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The ADAM optimizer is exceedingly popular in the deep learning community.Often it works very well, sometimes it doesn't. Why? We interpret ADAM as acombination of two aspects: for each weight, the update direction is determinedby the sign of stochastic gradients, whereas the update magnitude is determinedby an estimate of their relative variance. We disentangle these two aspects andanalyze them in isolation, gaining insight into the mechanisms underlying ADAM.This analysis also extends recent results on adverse effects of ADAM ongeneralization, isolating the sign aspect as the problematic one. Transferringthe variance adaptation to SGD gives rise to a novel method, completing thepractitioner's toolbox for problems where ADAM fails.

Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients