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Publikováno v:
Entropy, Vol 22, Iss 544, p 544 (2020)
Entropy
Volume 22
Issue 5
Entropy
Volume 22
Issue 5
When gradient descent (GD) is scaled to many parallel workers for large scale machine learning problems, its per-iteration computation time is limited by the straggling workers. Straggling workers can be tolerated by assigning redundant computations
Autor:
Sánchez-Gutiérrez, Máximo Eduardo1 (AUTHOR) maximo.sanchez@uacm.edu.mx, González-Pérez, Pedro Pablo2 (AUTHOR) pgonzalez@cua.uam.mx
Publikováno v:
Entropy. Feb2022, Vol. 24 Issue 2, p196. 1p.
Autor:
Hernández-Sanjaime, Rocío1 (AUTHOR) martin.gonzaleze@umh.es, González, Martín1 (AUTHOR), Peñalver, Antonio1 (AUTHOR), López-Espín, Jose J.1 (AUTHOR)
Publikováno v:
Entropy. Apr2021, Vol. 23 Issue 4, p384. 1p.
Publikováno v:
Entropy, Vol 22, Iss 5, p 544 (2020)
When gradient descent (GD) is scaled to many parallel workers for large-scale machine learning applications, its per-iteration computation time is limited by straggling workers. Straggling workers can be tolerated by assigning redundant computations
Externí odkaz:
https://doaj.org/article/2a1d725a763b43c58e930f43ec357a97
Autor:
Chen, Chi-Wei1,2 d103056006@mail.nchu.edu.tw, Chang, Kai-Po3,4 d17179@mail.cmuh.org.tw, Ho, Cheng-Wei2, Chang, Hsung-Pin1 hpchang@cs.nchu.edu.tw, Chu, Yen-Wei2,3,5 ywchu@nchu.edu.tw
Publikováno v:
Entropy. Dec2018, Vol. 20 Issue 12, p988. 1p.