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Title: Theory of Deep Learning IIb: Optimization Properties of SGD
Publisher: Center for Brains, Minds and Machines (CBMM)
Description: In Theory IIb we characterize with a mix of theory and experiments the optimization of deep convolutional networks by Stochastic Gradient Descent. The main new result in this paper is theoretical and experimental evidence for the following conjecture about SGD: SGD concentrates in probability - like the classical Langevin equation – on large volume, “flat” minima, selecting flat minimizers which are with very high probability also global minimizers.
This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 123 1216.
URI: http://lib.yhn.edu.vn/handle/YHN/715
Other Identifiers: http://hdl.handle.net/1721.1/115407
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