Representations, 2022.
[24] Dengyong Zhou, Olivier Bousquet, Thomas Lal, Jason Weston, and Bernhard Scholkopf. Learning with local and global consistency. Advances in neural information processing systems, 16, 2003.
[25] Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey. Symmetric cross entropy for robust learning with noisy labels. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 322-330, 2019.
[26] Xiong Zhou, Xianming Liu, Junjun Jiang, Xin Gao, and Xiangyang Ji. Asymmetric loss functions for learning with noisy labels. In International conference on machine learning, pages 12846-12856. PMLR, 2021.
[27] De Cheng, Yixiong Ning, Nannan Wang, Xinbo Gao, Heng Yang, Yuxuan Du, Bo Han, and Tongliang Liu. Class-dependent label-noise learning with cycle-consistency regularization. Advances in Neural Information Processing Systems, 35:11104-11116, 2022.
[28] Kilian Fatras, Bharath Bhushan Damodaran, Sylvain Lobry, Remi Flamary, Devis Tuia, and Nicolas Courty. Wasserstein adversarial regularization for learning with label noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
[29] Nazmul Karim, Mamshad Nayeem Rizve, Nazanin Rahnavard, Ajmal Mian, and Mubarak Shah. Unicon: Combating label noise through uniform selection and contrastive learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9676-9686, 2022.
[30] Deep Patel and PS Sastry. Adaptive sample selection for robust learning under label noise. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3932-3942, 2023.
[31] Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya Zhang, and Masashi Sugiyama. Masking: A new perspective of noisy supervision. Advances in neural information processing systems, 31, 2018.
[32] Zhaowei Zhu, Jialu Wang, and Yang Liu. Beyond images: Label noise transition matrix estimation for tasks with lower-quality features. In International Conference on Machine Learning, pages 27633-27653. PMLR, 2022.
[33] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. Advances in neural information processing systems, 33:20331-20342, 2020.