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[PRE REVIEW]: Surjectors: surjective normalizing flows for density estimation #5969

Closed editorialbot closed 8 months ago

editorialbot commented 11 months ago

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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Thanks for submitting your paper to JOSS @dirmeier. Currently, there isn't a JOSS editor assigned to your paper.

@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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editorialbot commented 11 months ago

Hello human, I'm @editorialbot, a robot that can help you with some common editorial tasks.

For a list of things I can do to help you, just type:

@editorialbot commands

For example, to regenerate the paper pdf after making changes in the paper's md or bib files, type:

@editorialbot generate pdf
editorialbot commented 11 months ago
Software report:

github.com/AlDanial/cloc v 1.88  T=0.06 s (963.9 files/s, 113279.2 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
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
-------------------------------------------------------------------------------
SUM:                            58            779           2854           3183
-------------------------------------------------------------------------------

gitinspector failed to run statistical information for the repository
editorialbot commented 11 months ago

Wordcount for paper.md is 363

editorialbot commented 11 months ago
Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- None

MISSING DOIs

- None

INVALID DOIs

- None
editorialbot commented 11 months ago

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

editorialbot commented 11 months ago

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arfon commented 11 months ago

@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 commented 11 months ago

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

No conflict of interest exists with any of these.

arfon commented 10 months ago

@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 commented 10 months ago

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 commented 8 months ago

@editorialbot assign me as editor

editorialbot commented 8 months ago

Assigned! @arfon is now the editor

arfon commented 8 months ago

: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

sandeshkatakam commented 8 months ago

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

animikhaich commented 8 months ago

Sure. Would be happy to review it.

arfon commented 8 months ago

@editorialbot assign @sandeshkatakam as reviewer

editorialbot commented 8 months ago

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

@editorialbot commands

arfon commented 8 months ago

@editorialbot add @sandeshkatakam as reviewer

editorialbot commented 8 months ago

@sandeshkatakam added to the reviewers list!

arfon commented 8 months ago

@editorialbot add @animikhaich as reviewer

arfon commented 8 months ago

@editorialbot add @animikhaich as reviewer

editorialbot commented 8 months ago

@animikhaich added to the reviewers list!

editorialbot commented 8 months ago

@animikhaich is already included in the reviewers list

arfon commented 8 months ago

@editorialbot start review

editorialbot commented 8 months ago

OK, I've started the review over in https://github.com/openjournals/joss-reviews/issues/6188.

arfon commented 8 months ago

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