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  "Description": "Optimizers for 'torch' deep learning library. These\nfunctions include recent results published in the literature\nand are not part of the optimizers offered in 'torch'.\nProspective users should test these optimizers with their data,\nsince performance depends on the specific problem being solved.\nThe packages includes the following optimizers: (a) 'adabelief'\nby Zhuang et al (2020), <arXiv:2010.07468>; (b) 'adabound' by\nLuo et al.(2019), <arXiv:1902.09843>; (c) 'adahessian' by Yao\net al.(2021) <arXiv:2006.00719>; (d) 'adamw' by Loshchilov &\nHutter (2019), <arXiv:1711.05101>; (e) 'madgrad' by Defazio and\nJelassi (2021), <arXiv:2101.11075>; (f) 'nadam' by Dozat\n(2019), <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>;\n(g) 'qhadam' by Ma and Yarats(2019), <arXiv:1810.06801>; (h)\n'radam' by Liu et al. (2019), <arXiv:1908.03265>; (i) 'swats'\nby Shekar and Sochee (2018), <arXiv:1712.07628>; (j) 'yogi' by\nZaheer et al.(2019),\n<https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.",
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  "URL": "https://github.com/e-sensing/torchopt/",
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  "Date/Publication": "2023-06-08 19:23:07 UTC",
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