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Stochastic optimization can be done well using only adaptive estimates of the gradient's lower-order moments.

Drawn from what Jimmy Ba and Diederik P. Kingma said

Subjectoptimizers
What this subject means

optimizers The algorithms that actually move a model's weights during training, and the trade-offs between them.

What they actually said

  1. Jimmy Ba Associate professor at the University of Toronto We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. Kingma & Ba, "Adam: A Method for Stochastic Optimization" (arXiv)arxiv.org · 22 Dec 2014All 8 korrents from this piece
    Their wordsWe introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments.

    Where this was said

    ↗Kingma & Ba, "Adam: A Method for Stochastic Optimization" (arXiv)arxiv.org

    22 Dec 2014

    All 8 korrents from this piecethis one is 2nd

  2. Diederik P. Kingma Machine learning researcher We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. Kingma & Ba, "Adam: A Method for Stochastic Optimization" (arXiv)arxiv.org · 22 Dec 2014All 8 korrents from this piece
    Their wordsWe introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments.

    Where this was said

    ↗Kingma & Ba, "Adam: A Method for Stochastic Optimization" (arXiv)arxiv.org

    22 Dec 2014

    All 8 korrents from this piecethis one is 1st

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