[nl]

The idea of AutoML (in this case[1]) is to improve the NN architecture for a given type of problem.

In "normal" machine learning this is basically hyperparmater optimization for a given dataset (eg, the depth of a random forest, XGB parameters, the best random seed/jk )

In this case is tests different combinations of operators on a known dataset to see what performs the best. So it is optimizing the prediction network

(Also this isn't DeepRL, it's a deep neural network. I think that was a typo)

[1] https://research.googleblog.com/2017/05/using-machine-learni...

[michaelgreen]

Jeff Dean talks about AutoML using RL and in the paper " Neural Architecture Search with Reinforcement Learning" it also talks about this.

Also it seems different from more traditional hyperparameter optimization because it makes novel cells. So the structure of the network isn't limited to our existing library of layers/cells.

https://arxiv.org/abs/1611.01578 https://youtu.be/HcStlHGpjN8?t=2073

[nl]

"Novel Cells" are combinations of existing operators.

It's entirely true that these are combinations that humans haven't (and probably wouldn't) come up with.

I don't want to underplay this. "It's similar to hyperparameter search" makes it sound like it isn't interesting or novel, which is untrue. I completely believe it is a revolutionary way to build software (so much so that I quit my job, raised funding and are working on a similar space of problems).

But it isn't doing something like inventing a new math operations similar to the other operators which humans put together to form cells/layers. It is rearranging and choosing those operators in new ways.

[michaelgreen]

Okay I see what you're saying and I completely agree.

[nl]

You maybe interested in their most recent paper and blog post from today: https://research.googleblog.com/2018/03/using-evolutionary-a...

[michaelgreen]

Wow thanks, reading it now (: