Comment by qsera
1 day ago
>it puts the lie to the argument
But it does not, right? You can either show it something, or modify the parameters in a way that resemble the result of showing it something.
You can claim that the model didn't see the thing, but that would mean nothing, because you are making the same effect with parameter tweaks indirectly.
That's a counterargument to a different thing.
Iteratively measuring loss is a way to reconstruct values. That's trivial to show for a single value If 5 gives you a loss of 2 and 9 gives you a loss of 2 then you know the missing value is 7.
A model with enough parameters can memorise the training set in a similar manner. Technically the model hasn't seen that data by direct input either, but the mechanism provides the means to determine the what the data was. In that respect it is reasonable to say the model has seen the data.
Performing well on examples not in the training set is doing something else.
Any attempt to characterise that as having been seen before negates any distinction between taking in data and reasoning about that data.
Yea, because "seeing" is also tweaking the parameters. Which this example is doing manually.
So I don't understand how any one can make the claim that the model as not seen it. Because the internal transformation is similar.
You are going to have to be more specific, because that reads like nonsense.
By what mechanism do you propose the model observed the test set?
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