Open distillpub-reviewers opened 5 years ago
RKehlbeck
commented
5 years ago Thank you very much for taking the time to review our article! We really appreciate the detailed comments and feedback!
We are just wrapping up all the changes. I will reference the corresponding PRs and commits in this issue.
RKehlbeck
commented
5 years ago PR #15 adresses
Labeling figures would have been helpful. Axis labels (even dummy values) on some plots would have been helpful because it's not clear what they refer to on the first pass, such as the first figures in the "Gaussian Process" and "Kernel" sections. It's also not intuitive how to play with the figures.
The definition for regression is not precise (what does "as close as possible" mean?). On the definition of Gaussian Processes, I would say that GPs give a confidence for the predicted value for a given input. GPs can be viewed as a distribution over functions, so it’s not accurate to use the term “predicted function” in the first definition.
PR #20 adresses
For diagrams with a shaded portion (first and last figures in article), it's not clear how much "confidence" (standard deviations) they show. Are they showing the mean plus/minus one standard deviation? Two standard deviations?
For the definition of GPs, I would avoid the use of the term “kernel” and prefer the term “covariance function”. Or include a caveat to avoid confusion between GP kernels and kernel methods (e.g. SVMs).
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Labeling figures would have been helpful. Axis labels (even dummy values) on some plots would have been helpful because it's not clear what they refer to on the first pass, such as the first figures in the "Gaussian Process" and "Kernel" sections. It's also not intuitive how to play with the figures.
For diagrams with a shaded portion (first and last figures in article), it's not clear how much "confidence" (standard deviations) they show. Are they showing the mean plus/minus one standard deviation? Two standard deviations?
The definition for regression is not precise (what does "as close as possible" mean?). On the definition of Gaussian Processes, I would say that GPs give a confidence for the predicted value for a given input. GPs can be viewed as a distribution over functions, so it’s not accurate to use the term “predicted function” in the first definition.
For the definition of GPs, I would avoid the use of the term “kernel” and prefer the term “covariance function”. Or include a caveat to avoid confusion between GP kernels and kernel methods (e.g. SVMs).
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Any concerns or conflicts of interest that you are aware of?: No known conflicts of interest What type of contributions does this article make?: Explanation of existing results