-
Notifications
You must be signed in to change notification settings - Fork 22
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #6347 from openjournals/joss.07606
Merging automatically
- Loading branch information
Showing
4 changed files
with
1,230 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,249 @@ | ||
<?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>20250118170553-199c16babe920ba975d7ac6f884862cde87b9c3b</doi_batch_id> | ||
<timestamp>20250118170553</timestamp> | ||
<depositor> | ||
<depositor_name>JOSS Admin</depositor_name> | ||
<email_address>[email protected]</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>01</month> | ||
<year>2025</year> | ||
</publication_date> | ||
<journal_volume> | ||
<volume>10</volume> | ||
</journal_volume> | ||
<issue>105</issue> | ||
</journal_issue> | ||
<journal_article publication_type="full_text"> | ||
<titles> | ||
<title>SBIAX: Density-estimation simulation-based inference in JAX</title> | ||
</titles> | ||
<contributors> | ||
<person_name sequence="first" contributor_role="author"> | ||
<given_name>Jed</given_name> | ||
<surname>Homer</surname> | ||
<affiliations> | ||
<institution><institution_name>University Observatory, Faculty for Physics, Ludwig-Maximilians-Universität München, Scheinerstrasse 1, München, Deustchland.</institution_name><institution_id type="ror">https://ror.org/00hx57361</institution_id></institution> | ||
<institution><institution_name>Munich Center for Machine Learning.</institution_name><institution_id type="ror">https://ror.org/00hx57361</institution_id></institution> | ||
</affiliations> | ||
<ORCID>https://orcid.org/0009-0002-0985-1437</ORCID> | ||
</person_name> | ||
<person_name sequence="additional" | ||
contributor_role="author"> | ||
<given_name>Oliver</given_name> | ||
<surname>Friedrich</surname> | ||
<affiliations> | ||
<institution><institution_name>University Observatory, Faculty for Physics, Ludwig-Maximilians-Universität München, Scheinerstrasse 1, München, Deustchland.</institution_name><institution_id type="ror">https://ror.org/00hx57361</institution_id></institution> | ||
<institution><institution_name>Munich Center for Machine Learning.</institution_name><institution_id type="ror">https://ror.org/00hx57361</institution_id></institution> | ||
<institution><institution_name>Excellence Cluster ORIGINS, Boltzmannstr. 2, 85748 Garching, Deutschland.</institution_name><institution_id type="ror">https://ror.org/00hx57361</institution_id></institution> | ||
</affiliations> | ||
<ORCID>https://orcid.org/0000-0001-6120-4988</ORCID> | ||
</person_name> | ||
</contributors> | ||
<publication_date> | ||
<month>01</month> | ||
<day>18</day> | ||
<year>2025</year> | ||
</publication_date> | ||
<pages> | ||
<first_page>7606</first_page> | ||
</pages> | ||
<publisher_item> | ||
<identifier id_type="doi">10.21105/joss.07606</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.14679498</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/7606</rel:inter_work_relation> | ||
</rel:related_item> | ||
</rel:program> | ||
<doi_data> | ||
<doi>10.21105/joss.07606</doi> | ||
<resource>https://joss.theoj.org/papers/10.21105/joss.07606</resource> | ||
<collection property="text-mining"> | ||
<item> | ||
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.07606.pdf</resource> | ||
</item> | ||
</collection> | ||
</doi_data> | ||
<citation_list> | ||
<citation key="ffjord"> | ||
<article_title>FFJORD: Free-form continuous dynamics for scalable reversible generative models</article_title> | ||
<author>Grathwohl</author> | ||
<cYear>2018</cYear> | ||
<unstructured_citation>Grathwohl, W., Chen, R. T. Q., Bettencourt, J., Sutskever, I., & Duvenaud, D. (2018). FFJORD: Free-form continuous dynamics for scalable reversible generative models. https://arxiv.org/abs/1810.01367</unstructured_citation> | ||
</citation> | ||
<citation key="blackjax"> | ||
<article_title>BlackJAX: Composable Bayesian inference in JAX</article_title> | ||
<author>Cabezas</author> | ||
<cYear>2024</cYear> | ||
<unstructured_citation>Cabezas, A., Corenflos, A., Lao, J., & Louf, R. (2024). BlackJAX: Composable Bayesian inference in JAX. https://arxiv.org/abs/2402.10797</unstructured_citation> | ||
</citation> | ||
<citation key="mafs"> | ||
<article_title>Masked autoregressive flow for density estimation</article_title> | ||
<author>Papamakarios</author> | ||
<cYear>2018</cYear> | ||
<unstructured_citation>Papamakarios, G., Pavlakou, T., & Murray, I. (2018). Masked autoregressive flow for density estimation. https://arxiv.org/abs/1705.07057</unstructured_citation> | ||
