Lecture 8: Subgradient method (continued); Proximal gradient descent and acceleration

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  1. Ryan T
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Комментарии
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@37:00 It's interesting that in Deep Learning we usually use (minibatch) /randomized/ SGD, but without replacement to make sure we don't skip any examples. This looks like a cyclic randomized rule!

kiuhnmmnhuik
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46:52 proximal gradient method

thanks

choungyoungjae
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@20:00 I don't understand the doubts here. If we use Polyak's step sizes then the subgradient method *must* converge. Nobody said that the subgradients must also converge.

kiuhnmmnhuik
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Hey Ryan, Thank you very much for uploading the lecture it is really comprehensive. Is there a way i can get the slides? I'm currently writing a bachelor thesis on dictionary learning.

Cheers

CoinedBeatz
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The last part was quite painful to watch because of the blur.

kiuhnmmnhuik