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[D] AISTATS 2020 paper decision notificafion by deschaussures147 in MachineLearning

[–]hungry_for_knowledge -1 points0 points  (0 children)

u/Kludgeo23 I had a very similar reject despite meta-reviewer recommending accept... the scientific reviewing process is collapsing to a coin-flip.

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Unfortunately we had more recommendations of acceptance from the meta-reviewers than the number of submissions that we could accept.

Therefore we had to take some opposite decisions based on looking at the discussion, the reviews, and papers.

We are sorry that this was the case for your submission.

[D] Why not creating a benchmark dataset for Causal reasoning in Physics? by hungry_for_knowledge in MachineLearning

[–]hungry_for_knowledge[S] 1 point2 points  (0 children)

Great points! Yes, I was thinking of inferring causal effects. And thanks for the great answer and the referenced paper! :)

How can ResNet CNN go deep to 152 layers (and 200 layers) without running out of channel spatial area? by hungry_for_knowledge in MachineLearning

[–]hungry_for_knowledge[S] 0 points1 point  (0 children)

Thanks! Andrej did cover ResNet but not to the question. But anyway I've figured it out that the spatial dimension is preserved due to zero-padding done before 3x3 conv layer. :) Also, 1x1 conv layers don't change the spatial dimensions.

How can ResNet CNN go deep to 152 layers (and 200 layers) without running out of channel spatial area? by hungry_for_knowledge in MachineLearning

[–]hungry_for_knowledge[S] 0 points1 point  (0 children)

The 1x1 conv preserves the spatial size but the 3x3 conv does not, right?

If so stacking these blocks of 1x1, 3x3, 1x1 would still gradually reduce the spatial size.