</citation> | ||
<citation key="flowjax"> | ||
<article_title>FlowJAX: Distributions and normalizing flows in JAX</article_title> | ||
<author>Ward</author> | ||
<doi>10.5281/zenodo.10402073</doi> | ||
<cYear>2024</cYear> | ||
<unstructured_citation>Ward, D. (2024). FlowJAX: Distributions and normalizing flows in JAX (Version 16.0.0). https://doi.org/10.5281/zenodo.10402073</unstructured_citation> | ||
</citation> | ||
<citation key="flowmatching"> | ||
<article_title>Flow matching for generative modeling</article_title> | ||
<author>Lipman</author> | ||
<cYear>2023</cYear> | ||
<unstructured_citation>Lipman, Y., Chen, R. T. Q., Ben-Hamu, H., Nickel, M., & Le, M. (2023). Flow matching for generative modeling. https://arxiv.org/abs/2210.02747</unstructured_citation> | ||
</citation> | ||
<citation key="optuna"> | ||
<article_title>Optuna: A next-generation hyperparameter optimization framework</article_title> | ||
<author>Akiba</author> | ||
<journal_title>The 25th ACM SIGKDD international conference on knowledge discovery & data mining</journal_title> | ||
<cYear>2019</cYear> | ||
<unstructured_citation>Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. The 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2623–2631.</unstructured_citation> | ||
</citation> | ||
<citation key="sbi"> | ||
<article_title>The frontier of simulation-based inference</article_title> | ||
<author>Cranmer</author> | ||
<journal_title>Proceedings of the National Academy of Sciences</journal_title> | ||
<issue>48</issue> | ||
<volume>117</volume> | ||
<doi>10.1073/pnas.1912789117</doi> | ||
<issn>1091-6490</issn> | ||
<cYear>2020</cYear> | ||
<unstructured_citation>Cranmer, K., Brehmer, J., & Louppe, G. (2020). The frontier of simulation-based inference. Proceedings of the National Academy of Sciences, 117(48), 30055–30062. https://doi.org/10.1073/pnas.1912789117</unstructured_citation> | ||
</citation> | ||
<citation key="delfi"> | ||
<article_title>Fast likelihood-free cosmology with neural density estimators and active learning</article_title> | ||
<author>Alsing</author> | ||
<journal_title>Monthly Notices of the Royal Astronomical Society</journal_title> | ||
<doi>10.1093/mnras/stz1960</doi> | ||
<issn>1365-2966</issn> | ||
<cYear>2019</cYear> | ||
<unstructured_citation>Alsing, J., Charnock, T., Feeney, S., & Wandelt, B. (2019). Fast likelihood-free cosmology with neural density estimators and active learning. Monthly Notices of the Royal Astronomical Society. https://doi.org/10.1093/mnras/stz1960</unstructured_citation> | ||
</citation> | ||
<citation key="papamakarios"> | ||
<article_title>Neural density estimation and likelihood-free inference</article_title> | ||
<author>Papamakarios</author> | ||
<cYear>2019</cYear> | ||
<unstructured_citation>Papamakarios, G. (2019). Neural density estimation and likelihood-free inference. https://arxiv.org/abs/1910.13233</unstructured_citation> | ||
</citation> | ||
<citation key="npe"> | ||
<article_title>Automatic posterior transformation for likelihood-free inference</article_title> | ||
<author>Greenberg</author> | ||
<cYear>2019</cYear> | ||
<unstructured_citation>Greenberg, D. S., Nonnenmacher, M., & Macke, J. H. (2019). Automatic posterior transformation for likelihood-free inference. https://arxiv.org/abs/1905.07488</unstructured_citation> | ||
</citation> | ||
<citation key="jax"> | ||
<article_title>JAX: Composable transformations of Python+NumPy programs</article_title> | ||
<author>Bradbury</author> | ||
<cYear>2018</cYear> | ||
<unstructured_citation>Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., & Zhang, Q. (2018). JAX: Composable transformations of Python+NumPy programs (Version 0.3.13). http://github.com/jax-ml/jax</unstructured_citation> | ||
</citation> | ||
<citation key="equinox"> | ||
<article_title>Equinox: Neural networks in JAX via callable PyTrees and filtered transformations</article_title> | ||
<author>Kidger</author> | ||
<journal_title>Differentiable Programming workshop at Neural Information Processing Systems 2021</journal_title> | ||
<cYear>2021</cYear> | ||
<unstructured_citation>Kidger, P., & Garcia, C. (2021). Equinox: Neural networks in JAX via callable PyTrees and filtered transformations. Differentiable Programming Workshop at Neural Information Processing Systems 2021.</unstructured_citation> | ||
</citation> | ||
<citation key="optax"> | ||
<article_title>The DeepMind JAX Ecosystem</article_title> | ||
