Robust Inverse Framework using Knowledge-guided Self-Supervised Learning-Reference-Cited by-同舟云学术

Robust Inverse Framework using Knowledge-guided Self-Supervised Learning

Published:2022-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Ghosh Rahul¹,Renganathan Arvind¹,Tayal Kshitij¹,Li Xiang¹,Khandelwal Ankush¹,Jia Xiaowei²,Duffy Christopher³,Nieber John¹,Kumar Vipin¹

Affiliation:

1. University of Minnesota - Twin Cities, Minneapolis, MN, USA

2. University of Pittsburgh, Pittsburgh, PA, USA

3. Penn State University, State College, PA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539448

Reference38 articles.

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2. Camila Alvarez-Garreton et al. 2018. The CAMELS-CL dataset: Catchment attributes and meteorology for large sample studies-Chile dataset. Hydrology and Earth System Sciences (2018). Camila Alvarez-Garreton et al. 2018. The CAMELS-CL dataset: Catchment attributes and meteorology for large sample studies-Chile dataset. Hydrology and Earth System Sciences (2018).

3. Lynton Ardizzone et al. 2018. Analyzing inverse problems with invertible neural networks. arXiv preprint arXiv:1808.04730 (2018). Lynton Ardizzone et al. 2018. Analyzing inverse problems with invertible neural networks. arXiv preprint arXiv:1808.04730 (2018).

4. Keith Beven . 202 0. Deep learning, hydrological processes and the uniqueness of place. Hydrological Processes ( 2020 ). Keith Beven. 2020. Deep learning, hydrological processes and the uniqueness of place. Hydrological Processes (2020).

5. Vinicius B.P. Chagas et al . 2020 . CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in Brazil. Earth System Science Data ( 2020). Vinicius B.P. Chagas et al. 2020. CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in Brazil. Earth System Science Data (2020).

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