Merge branch 'main' of https://github.com/marvinschmitt/awesome-amortized-inference

Author: marvinschmitt

README.md

@@ -7,148 +7,148 @@ Feel free to explore the entries below and use the provided BibTeX information f
 Contributions are always welcome, this is a community-driven project.
 ## Overview
 
-- **Normalizing flows for probabilistic modeling and inference**.<br />  George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
+- **Normalizing flows for probabilistic modeling and inference**.<br />  George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan<br />
   <details>
   <summary>Show BibTeX</summary>
   <pre><code>
   @article{papamakarios2021normalizing,
-  title = {Normalizing flows for probabilistic modeling and inference},
-  year = {2021},
-  publisher = {JMLR.org},
-  volume = {22},
-  number = {1},
-  issn = {1532-4435},
-  journal = {J. Mach. Learn. Res.},
-  month = {jan},
-  articleno = {57},
-  numpages = {64},
-  category = {overview},
-  author = {Papamakarios, George and Nalisnick, Eric and Rezende, Danilo Jimenez and Mohamed, Shakir and Lakshminarayanan, Balaji}
+  title = {Normalizing flows for probabilistic modeling and inference},
+  year = {2021},
+  publisher = {JMLR.org},
+  volume = {22},
+  number = {1},
+  issn = {1532-4435},
+  journal = {J. Mach. Learn. Res.},
+  month = {jan},
+  articleno = {57},
+  numpages = {64},
+  category = {overview},
+  author = {Papamakarios, George and Nalisnick, Eric and Rezende, Danilo Jimenez and Mohamed, Shakir and Lakshminarayanan, Balaji}
   }
   </code>
   </pre></details>
 
-- **Neural Methods for Amortized Inference**. [[Paper]](https://arxiv.org/abs/2404.12484) <br />  Andrew Zammit-Mangion, Matthew Sainsbury-Dale, Raphaël Huser
+- **Neural Methods for Amortized Inference**. [[Paper]](https://arxiv.org/abs/2404.12484) <br />  Andrew Zammit-Mangion, Matthew Sainsbury-Dale, Raphaël Huser<br />
   <details>
   <summary>Show BibTeX</summary>
   <pre><code>
   @misc{zammit-mangion2024neural,
-  title = {Neural Methods for Amortized Inference},
-  publisher = {arXiv},
-  year = {2024},
-  category = {overview},
-  author = {Zammit-Mangion, Andrew and Sainsbury-Dale, Matthew and Huser, Raphaël}
+  title = {Neural Methods for Amortized Inference},
+  publisher = {arXiv},
+  year = {2024},
+  category = {overview},
+  author = {Zammit-Mangion, Andrew and Sainsbury-Dale, Matthew and Huser, Raphaël}
   }
   </code>
   </pre></details>
 
-- **The frontier of simulation-based inference**. [[Paper]](http://dx.doi.org/10.1073/pnas.1912789117) <br />  Kyle Cranmer, Johann Brehmer, Gilles Louppe
+- **The frontier of simulation-based inference**. [[Paper]](http://dx.doi.org/10.1073/pnas.1912789117) <br />  Kyle Cranmer, Johann Brehmer, Gilles Louppe<br />
   <details>
   <summary>Show BibTeX</summary>
   <pre><code>
   @article{Cranmer2020,
-  title = {The frontier of simulation-based inference},
-  volume = {117},
-  ISSN = {1091-6490},
-  DOI = {10.1073/pnas.1912789117},
-  number = {48},
-  journal = {Proceedings of the National Academy of Sciences},
-  publisher = {Proceedings of the National Academy of Sciences},
-  year = {2020},
-  pages = {30055–30062},
-  category = {overview},
-  author = {Cranmer, Kyle and Brehmer, Johann and Louppe, Gilles}
+  title = {The frontier of simulation-based inference},
+  volume = {117},
+  ISSN = {1091-6490},
+  DOI = {10.1073/pnas.1912789117},
+  number = {48},
+  journal = {Proceedings of the National Academy of Sciences},
+  publisher = {Proceedings of the National Academy of Sciences},
+  year = {2020},
+  pages = {30055–30062},
+  category = {overview},
+  author = {Cranmer, Kyle and Brehmer, Johann and Louppe, Gilles}
   }
   </code>
   </pre></details>
 ## Software
 
-- **BayesFlow: Amortized Bayesian Workflows With Neural Networks**. [[Code]](https://bayesflow.org/) <br />  Stefan T. Radev, Marvin Schmitt, Lukas Schumacher, Lasse Elsemüller, Valentin Pratz, Yannik Schälte, Ullrich Köthe, Paul-Christian Bürkner
+- **BayesFlow: Amortized Bayesian Workflows With Neural Networks**. [[Code]](https://bayesflow.org/) <br />  Stefan T. Radev, Marvin Schmitt, Lukas Schumacher, Lasse Elsemüller, Valentin Pratz, Yannik Schälte, Ullrich Köthe, Paul-Christian Bürkner<br />
   <details>
   <summary>Show BibTeX</summary>
   <pre><code>
   @article{radev2023bayesflow,
-  doi = {10.21105/joss.05702},
-  year = {2023},
-  publisher = {The Open Journal},
-  volume = {8},
-  number = {89},
-  pages = {5702},
-  title = {BayesFlow: Amortized Bayesian Workflows With Neural Networks},
-  journal = {Journal of Open Source Software},
-  category = {software},
-  author = {Radev, Stefan T. and Schmitt, Marvin and Schumacher, Lukas and Elsemüller, Lasse and Pratz, Valentin and Schälte, Yannik and Köthe, Ullrich and Bürkner, Paul-Christian}
+  doi = {10.21105/joss.05702},
+  year = {2023},
+  publisher = {The Open Journal},
+  volume = {8},
+  number = {89},
+  pages = {5702},
+  title = {BayesFlow: Amortized Bayesian Workflows With Neural Networks},
+  journal = {Journal of Open Source Software},
+  category = {software},
+  author = {Radev, Stefan T. and Schmitt, Marvin and Schumacher, Lukas and Elsemüller, Lasse and Pratz, Valentin and Schälte, Yannik and Köthe, Ullrich and Bürkner, Paul-Christian}
   }
   </code>
   </pre></details>
 
