Update README.md [GH Action]

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README.md

@@ -22,7 +22,7 @@ Contributions are always welcome, this is a community-driven project.
 > -- hopefully with the help of many awesome people from the community 🧡
 ## Overview
 
-- **Normalizing flows for probabilistic modeling and inference** (2021). [[Paper]](https://arxiv.org/abs/1912.02762) <br />  George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan<br />
+- **Normalizing flows for probabilistic modeling and inference** (2021).<br />  George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan<br /> [[Paper]](https://arxiv.org/abs/1912.02762) 
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@@ -37,27 +37,25 @@ Contributions are always welcome, this is a community-driven project.
   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** (2024). [[Paper]](https://arxiv.org/abs/2404.12484) <br />  Andrew Zammit-Mangion, Matthew Sainsbury-Dale, Raphaël Huser<br />
+- **Neural Methods for Amortized Inference** (2024).<br />  Andrew Zammit-Mangion, Matthew Sainsbury-Dale, Raphaël Huser<br /> [[Paper]](https://arxiv.org/abs/2404.12484) <br />**TL;DR**: Overview paper of amortized point estimators and full posterior estimators.<br />
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   <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}
   }
   </code>
   </pre></details>
 
-- **The frontier of simulation-based inference** (2020). [[Paper]](http://dx.doi.org/10.1073/pnas.1912789117) <br />  Kyle Cranmer, Johann Brehmer, Gilles Louppe<br />
+- **The frontier of simulation-based inference** (2020).<br />  Kyle Cranmer, Johann Brehmer, Gilles Louppe<br /> [[Paper]](http://dx.doi.org/10.1073/pnas.1912789117) 
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@@ -71,14 +69,13 @@ Contributions are always welcome, this is a community-driven project.
   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** (2023). [[Code]](https://bayesflow.org/) <br />  Stefan T. Radev, Marvin Schmitt, Lukas Schumacher, Lasse Elsemüller, Valentin Pratz, Yannik Schälte, Ullrich Köthe, Paul-Christian Bürkner<br />
+- **BayesFlow: Amortized Bayesian Workflows With Neural Networks**. [[Code]](https://bayesflow.org/) 
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@@ -91,13 +88,12 @@ Contributions are always welcome, this is a community-driven project.
   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** (2020). [[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 />
+- **sbi: A toolkit for simulation-based inference**. [[Code]](https://sbi-dev.github.io/sbi/latest/) 
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@@ -110,14 +106,13 @@ Contributions are always welcome, this is a community-driven project.
   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** (2023). [[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 />
+- **Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference** (2023).<br />  Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf<br /> [[Paper]](http://dx.doi.org/10.1103/PhysRevLett.130.171403) 
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@@ -130,13 +125,12 @@ Contributions are always welcome, this is a community-driven project.
   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** (2023). [[Paper]](https://proceedings.mlr.press/v216/radev23a) <br />  Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner<br />
+- **JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models** (2023).<br />  Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner<br /> [[Paper]](https://proceedings.mlr.press/v216/radev23a) 
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@@ -148,28 +142,26 @@ Contributions are always welcome, this is a community-driven project.
   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** (2024). [[Paper]](https://arxiv.org/abs/2405.05398) <br />  Rafael Orozco, Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann<br />
+- **ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems** (2024).<br />  Rafael Orozco, Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann<br />
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   <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.}
   }
   </code>
   </pre></details>
 ## Uncategorized
 
-- **Flow Matching for Scalable Simulation-Based Inference** (2023). [[Paper]](https://openreview.net/forum?id=D2cS6SoYlP) [[Code]](https://github.com/dingo-gw/flow-matching-posterior-estimation) <br />  Jonas Bernhard Wildberger, Maximilian Dax, Simon Buchholz, Stephen R Green, Jakob H. Macke, Bernhard Schölkopf<br />
+- **Flow Matching for Scalable Simulation-Based Inference** (2023).<br />  Jonas Bernhard Wildberger, Maximilian Dax, Simon Buchholz, Stephen R Green, Jakob H. Macke, Bernhard Schölkopf<br /> [[Paper]](https://openreview.net/forum?id=D2cS6SoYlP) [[Code]](https://github.com/dingo-gw/flow-matching-posterior-estimation) 
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