My point is that they don't have a mathematical meaning. They have a training-time impact, but that's more relevant when creating than using. You'd need to kno

My point is that they don't have a mathematical meaning. They have a training-time impact, but that's more relevant when creating than using. You'd need to know how many parameters were independently covarying for any given class of predictions. It certainly isnt all of them. You could cite the "average dropout percent to random-level accuracy on a given class of problems" (my guess is that this would show 5-20% of parameters could be dropped). My point, I suppose, is that users of NNs arent interested in the architecture characteristics which affect training -- they're interested in how capable any given model will be. For this we really want to know how large the training data was, and how compressed it has been. If it's 1PB -> 1MB then we can easily say that's much less useful (but much faster to use) than 1PB -> 0.5TB. Likewise we can say, if it's a video generator, that 1PB is far too small to be generally useful -- so at best it'll be domain-speicifc.