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Creating pull request for 10.21105.joss.03823 #2836

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161 changes: 161 additions & 0 deletions joss.03823/10.21105.joss.03823.crossref.xml
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<doi>10.2202/1544-6115.1406</doi>
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<unstructured_citation>Pytorch: An imperative style, high-performance deep learning library, Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and others, Advances in neural information processing systems, 32, 8026–8037, 2019</unstructured_citation>
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</citation>
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<doi>10.1109/cvpr.2007.383137</doi>
</citation>
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<unstructured_citation>Multimodal deep learning, Ngiam, Jiquan and Khosla, Aditya and Kim, Mingyu and Nam, Juhan and Lee, Honglak and Ng, Andrew Y, ICML, 2011</unstructured_citation>
</citation>
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</citation>
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<unstructured_citation>On deep multi-view representation learning, Wang, Weiran and Arora, Raman and Livescu, Karen and Bilmes, Jeff, International conference on machine learning, 1083–1092, 2015, PMLR</unstructured_citation>
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<doi>10.1109/tnn.2007.891186</doi>
</citation>
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<unstructured_citation>Deep Tensor CCA for Multi-view Learning, Wong, Hok Shing and Wang, Li and Chan, Raymond and Zeng, Tieyong, IEEE Transactions on Big Data, 2021, IEEE</unstructured_citation>
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