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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.

Interesting
A private bookmark. Not a position, and never counted.

What they actually said

Word for word, with the source under each one. They did not write this page.

  1. Jimmy Ba We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. arxiv.org

    Associate professor at the University of Toronto

    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

    All 8 korrents from this piecethis one is 2nd

  2. Diederik P. Kingma We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. arxiv.org

    Machine learning researcher

    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

    All 8 korrents from this piecethis one is 1st

Added to korrents 22 Dec 2014 · How quotes work · Something wrong? Tell us

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