Liu Et Al. (2019).pdf

1908.03265.pdf
Preview of Liu et al. (2019)
🔗 Source: arxiv.org
📊 Size: 2.74 MB
📄 Pages: 14 pages
⬇️ Downloads: 29

Summary

Researchers identify the variance issue of the adaptive learning rate in stochastic optimization algorithms like Adam and RMSprop, which can lead to convergence to bad local optima. They propose Rectified Adam (RAdam), a novel variant of Adam that rectifies the variance of the adaptive learning rate, and demonstrate its efficacy and robustness in image classification, language modeling, and neural machine translation tasks. The warmup heuristic is shown to be a variance reduction technique, and RAdam provides a theoretical justification for it.

Description

Researchers identify the variance issue of the adaptive learning rate in stochastic optimization algorithms like Adam and RMSprop, which can lead to...

Technical Information

  • File Format: PDF
  • File Size: 2.74 MB
  • Pages: 14
  • Language: EN
  • Total Downloads: 29
  • Last Updated: 7 days ago

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