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tripple13 t1_jb0ksx6 wrote

I find it quite ridiculous to discount RL. Optimal control problems have existed since the beginning of time, and for the situations in which you cannot formulate a set of differential equations, optimizing obtuse functions with value or policy optimization could be a way forward.

It reminds me of the people who discount GANs due to their lack of a likelihood. Sure, but can it be useful regardless? Yes, actually, it can.

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tonicinhibition t1_jb1fgpe wrote

> people who discount GANs due to their lack of a likelihood

I was going to ask you to expand on this a little, but instead found a post that describes it pretty well for anyone else who is curious:

Do GANS really model the true data distribution...

For further nuance on this topic, Machine Learning Street Talk discussed interpolation vs extrapolation with Yann LeCun regarding interpolation vs extrapolation, which Letitia Parcalabescu summarizes here.

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currentscurrents t1_jb1j20n wrote

>Do GANS really model the true data distribution...

I find their argument to be pretty weak. Of course these images look semantically similar; they ran a semantic similarity search to find them.

They are clearly not memorized training examples. The pose, framing, and facial expressions are very different.

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tonicinhibition t1_jb1ntqz wrote

I don't think the author of the post took a position on the original argument; rather they just presented ways to explore the latent space and make comparisons that are reasonable so that we might derive better distance metrics.

I see it as a potential way to probe for evidence of mode collapse.

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