[REVIEW]: ISL: A Julia package for training Implicit Generative Models via an Invariant Statistical Loss

[editorialbot]

Submitting author: !--author-handle-->@https://github.com/josemanuel22<!--end-author-handle-- (José Manuel de Frutos) Repository: https://github.com/josemanuel22/ISL Branch with paper.md (empty if default branch): Version: v0.1.0 Editor: !--editor-->@bmcfee<!--end-editor-- Reviewers: @ziyiyin97, @odunbar Archive: Pending

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Reviewers and authors:

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Reviewer instructions & questions

@ziyiyin97 & @odunbar, your review will be checklist based. Each of you will have a separate checklist that you should update when carrying out your review. First of all you need to run this command in a separate comment to create the checklist:

@editorialbot generate my checklist

The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html. Any questions/concerns please let @bmcfee know.

✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨

Checklists

📝 Checklist for @ziyiyin97

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[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.90  T=0.08 s (492.9 files/s, 314372.0 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
CSV                              1              0              0          17421
Julia                           18            752           1095           3027
Markdown                         6            172              0            479
TeX                              1             53              0            387
YAML                             6              8              9            123
TOML                             4              4              0             47
SVG                              1              0              1             20
-------------------------------------------------------------------------------
SUM:                            37            989           1105          21504
-------------------------------------------------------------------------------

Commit count by author:

   332  josemanuel22
    33  José Manuel de Frutos
    21  José Manuel Frutos

[editorialbot]

Paper file info:

📄 Wordcount for paper.md is 1031

✅ The paper includes a Statement of need section

[editorialbot]

License info:

✅ License found: MIT License (Valid open source OSI approved license)

[editorialbot]

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

✅ OK DOIs

- 10.1080/00031305.2017.1380080 is OK

🟡 SKIP DOIs

- No DOI given, and none found for title: On the performance of particle filters with adapti...
- No DOI given, and none found for title: Enhancing the Locality and Breaking the Memory Bot...
- No DOI given, and none found for title: GAN connoisseur: Can GANs learn simple 1D parametr...
- No DOI given, and none found for title: Generative adversarial nets
- No DOI given, and none found for title: Generalization and equilibrium in generative adver...
- No DOI given, and none found for title: Wasserstein generative adversarial networks
- No DOI given, and none found for title: MMD GAN: Towards deeper understanding of moment ma...
- No DOI given, and none found for title: Do GANs actually learn the distribution? an empiri...
- No DOI given, and none found for title: Probability and Measure
- No DOI given, and none found for title: f-GAN: Training generative neural samplers using v...
- No DOI given, and none found for title: Adversarial variational bayes: Unifying variationa...
- No DOI given, and none found for title: Generative moment matching networks
- No DOI given, and none found for title: A kernel two-sample test
- No DOI given, and none found for title: Function-space inference with sparse implicit proc...
- No DOI given, and none found for title: Probabilistic time series forecasting with implici...
- No DOI given, and none found for title: Multivariate Probabilistic Time Series Forecasting...
- No DOI given, and none found for title: Deep State Space Models for Time Series Forecastin...
- No DOI given, and none found for title: Temporal Regularized Matrix Factorization for High...
- No DOI given, and none found for title: Time Series Analysis: Forecasting and Control, 5th...
- No DOI given, and none found for title: Autoregressive Denoising Diffusion Models for Mult...
- No DOI given, and none found for title: Do GANs learn the distribution? some theory and em...
- No DOI given, and none found for title: Deep autoregressive models with spectral attention
- No DOI given, and none found for title: Training generative neural networks via maximum me...
- No DOI given, and none found for title: Nonparametric density estimation & convergence rat...
- No DOI given, and none found for title: ElectricityLoadDiagrams20112014
- No DOI given, and none found for title: Enhancing the locality and breaking the memory bot...
- No DOI given, and none found for title: Independent random sampling methods
- No DOI given, and none found for title: Adam: A method for stochastic optimization
- No DOI given, and none found for title: Improved techniques for training GANs
- No DOI given, and none found for title: Spectral normalization for generative adversarial ...
- No DOI given, and none found for title: Learning in implicit generative models
- No DOI given, and none found for title: 30 Years of European Wind Generation
- No DOI given, and none found for title: Gluonts: Probabilistic and neural time series mode...
- No DOI given, and none found for title: Autoformer: Decomposition transformers with auto-c...
- No DOI given, and none found for title: On the measure of Voronoi cells
- No DOI given, and none found for title: Understanding and contextualising diffusion models
- No DOI given, and none found for title: Training Implicit Generative Models via an Invaria...

❌ MISSING DOIs

- 10.1109/tsp.2016.2637324 may be a valid DOI for title: Adapting the number of particles in sequential Mon...
- 10.1109/icassp.2016.7472504 may be a valid DOI for title: Online adaptation of the number of particles of SM...
- 10.1109/iccv.2019.00456 may be a valid DOI for title: Learning implicit generative models by matching pe...
- 10.1214/aoms/1177729394 may be a valid DOI for title: Remarks on a multivariate transformation
- 10.1016/j.ijforecast.2019.07.001 may be a valid DOI for title: DeepAR: Probabilistic forecasting with autoregress...
- 10.1109/tsp.2010.2053707 may be a valid DOI for title: Assessment of nonlinear dynamic models by Kolmogor...
- 10.1609/aaai.v35i12.17325 may be a valid DOI for title: Informer: Beyond efficient transformer for long se...
- 10.1609/aaai.v37i9.26317 may be a valid DOI for title: Are transformers effective for time series forecas...
- 10.1109/cvpr.2018.00367 may be a valid DOI for title: Generative modeling using the sliced wasserstein d...

❌ INVALID DOIs

- None

[editorialbot]

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

[ziyiyin97]

Review checklist for @ziyiyin97

Conflict of interest

• [ ] I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• [ ] I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• [ ] Repository: Is the source code for this software available at the https://github.com/josemanuel22/ISL?
• [ ] License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
• [ ] Contribution and authorship: Has the submitting author (@https://github.com/josemanuel22) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• [ ] Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
• [ ] Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
• [ ] Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
• [ ] Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

• [ ] Installation: Does installation proceed as outlined in the documentation?
• [ ] Functionality: Have the functional claims of the software been confirmed?
• [ ] Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• [ ] A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• [ ] Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• [ ] Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• [ ] Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• [ ] Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• [ ] Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• [ ] Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• [ ] A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
• [ ] State of the field: Do the authors describe how this software compares to other commonly-used packages?
• [ ] Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• [ ] References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?