[PRE REVIEW]: Surjectors: surjective normalizing flows for density estimation

[editorialbot]

Submitting author: @dirmeier (Simon Dirmeier)
Repository: https://github.com/dirmeier/surjectors
Branch with paper.md (empty if default branch): joss
Version: v0.0.3
Editor: @arfon
Reviewers: @sandeshkatakam, @animikhaich
Managing EiC: Arfon Smith

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@dirmeier if you have any suggestions for potential reviewers then please mention them here in this thread (without tagging them with an @). You can search the list of people that have already agreed to review and may be suitable for this submission.

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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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gitinspector failed to run statistical information for the repository

[editorialbot]

Wordcount for paper.md is 363

[editorialbot]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- None

MISSING DOIs

- None

INVALID DOIs

- None

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[editorialbot]

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[arfon]

@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!

[dirmeier]

Hey, thanks for the info. There are several potential reviewers:

• VincentStimper,
• thomaspinder,
• animikhaich,
• sandeshkatakam,
• Uddiptaatwork

No conflict of interest exists with any of these.

[arfon]

@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.

[dirmeier]

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), ...

[arfon]

@editorialbot assign me as editor

[editorialbot]

Assigned! @arfon is now the editor

[arfon]

👋 @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

[sandeshkatakam]

Sure. I can review this submission. Let me know any further details

[animikhaich]

Sure. Would be happy to review it.

[arfon]

@editorialbot assign @sandeshkatakam as reviewer

[editorialbot]

I'm sorry human, I don't understand that. You can see what commands I support by typing:

@editorialbot commands

[arfon]

@editorialbot add @sandeshkatakam as reviewer

[editorialbot]

@sandeshkatakam added to the reviewers list!

[arfon]

@editorialbot add @animikhaich as reviewer

[arfon]

@editorialbot add @animikhaich as reviewer

[editorialbot]

@animikhaich added to the reviewers list!

[editorialbot]

@animikhaich is already included in the reviewers list

[arfon]

@editorialbot start review

[editorialbot]

OK, I've started the review over in #6188.

[arfon]

@sandeshkatakam, @animikhaich, @dirmeier – see you all over in #6188 where the actual review will take place.