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torchopt  

Advanced Optimizers for Torch
View on CRAN: Click here


Download and install torchopt package within the R console
Install from CRAN:
install.packages("torchopt")

Install from Github:
library("remotes")
install_github("cran/torchopt")

Install by package version:
library("remotes")
install_version("torchopt", "0.1.4")



Attach the package and use:
library("torchopt")
Maintained by
Gilberto Camara
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-04-25
Latest Update: 2023-06-06
Description:
Optimizers for 'torch' deep learning library. These functions include recent results published in the literature and are not part of the optimizers offered in 'torch'. Prospective users should test these optimizers with their data, since performance depends on the specific problem being solved.The packages includes the following optimizers: (a) 'adabelief' by Zhuang et al (2020), <doi:10.48550/arXiv.2010.07468>; (b) 'adabound' by Luo et al.(2019), <doi:10.48550/arXiv.1902.09843>; (c) 'adahessian' by Yao et al.(2021) <doi:10.48550/arXiv.2006.00719>; (d) 'adamw' by Loshchilov & Hutter (2019), <doi:10.48550/arXiv.1711.05101>; (e) 'madgrad' by Defazio and Jelassi (2021), <doi:10.48550/arXiv.2101.11075>; (f) 'nadam' by Dozat (2019), <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) 'qhadam' by Ma and Yarats(2019), <doi:10.48550/arXiv.1810.06801>; (h) 'radam' by Liu et al. (2019), <doi:10.48550/arXiv.1908.03265>; (i) 'swats' by Shekar and Sochee (2018), <doi:10.48550/arXiv.1712.07628>; (j) 'yogi' by Zaheer et al.(2019), <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.
How to cite:
Gilberto Camara (2022). torchopt: Advanced Optimizers for Torch. R package version 0.1.4, https://cran.r-project.org/web/packages/torchopt
Previous versions and publish date:
0.1.1 (2022-04-25 10:10), 0.1.2 (2022-06-30 15:50), 0.1.3 (2023-03-08 14:20)
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