
Neural Architecture Search with Reinforcement Learning - baq
https://openreview.net/forum?id=r1Ue8Hcxg&noteId=r1Ue8Hcxg
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jacquesm
Ask and ye shall receive... just a couple of hours ago I asked on twitter if
such a thing has been done and I've been bombarded with prior attempts.

Interesting how many different ways people have come to this idea. It's a
powerful one: if architectures are where we can make a difference and
architectures is a trial-and-error kind of thing at the moment that makes it a
perfect candidate for automation.

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adw
Hyperparameter optimization has been a thing forever.

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alexbeloi
I dig hyper-parameter optimization and architecture discovery but I'm always
reminded of [this
slide]([https://image.slidesharecdn.com/k2jeffdean-160609173832/95/l...](https://image.slidesharecdn.com/k2jeffdean-160609173832/95/large-
scale-deep-learning-with-tensorflow-46-638.jpg?cb=1465493958)) and how these
hp-tuning and architecture discovery tasks will always fall one tier above
whatever your base optimization task is. The base tasks have to get faster for
it to be practical for anyone without a server farm.

I definitely think this is the future of ANNs, nets optimizing nets optimizing
nets. Turtles all the way down.

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dankai
Wow, this is amazing! I've been looking for work like this.

At the same conference also this was published: HYPERNETWORKS [David Ha, et
al]
[https://openreview.net/pdf?id=rkpACe1lx](https://openreview.net/pdf?id=rkpACe1lx)

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deepnotderp
This has already been posted before, but the fact that DRL can do pretty good
autoML is pretty useful!

