Jimmy Ba
Person on 2 sites
Associate professor at the University of Toronto, and co-author of the Adam optimizer.
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1 channel on ppll (opens ppll.app)
Where public figures publish, and what they are on record saying.
Updated 2023-04-12 -
4 beliefs on korrents (opens korrents.com)
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Stochastic optimization can be done well using only adaptive estimates of the gradient's lower-order moments. (opens korrents.com)
“We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments.”
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An optimizer that is invariant to rescaling the gradients and cheap in memory is what makes very large models practical to train. (opens korrents.com)
“The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of…”
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The same optimizer should handle objectives that move under it and gradients that are noisy or sparse. (opens korrents.com)
“The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients.”
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Stochastic optimization can be done well using only adaptive estimates of the gradient's lower-order moments. (opens korrents.com)