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unlockNN module for uncertainty quantification of MEGNet #338
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@sgbaird https://github.com/openjournals/joss-papers/blob/joss.03700/joss.03700/10.21105.joss.03700.pdf I think the JOSS paper is here, not sure if they have another more detailed paper planned (really looking forward to it!). I think the idea is fascinating and I am sure @a-ws-m has done great work on unlockNN. Regarding incorporating it into megnet, I have no issue about it. It just makes me think why not we just use unlockNN directly? |
@chc273 thanks for the quick response! The main thought behind the suggestion was for the uncertainty quantification to be housed directly within the MEGNet API for both visibility and ease of use. Maybe it would be easier to do this directly with |
Hi both, thanks for the interest 😄. the main blocker for |
@sgbaird Thanks! My thoughts are that the @a-ws-m thanks for the example! Do you have a more detailed paper explaining the mechanisms behind the code and method? (not relevant to this Issue, but a personal request :) ) |
Sorry for the slow reply @chc273 -- it's my intention to write a couple of papers related to it: the JOSS paper, which is nearly finished, and a more detailed one a bit further down the line. There are two papers/works in particular that this work builds upon, which have more detail than the JOSS paper at the moment: Variational Gaussian Processes (explanation in TensorFlow docs here and paper here) and Graph-fed Gaussian Processes (see here). |
Did a quick search, and it seems like some additional dropout functionality related to quantifying model uncertainty was added, but I don't think that was ever fleshed out into top-level UQ functionality with MEGNet (please correct me if I'm wrong). I came across unlockNN (unaffiliated) and thought it would be worth bringing attention to in the repo. Has some specific functionality compatible with MEGNet. Maybe worth incorporating the functionality into MEGNet if you read the paper and like the results (I haven't read it yet).
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