Title: Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients,
Abstract: 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 a
combination of two aspects: for each weight, the update direction is determined
by the sign of stochastic gradients, whereas the update magnitude is determined
by an estimate of their relative variance. We disentangle these two aspects and
analyze them in isolation, gaining insight into the mechanisms underlying ADAM.
This analysis also extends recent results on adverse effects of ADAM on
generalization, isolating the sign aspect as the problematic one. Transferring
the variance adaptation to SGD gives rise to a novel method, completing the
practitioner's toolbox for problems where ADAM fails.