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The same optimizer should handle objectives that move under it and gradients that are noisy or sparse.

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 The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. arxiv.org

    Associate professor at the University of Toronto

    Their wordsThe method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients.

    Where this was said

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

    All 8 korrents from this piecethis one is 6th

  2. Diederik P. Kingma The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. arxiv.org

    Machine learning researcher

    Their wordsThe method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients.

    Where this was said

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

    All 8 korrents from this piecethis one is 5th

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

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