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Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc."},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2019-1873"},{"key":"e_1_3_2_2_68_1","volume-title":"Yusuke Tashiro, Lei You, Pawel Herman, Mats Nordahl, Ruibo Tu, and Vilhelm von Ehrenheim.","author":"Senane Zineb","year":"2024","unstructured":"Zineb Senane, Lele Cao, Valentin Leonhard Buchner, Yusuke Tashiro, Lei You, Pawel Herman, Mats Nordahl, Ruibo Tu, and Vilhelm von Ehrenheim. 2024. Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask. (2024). arXiv:2405.05959"},{"key":"e_1_3_2_2_69_1","volume-title":"Multi-Time Attention Networks for Irregularly Sampled Time Series. 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Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc."},{"key":"e_1_3_2_2_83_1","volume-title":"DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model. arXiv preprint arXiv:2306.01001","author":"Wang Zhixian","year":"2023","unstructured":"Zhixian Wang, Qingsong Wen, Chaoli Zhang, Liang Sun, and Yi Wang. 2023. DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model. arXiv preprint arXiv:2306.01001 (2023)."},{"key":"e_1_3_2_2_84_1","volume-title":"DiffSTG: Probabilistic spatio-temporal graph forecasting with denoising diffusion models. arXiv preprint arXiv:2301.13629","author":"Wen Haomin","year":"2023","unstructured":"Haomin Wen, Youfang Lin, Yutong Xia, Huaiyu Wan, Roger Zimmermann, and Yuxuan Liang. 2023. 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PMLR, 25038--25054."},{"key":"e_1_3_2_2_91_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108606"},{"key":"e_1_3_2_2_92_1","volume-title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","author":"Yi Xiuwen","year":"2016","unstructured":"Xiuwen Yi, Yu Zheng, Junbo Zhang, and Tianrui Li. 2016. ST-MVL: filling missing values in geo-sensory time series data. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (New York, New York, USA) (IJCAI'16). AAAI Press, 2704--2710."},{"key":"e_1_3_2_2_93_1","volume-title":"Time-series generative adversarial networks. Advances in neural information processing systems 32","author":"Yoon Jinsung","year":"2019","unstructured":"Jinsung Yoon, Daniel Jarrett, and Mihaela Van der Schaar. 2019. Time-series generative adversarial networks. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_2_94_1","unstructured":"Jinsung Yoon William R. Zame and Mihaela van der Schaar. 2017. Multidirectional Recurrent Neural Networks: A Novel Method for Estimating Missing Data. https:\/\/api.semanticscholar.org\/CorpusID:39429412"},{"key":"e_1_3_2_2_95_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"e_1_3_2_2_96_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"e_1_3_2_2_97_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467401"},{"key":"e_1_3_2_2_98_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.427"},{"key":"e_1_3_2_2_99_1","volume-title":"Unsupervised Feature Learning with Data Augmentation for Control Valve Stiction Detection. In 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 1385--1390","author":"Zhang Kexin","year":"2021","unstructured":"Kexin Zhang and Yong Liu. 2021. 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