-- **sbi: A toolkit for simulation-based inference**. [[Code]](https://sbi-dev.github.io/sbi/latest/) <br />  Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, Jakob H. Macke
+- **sbi: A toolkit for simulation-based inference**. [[Code]](https://sbi-dev.github.io/sbi/latest/) <br />  Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, Jakob H. Macke<br />
   <details>
   <summary>Show BibTeX</summary>
   <pre><code>
   @article{tejero-cantero2020sbi,
-  doi = {10.21105/joss.02505},
-  year = {2020},
-  publisher = {The Open Journal},
-  volume = {5},
-  number = {52},
-  pages = {2505},
-  title = {sbi: A toolkit for simulation-based inference},
-  journal = {Journal of Open Source Software},
-  category = {software},
-  author = {Tejero-Cantero, Alvaro and Boelts, Jan and Deistler, Michael and Lueckmann, Jan-Matthis and Durkan, Conor and Gonçalves, Pedro J. and Greenberg, David S. and Macke, Jakob H.}
+  doi = {10.21105/joss.02505},
+  year = {2020},
+  publisher = {The Open Journal},
+  volume = {5},
+  number = {52},
+  pages = {2505},
+  title = {sbi: A toolkit for simulation-based inference},
+  journal = {Journal of Open Source Software},
+  category = {software},
+  author = {Tejero-Cantero, Alvaro and Boelts, Jan and Deistler, Michael and Lueckmann, Jan-Matthis and Durkan, Conor and Gonçalves, Pedro J. and Greenberg, David S. and Macke, Jakob H.}
   }
   </code>
   </pre></details>
 ## Paper
 
-- **Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference**. [[Paper]](http://dx.doi.org/10.1103/PhysRevLett.130.171403) <br />  Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
+- **Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference**. [[Paper]](http://dx.doi.org/10.1103/PhysRevLett.130.171403) <br />  Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf<br />
   <details>
   <summary>Show BibTeX</summary>
   <pre><code>
   @article{dax2023neural,
-  title = {Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference},
-  volume = {130},
-  ISSN = {1079-7114},
-  DOI = {10.1103/physrevlett.130.171403},
-  number = {17},
-  journal = {Physical Review Letters},
-  publisher = {American Physical Society (APS)},
-  year = {2023},
-  category = {paper},
-  author = {Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and P\"{u}rrer, Michael and Wildberger, Jonas and Macke, Jakob H. and Buonanno, Alessandra and Sch\"{o}lkopf, Bernhard}
+  title = {Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference},
+  volume = {130},
+  ISSN = {1079-7114},
+  DOI = {10.1103/physrevlett.130.171403},
+  number = {17},
+  journal = {Physical Review Letters},
+  publisher = {American Physical Society (APS)},
+  year = {2023},
+  category = {paper},
+  author = {Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and P\"{u}rrer, Michael and Wildberger, Jonas and Macke, Jakob H. and Buonanno, Alessandra and Sch\"{o}lkopf, Bernhard}
   }
   </code>
   </pre></details>
 
-- **JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models**. [[Paper]](https://proceedings.mlr.press/v216/radev23a) <br />  Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner
+- **JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models**. [[Paper]](https://proceedings.mlr.press/v216/radev23a) <br />  Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner<br />
   <details>
   <summary>Show BibTeX</summary>
   <pre><code>
   @inproceedings{radev2023jana,
-  title = {{JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models}},
-  booktitle = {Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence},
-  pages = {1695--1706},
-  year = {2023},
-  volume = {216},
-  series = {Proceedings of Machine Learning Research},
-  publisher = {PMLR},
-  category = {paper},
-  author = {Radev, Stefan T. and Schmitt, Marvin and Pratz, Valentin and Picchini, Umberto and K\"othe, Ullrich and B\"urkner, Paul-Christian}
+  title = {{JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models}},
+  booktitle = {Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence},
+  pages = {1695--1706},
+  year = {2023},
+  volume = {216},
+  series = {Proceedings of Machine Learning Research},
+  publisher = {PMLR},
+  category = {paper},
+  author = {Radev, Stefan T. and Schmitt, Marvin and Pratz, Valentin and Picchini, Umberto and K\"othe, Ullrich and B\"urkner, Paul-Christian}
   }
   </code>
   </pre></details>
 
-- **ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems**. [[Paper]](https://arxiv.org/abs/2405.05398) <br />  Rafael Orozco, Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann
+- **ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems**. [[Paper]](https://arxiv.org/abs/2405.05398) <br />  Rafael Orozco, Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann<br />
   <details>
   <summary>Show BibTeX</summary>
   <pre><code>
   @misc{orozco2024aspire,
-  Title = {ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems},
-  Year = {2024},
-  Eprint = {arXiv:2405.05398},
-  category = {paper},
-  author = {Orozco, Rafael and Siahkoohi, Ali and Louboutin, Mathias and Herrmann, Felix J.}
+  Title = {ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems},
+  Year = {2024},
+  Eprint = {arXiv:2405.05398},
+  category = {paper},
+  author = {Orozco, Rafael and Siahkoohi, Ali and Louboutin, Mathias and Herrmann, Felix J.}
   }
   </code>
   </pre></details>