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You regularly utilize the term dense, and sometimes I do not comprehend for those who mean a totally linked layer or an unpruned (completely linked or convolutional) layer. Is it possible to explain it for all occurrences in which it might be unclear?
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Will be the lottery tickets the cross solution of subnetworks and points within the parameter tangent space? The subnetworks by itself? When the latter then how does this vary from the first lottery ticket hypothesis?
Initialization could possibly perfectly be significant, but the primary place of initialization is to begin in a few not-entirely-insane A part of the decline landscape, and then the optimization algorithm does the actual function.
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Evan Hubinger speculates about whether the lottery ticket hypothesis might make clear the deep double descent phenomenon:
“In certain scenarios we present that neural networks understand via a technique of “grokking” a pattern in the information, strengthening generalization general performance from random possibility stage to best generalization, and that this enhancement in generalization can occur nicely past the point of overfitting.”
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My guess regarding how double descent operates When the Lottery Tickets Hypothesis is true is usually that from the interpolation regime SGD gets to simply give attention to the profitable tickets and disregard the others—as it doesn't have to use the complete model potential—While on the interpolation threshold SGD is compelled to take advantage of the complete network (to have the total product capability), not merely the successful tickets, which hurts generalization. [...] Which is just speculation on my element, however
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