Skip to content

Commit

Permalink
Merge pull request #5710 from openjournals/joss.06794
Browse files Browse the repository at this point in the history
Merging automatically
  • Loading branch information
editorialbot authored Jul 30, 2024
2 parents 7f33262 + a0d10af commit e2d2a07
Show file tree
Hide file tree
Showing 3 changed files with 817 additions and 0 deletions.
248 changes: 248 additions & 0 deletions joss.06794/10.21105.joss.06794.crossref.xml
Original file line number Diff line number Diff line change
@@ -0,0 +1,248 @@
<?xml version="1.0" encoding="UTF-8"?>
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1"
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd"
xmlns:rel="http://www.crossref.org/relations.xsd"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
version="5.3.1"
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd">
<head>
<doi_batch_id>20240730210706-6f20f03410e17cc3fb35e104b63e493419ea235c</doi_batch_id>
<timestamp>20240730210706</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>admin@theoj.org</email_address>
</depositor>
<registrant>The Open Journal</registrant>
</head>
<body>
<journal>
<journal_metadata>
<full_title>Journal of Open Source Software</full_title>
<abbrev_title>JOSS</abbrev_title>
<issn media_type="electronic">2475-9066</issn>
<doi_data>
<doi>10.21105/joss</doi>
<resource>https://joss.theoj.org</resource>
</doi_data>
</journal_metadata>
<journal_issue>
<publication_date media_type="online">
<month>07</month>
<year>2024</year>
</publication_date>
<journal_volume>
<volume>9</volume>
</journal_volume>
<issue>99</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>AutoEncoderToolkit.jl: A Julia package for training
(Variational) Autoencoders</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Manuel</given_name>
<surname>Razo-Mejia</surname>
<ORCID>https://orcid.org/0000-0002-9510-0527</ORCID>
</person_name>
</contributors>
<publication_date>
<month>07</month>
<day>30</day>
<year>2024</year>
</publication_date>
<pages>
<first_page>6794</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.06794</identifier>
</publisher_item>
<ai:program name="AccessIndicators">
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
</ai:program>
<rel:program>
<rel:related_item>
<rel:description>Software archive</rel:description>
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.12802504</rel:inter_work_relation>
</rel:related_item>
<rel:related_item>
<rel:description>GitHub review issue</rel:description>
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/6794</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.06794</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.06794</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.06794.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="arvanitidis2021">
<article_title>Latent Space Oddity: On the Curvature of Deep
Generative Models</article_title>
<author>Arvanitidis</author>
<doi>10.48550/arXiv.1710.11379</doi>
<cYear>2021</cYear>
<unstructured_citation>Arvanitidis, G., Hansen, L. K., &amp;
Hauberg, S. (2021, December 13). Latent Space Oddity: On the Curvature
of Deep Generative Models.
https://doi.org/10.48550/arXiv.1710.11379</unstructured_citation>
</citation>
<citation key="caterini2018">
<article_title>Hamiltonian Variational
Auto-Encoder</article_title>
<author>Caterini</author>
<doi>10.48550/arXiv.1805.11328</doi>
<cYear>2018</cYear>
<unstructured_citation>Caterini, A. L., Doucet, A., &amp;
Sejdinovic, D. (2018). Hamiltonian Variational Auto-Encoder. 11.
https://doi.org/10.48550/arXiv.1805.11328</unstructured_citation>
</citation>
<citation key="chadebec2020">
<article_title>Geometry-Aware Hamiltonian Variational
Auto-Encoder</article_title>
<author>Chadebec</author>
<doi>10.48550/arXiv.2010.11518</doi>
<cYear>2020</cYear>
<unstructured_citation>Chadebec, C., Mantoux, C., &amp;
Allassonnière, S. (2020, October 22). Geometry-Aware Hamiltonian
Variational Auto-Encoder.
https://doi.org/10.48550/arXiv.2010.11518</unstructured_citation>
</citation>
<citation key="chadebec2022">
<article_title>A Geometric Perspective on Variational
Autoencoders</article_title>
<author>Chadebec</author>
<doi>10.48550/arXiv.2209.07370</doi>
<cYear>2022</cYear>
<unstructured_citation>Chadebec, C., &amp; Allassonnière, S.
(2022, November 3). A Geometric Perspective on Variational Autoencoders.
https://doi.org/10.48550/arXiv.2209.07370</unstructured_citation>
</citation>
<citation key="champion2019">
<article_title>Data-driven discovery of coordinates and
governing equations</article_title>