<author>DeepMind</author> | ||
<cYear>2020</cYear> | ||
<unstructured_citation>DeepMind, Babuschkin, I., Baumli, K., Bell, A., Bhupatiraju, S., Bruce, J., Buchlovsky, P., Budden, D., Cai, T., Clark, A., Danihelka, I., Dedieu, A., Fantacci, C., Godwin, J., Jones, C., Hemsley, R., Hennigan, T., Hessel, M., Hou, S., … Viola, F. (2020). The DeepMind JAX Ecosystem. http://github.com/google-deepmind</unstructured_citation> | ||
</citation> | ||
<citation key="diffrax"> | ||
<article_title>On neural differential equations</article_title> | ||
<author>Kidger</author> | ||
<cYear>2022</cYear> | ||
<unstructured_citation>Kidger, P. (2022). On neural differential equations. https://arxiv.org/abs/2202.02435</unstructured_citation> | ||
</citation> | ||
<citation key="homersbi"> | ||
<article_title>Simulation-based inference has its own Dodelson-Schneider effect (but it knows that it does)</article_title> | ||
<author>Homer</author> | ||
<cYear>2024</cYear> | ||
<unstructured_citation>Homer, J., Friedrich, O., & Gruen, D. (2024). Simulation-based inference has its own Dodelson-Schneider effect (but it knows that it does). https://arxiv.org/abs/2412.02311</unstructured_citation> | ||
</citation> | ||
<citation key="sbimacke"> | ||
<article_title>Sbi: A toolkit for simulation-based inference</article_title> | ||
<author>Tejero-Cantero</author> | ||
<journal_title>Journal of Open Source Software</journal_title> | ||
<issue>52</issue> | ||
<volume>5</volume> | ||
<doi>10.21105/joss.02505</doi> | ||
<cYear>2020</cYear> | ||
<unstructured_citation>Tejero-Cantero, A., Boelts, J., Deistler, M., Lueckmann, J.-M., Durkan, C., Gonçalves, P. J., Greenberg, D. S., & Macke, J. H. (2020). Sbi: A toolkit for simulation-based inference. Journal of Open Source Software, 5(52), 2505. https://doi.org/10.21105/joss.02505</unstructured_citation> | ||
</citation> | ||
<citation key="sbidirmeier"> | ||
<article_title>SBIJAX: Simulation-based inference in JAX.</article_title> | ||
<author>Dirmeir</author> | ||
<cYear>2024</cYear> | ||
<unstructured_citation>Dirmeir, S. (2024). SBIJAX: Simulation-based inference in JAX. (Version 0.3.0). https://github.com/dirmeier/sbijax</unstructured_citation> | ||
</citation> | ||
<citation key="Euclid"> | ||
<article_title>Euclid definition study report</article_title> | ||
<author>Laureijs</author> | ||
<cYear>2011</cYear> | ||
<unstructured_citation>Laureijs, R., Amiaux, J., Arduini, S., Auguères, J. -L., Brinchmann, J., Cole, R., Cropper, M., Dabin, C., Duvet, L., Ealet, A., Garilli, B., Gondoin, P., Guzzo, L., Hoar, J., Hoekstra, H., Holmes, R., Kitching, T., Maciaszek, T., Mellier, Y., … Zucca, E. (2011). Euclid definition study report. https://arxiv.org/abs/1110.3193</unstructured_citation> | ||
</citation> | ||
<citation key="DESI"> | ||
<article_title>The dark energy spectroscopic instrument (DESI)</article_title> | ||
<author>Levi</author> | ||
<cYear>2019</cYear> | ||
<unstructured_citation>Levi, M. E., Allen, L. E., Raichoor, A., Baltay, C., BenZvi, S., Beutler, F., Bolton, A., Castander, F. J., Chuang, C.-H., Cooper, A., Cuby, J.-G., Dey, A., Eisenstein, D., Fan, X., Flaugher, B., Frenk, C., Gonzalez-Morales, A. X., Graur, O., Guy, J., … Zu, Y. (2019). The dark energy spectroscopic instrument (DESI). https://arxiv.org/abs/1907.10688</unstructured_citation> | ||
</citation> | ||
<citation key="ABC"> | ||
<article_title>Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician</article_title> | ||
<author>Rubin</author> | ||
<journal_title>The Annals of Statistics</journal_title> | ||
<issue>4</issue> | ||
<volume>12</volume> | ||
<doi>10.1214/aos/1176346785</doi> | ||
<cYear>1984</cYear> | ||
<unstructured_citation>Rubin, D. B. (1984). Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician. The Annals of Statistics, 12(4), 1151–1172. https://doi.org/10.1214/aos/1176346785</unstructured_citation> | ||
</citation> | ||
<citation key="NRE"> | ||
<article_title>Towards reliable simulation-based inference with balanced neural ratio estimation</article_title> | ||
<author>Delaunoy</author> | ||
<cYear>2022</cYear> | ||
<unstructured_citation>Delaunoy, A., Hermans, J., Rozet, F., Wehenkel, A., & Louppe, G. (2022). Towards reliable simulation-based inference with balanced neural ratio estimation. https://arxiv.org/abs/2208.13624</unstructured_citation> | ||
</citation> | ||
</citation_list> | ||
</journal_article> | ||
</journal> | ||
</body> | ||
</doi_batch> |
Binary file not shown.
Oops, something went wrong.