Translations:Adam A Method for Stochastic Optimization/23/en: Difference between revisions

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    * '''Logistic regression''' on MNIST: Adam converged faster than SGD with momentum, AdaGrad, and RMSProp.
    * '''{{Term|logistic regression}}''' on MNIST: Adam converged faster than {{Term|stochastic gradient descent|SGD}} with {{Term|momentum}}, {{Term|adagrad}}, and RMSProp.
    * '''Multi-layer neural networks''' on MNIST: Adam achieved the lowest training cost, with convergence speed comparable to or better than competing methods.
    * '''Multi-layer neural networks''' on MNIST: Adam achieved the lowest training cost, with {{Term|convergence}} speed comparable to or better than competing methods.
    * '''Convolutional neural networks''' on CIFAR-10: Adam performed comparably to SGD with carefully tuned momentum and learning rate schedules.
    * '''Convolutional neural networks''' on CIFAR-10: Adam performed comparably to {{Term|stochastic gradient descent|SGD}} with carefully tuned {{Term|momentum}} and {{Term|learning rate}} schedules.
    * '''Variational autoencoders''' (VAEs): Adam was used successfully to optimize the variational lower bound, demonstrating its applicability to generative models.
    * '''Variational autoencoders''' (VAEs): Adam was used successfully to optimize the variational lower bound, demonstrating its applicability to generative models.

    Latest revision as of 21:37, 27 April 2026

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    Message definition (Adam A Method for Stochastic Optimization)
    * '''{{Term|logistic regression}}''' on MNIST: Adam converged faster than {{Term|stochastic gradient descent|SGD}} with {{Term|momentum}}, {{Term|adagrad}}, and RMSProp.
    * '''Multi-layer neural networks''' on MNIST: Adam achieved the lowest training cost, with {{Term|convergence}} speed comparable to or better than competing methods.
    * '''Convolutional neural networks''' on CIFAR-10: Adam performed comparably to {{Term|stochastic gradient descent|SGD}} with carefully tuned {{Term|momentum}} and {{Term|learning rate}} schedules.
    * '''Variational autoencoders''' (VAEs): Adam was used successfully to optimize the variational lower bound, demonstrating its applicability to generative models.
    • logistic regression on MNIST: Adam converged faster than SGD with momentum, adagrad, and RMSProp.
    • Multi-layer neural networks on MNIST: Adam achieved the lowest training cost, with convergence speed comparable to or better than competing methods.
    • Convolutional neural networks on CIFAR-10: Adam performed comparably to SGD with carefully tuned momentum and learning rate schedules.
    • Variational autoencoders (VAEs): Adam was used successfully to optimize the variational lower bound, demonstrating its applicability to generative models.
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