Closed editorialbot closed 8 months ago
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Software report:
github.com/AlDanial/cloc v 1.88 T=0.06 s (963.9 files/s, 113279.2 lines/s)
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Language files blank comment code
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Python 37 587 776 1875
Jupyter Notebook 3 0 1969 651
YAML 5 20 4 206
TeX 1 23 0 120
reStructuredText 6 92 94 113
Markdown 2 34 0 95
TOML 1 14 0 88
CSS 1 4 4 20
make 2 5 7 15
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SUM: 58 779 2854 3183
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gitinspector failed to run statistical information for the repository
Wordcount for paper.md
is 363
Reference check summary (note 'MISSING' DOIs are suggestions that need verification):
OK DOIs
- None
MISSING DOIs
- None
INVALID DOIs
- None
:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:
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⚠️ Note to editors: If these papers look like they might be a good match, click through to the review issue for that paper and invite one or more of the authors before before considering asking the reviewers of these papers to review again for JOSS.
@dirmeier – thanks for your submission to JOSS. We're currently managing a large backlog of submissions and the editor most appropriate for your area is already rather busy.
For now, we will need to waitlist this paper and process it as the queue reduces. Thanks for your patience!
Hey, thanks for the info. There are several potential reviewers:
No conflict of interest exists with any of these.
@dirmeier – in order to help me find an editor for this submission, could you help me understand what sorts of academic fields these methods are typically applied in? Skimming your paper, it's not obvious.
Hello @arfon , I think either generative modelling or neural density estimation which I would subsume under probabilistic deep learning or more generally machine learning.
Normalizing flows are ubiquitous in ML, for instance, for Bayesian inference (i.e., variational inference), for generative modelling (e.g., for images or audio), for density estimation (and outlier detection), ...
@editorialbot assign me as editor
Assigned! @arfon is now the editor
:wave: @VincentStimper @thomaspinder @animikhaich @sandeshkatakam @Uddiptaatwork – would any of you be willing to review this submission for JOSS? The submission under consideration is Surjectors: surjective normalizing flows for density estimation
The review process at JOSS is unique: it takes place in a GitHub issue, is open, and author-reviewer-editor conversations are encouraged. You can learn more about the process in these guidelines: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html
Based on your experience, we think you might be able to provide a great review of this submission. Please let me know if you think you can help us out!
Many thanks Arfon
Sure. I can review this submission. Let me know any further details
Sure. Would be happy to review it.
@editorialbot assign @sandeshkatakam as reviewer
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@editorialbot add @sandeshkatakam as reviewer
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@editorialbot add @animikhaich as reviewer
@editorialbot add @animikhaich as reviewer
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@animikhaich is already included in the reviewers list
@editorialbot start review
OK, I've started the review over in https://github.com/openjournals/joss-reviews/issues/6188.
@sandeshkatakam, @animikhaich, @dirmeier – see you all over in #6188 where the actual review will take place.
Submitting author: !--author-handle-->@dirmeier<!--end-author-handle-- (Simon Dirmeier) Repository: https://github.com/dirmeier/surjectors Branch with paper.md (empty if default branch): joss Version: v0.0.3 Editor: !--editor-->@arfon<!--end-editor-- Reviewers: @sandeshkatakam, @animikhaich Managing EiC: Arfon Smith
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