Translations:Adam A Method for Stochastic Optimization/27/en: Difference between revisions

    From Marovi AI
    (Importing a new version from external source)
     
    (Importing a new version from external source)
     
    Line 1: Line 1:
    Subsequent work identified limitations, including convergence issues in certain settings (addressed by AMSGrad), potential generalization gaps compared to well-tuned SGD (particularly for image classification), and sensitivity to the choice of <math>\epsilon</math>. Variants such as AdamW (which decouples weight decay from the adaptive learning rate) became preferred for training large Transformer models. Despite these refinements, Adam and its variants remain the backbone of modern neural network optimization.
    Subsequent work identified limitations, including {{Term|convergence}} issues in certain settings (addressed by AMSGrad), potential generalization gaps compared to well-tuned {{Term|stochastic gradient descent|SGD}} (particularly for image classification), and sensitivity to the choice of <math>\epsilon</math>. Variants such as AdamW (which decouples {{Term|weight decay}} from the adaptive {{Term|learning rate}}) became preferred for training large {{Term|transformer}} models. Despite these refinements, Adam and its variants remain the backbone of modern neural network optimization.

    Latest revision as of 21:37, 27 April 2026

    Information about message (contribute)
    This message has no documentation. If you know where or how this message is used, you can help other translators by adding documentation to this message.
    Message definition (Adam A Method for Stochastic Optimization)
    Subsequent work identified limitations, including {{Term|convergence}} issues in certain settings (addressed by AMSGrad), potential generalization gaps compared to well-tuned {{Term|stochastic gradient descent|SGD}} (particularly for image classification), and sensitivity to the choice of <math>\epsilon</math>. Variants such as AdamW (which decouples {{Term|weight decay}} from the adaptive {{Term|learning rate}}) became preferred for training large {{Term|transformer}} models. Despite these refinements, Adam and its variants remain the backbone of modern neural network optimization.

    Subsequent work identified limitations, including convergence issues in certain settings (addressed by AMSGrad), potential generalization gaps compared to well-tuned SGD (particularly for image classification), and sensitivity to the choice of $ \epsilon $. Variants such as AdamW (which decouples weight decay from the adaptive learning rate) became preferred for training large transformer models. Despite these refinements, Adam and its variants remain the backbone of modern neural network optimization.

    Retrieved from "https://marovi.ai/index.php?title=Translations:Adam_A_Method_for_Stochastic_Optimization/27/en&oldid=13258"