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The only setup where a global minimum has any chance to generalize is for underparameterized models, such as logistic regression, or a 1-layer network with few units. Otherwise, the points in parameter space that you are looking for during training are pretty far from the minima
An important research direction would be to use the information bottleneck principle to come up with models that have exactly the right amount of memorization capacity for a given task, as well as optimization methods to get to the global optimum
choosing the right “coordinates” to use is a non-trivial question, even before you run any optimisation algorithms
Memorization means the net can reach the global minimum for a particular instance of a problem but “track-back” to a point of reference (in order to still generalize)?
Maybe the memorization in this case is referring to the “end games” of how to get from the general regions to the finer optimizations?
You might find the discussion here interesting if you haven't already seen it. https://openreview.net/forum?id=ry_WPG-A- …
For a while I assumed that quantum computers were a clear path to global optimization, but even quantum annealers (or the quantum / classical hybrid approximate optimization algorithm) give results within a probability distribution.
PSO , instead of G. Descent
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