Deep networks probably mostly just memorize and regurgitate their training data. This seems likely all that AlphaGo Zero does, for instance.
Relevant post from @nostalgebraist
@xuenay
http://ift.tt/2t36Ix2 pic.twitter.com/UCaeQaoT3S
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Deep networks probably mostly just memorize and regurgitate their training data. This seems likely all that AlphaGo Zero does, for instance.
Relevant post from @nostalgebraist
@xuenay
http://ift.tt/2t36Ix2 pic.twitter.com/UCaeQaoT3S
In the AlphaGo Zero case, its training data is self-generated though – it seems weird to call this memorization.
Yes… but it’s not the dl net which is doing that, it’s the rl architecture.
I probably should have said “all that AG0’s net does”! Aiming for twittery concision does result in inaccuracy
I’d really like to run this control experiment: replace the AG0 learner with something much simpler. The MENACE algorithm might even do the trick, if it tracked results for all nxm subsets of the board, for bounded m,n.
Whoa! I just now got curious and checked how many game AG0 played. Only 4.9 million! Without the vast expense of backprop, it seems feasible to test a dumb algorithm with realistic hardware. Restraining myself from starting to code this!
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