Equilibrated adaptive learning rates for non-convex optimization

Train deep learning models faster with a simple tweak: ESGD

Struggling to make deep learning train faster? Many networks get stuck on flat stretches or weird saddle shapes that slow learning down, and plain step sizes can't fix that.
A smarter idea is to use adaptive learning rates that change for each part of the model, so slow parts speed up and fast parts calm down.
Old tricks sometimes react badly when the problem has both up and down directions, so they can even slow you, not help.
Looking at how the surface bends gives a better clue, and an approach called equilibration balances those bends across the whole model.
From that comes a new method named ESGD, which adapts steps in a steadier way.
In practice ESGD learns as fast or faster than popular tools like RMSProp, and nearly always beats plain stochastic gradient descent.
If you want models that converge quicker, with less fiddling over step sizes, this is a simple change to try — many saw faster, more stable training after switching, and you might too.

Read article comprehensive review in Paperium.net:
Equilibrated adaptive learning rates for non-convex optimization

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