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Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_1_16_1","volume-title":"Examining covid-19 forecasting using spatio-temporal graph neural networks. arXiv preprint arXiv:2007.03113","author":"Kapoor Amol","year":"2020","unstructured":"Amol Kapoor , Xue Ben , Luyang Liu , Bryan Perozzi , Matt Barnes , Martin Blais , and Shawn O'Banion . 2020. Examining covid-19 forecasting using spatio-temporal graph neural networks. arXiv preprint arXiv:2007.03113 ( 2020 ). Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais, and Shawn O'Banion. 2020. Examining covid-19 forecasting using spatio-temporal graph neural networks. arXiv preprint arXiv:2007.03113 (2020)."},{"key":"e_1_3_2_1_17_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . 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Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_31_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Velivckovi\u0107 Petar","year":"2017","unstructured":"Petar Velivckovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Velivckovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_32_1","unstructured":"WHO. 2022. WHO Coronavirus (COVID-19) Dashboard. https:\/\/covid19.who.int\/. Accessed on 28 January 2022.  WHO. 2022. WHO Coronavirus (COVID-19) Dashboard. https:\/\/covid19.who.int\/. 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