<author>Champion</author>
<journal_title>Proceedings of the National Academy of
Sciences</journal_title>
<issue>45</issue>
<volume>116</volume>
<doi>10.1073/pnas.1906995116</doi>
<issn>0027-8424</issn>
<cYear>2019</cYear>
<unstructured_citation>Champion, K., Lusch, B., Kutz, J. N.,
&amp; Brunton, S. L. (2019). Data-driven discovery of coordinates and
governing equations. Proceedings of the National Academy of Sciences,
116(45), 22445–22451.
https://doi.org/10.1073/pnas.1906995116</unstructured_citation>
</citation>
<citation key="chen2018a">
<article_title>Metrics for Deep Generative
Models</article_title>
<author>Chen</author>
<journal_title>Proceedings of the Twenty-First International
Conference on Artificial Intelligence and Statistics</journal_title>
<issn>2640-3498</issn>
<cYear>2018</cYear>
<unstructured_citation>Chen, N., Klushyn, A., Kurle, R.,
Jiang, X., Bayer, J., &amp; Smagt, P. (2018). Metrics for Deep
Generative Models. Proceedings of the Twenty-First International
Conference on Artificial Intelligence and Statistics, 1540–1550.
https://proceedings.mlr.press/v84/chen18e.html</unstructured_citation>
</citation>
<citation key="higgins2017a">
<article_title>Β-VAE: Learning Basic Visual Concepts with a
Constrained Variational Framework</article_title>
<author>Higgins</author>
<cYear>2017</cYear>
<unstructured_citation>Higgins, I., Matthey, L., Pal, A.,
Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., &amp; Lerchner, A.
(2017). Β-VAE: Learning Basic Visual Concepts with a Constrained
Variational Framework.
https://openreview.net/forum?id=Sy2fzU9gl</unstructured_citation>
</citation>
<citation key="innes2018">
<article_title>Flux: Elegant machine learning with
Julia</article_title>
<author>Innes</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>25</issue>
<volume>3</volume>
<doi>10.21105/joss.00602</doi>
<issn>2475-9066</issn>
<cYear>2018</cYear>
<unstructured_citation>Innes, M. (2018). Flux: Elegant
machine learning with Julia. Journal of Open Source Software, 3(25),
602. https://doi.org/10.21105/joss.00602</unstructured_citation>
</citation>
<citation key="kingma2014">
<article_title>Auto-Encoding Variational
Bayes</article_title>
<author>Kingma</author>
<doi>10.48550/arXiv.1312.6114</doi>
<cYear>2014</cYear>
<unstructured_citation>Kingma, D. P., &amp; Welling, M.
(2014, May 1). Auto-Encoding Variational Bayes.
https://doi.org/10.48550/arXiv.1312.6114</unstructured_citation>
</citation>
<citation key="lian2022">
<article_title>Deep learning-enabled design of synthetic
orthologs of a signaling protein</article_title>
<author>Lian</author>
<doi>10.1101/2022.12.21.521443</doi>
<cYear>2022</cYear>
<unstructured_citation>Lian, X., Praljak, N., Subramanian,
S. K., Wasinger, S., Ranganathan, R., &amp; Ferguson, A. L. (2022). Deep
learning-enabled design of synthetic orthologs of a signaling protein
[Preprint]. Molecular Biology.
https://doi.org/10.1101/2022.12.21.521443</unstructured_citation>
</citation>
<citation key="lopez2018">
<article_title>Deep generative modeling for single-cell
transcriptomics</article_title>
<author>Lopez</author>
<journal_title>Nature Methods</journal_title>
<issue>12</issue>
<volume>15</volume>
<doi>10.1038/s41592-018-0229-2</doi>
<issn>1548-7105</issn>
<cYear>2018</cYear>
<unstructured_citation>Lopez, R., Regier, J., Cole, M. B.,
Jordan, M. I., &amp; Yosef, N. (2018). Deep generative modeling for
single-cell transcriptomics. Nature Methods, 15(12), 1053–1058.
https://doi.org/10.1038/s41592-018-0229-2</unstructured_citation>
</citation>
<citation key="rezaabad2020">
<article_title>Learning Representations by Maximizing Mutual
Information in Variational Autoencoders</article_title>
<author>Rezaabad</author>
<doi>10.48550/arXiv.1912.13361</doi>
<cYear>2020</cYear>
<unstructured_citation>Rezaabad, A. L., &amp; Vishwanath, S.
(2020, January 7). Learning Representations by Maximizing Mutual
Information in Variational Autoencoders.
https://doi.org/10.48550/arXiv.1912.13361</unstructured_citation>
</citation>
<citation key="zhao2018">
<article_title>InfoVAE: Information Maximizing Variational
Autoencoders</article_title>
<author>Zhao</author>
<doi>10.48550/arXiv.1706.02262</doi>
<cYear>2018</cYear>
<unstructured_citation>Zhao, S., Song, J., &amp; Ermon, S.
(2018, May 30). InfoVAE: Information Maximizing Variational
Autoencoders.
https://doi.org/10.48550/arXiv.1706.02262</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Binary file added joss.06794/10.21105.joss.06794.pdf
Binary file not shown.
Loading

0 comments on commit e2d2a07

Please sign in to comment.