Adding methods paper (#25)

* add papers

* fix minor spelling mistakes

CONTRIBUTING.md

@@ -24,7 +24,7 @@ To contribute a new resource, follow these steps:
 
 - **Awesome Fields**: You can add important meta-information for the resource list in the `awesome-` fields:
   - `awesome-category`: The category of the paper, choose from `software, method, application, overview`.
-  - `awesome-tldr`: A short one-sentence summary of the paper (TL;DR = too long, didn't read). Please try to make the TLDR say more than the title. We know it's hard but it's well worth the effort because it helps others get the gist of a resource.
+  - `awesome-tldr`: A short one-sentence summary of the paper (TL;DR = too long, didn't read). Please try to make the TLDR say more than the title. We know it's hard, but it's well worth the effort because it helps others get the gist of a resource.
   - These fields will **not** be included in the BibTeX export.
 
 - **Awesome Links**: You can add any additional links using fields of the form `awesome-link-<key> = {<value>}`. These fields will create links in the `README.md`:
@@ -57,7 +57,7 @@ In the above example:
 
 ## Important Note
 
-The `README.md` file is automatically generated from the `resources.bib` file by a GitHub Action workflow. **Do not edit the `README.md` file directly**, as your changes will be overwritten. Instead, make all changes by editing the `data.bib` file.
+The `README.md` file is automatically generated from the `resources.bib` file by a GitHub Action workflow. **Do not edit the `README.md` file directly**, as your changes will be overwritten. Instead, make all changes by editing the `resources.bib` file.
 
 ## Submitting Your Contribution
 

resources.bib

@@ -7,6 +7,17 @@ @article{altekruger2023conditional
   awesome-category = {method}
 }
 
+@inproceedings{arruda2024anamortized,
+  title = {An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation},
+  author = {Arruda, Jonas and Sch{\"a}lte, Yannik and Peiter, Clemens and Teplytska, Olga and Jaehde, Ulrich and Hasenauer, Jan},
+  booktitle = {Forty-first International Conference on Machine Learning},
+  year = {2024},
+  awesome-category = {method},
+  awesome-tldr = {Neural posterior estimation for hierarchical models, where the NPE is used in a first stage on a local level and then repeatedly used for global inference leveraging amortization.},
+  awesome-link-paper = {https://openreview.net/forum?id=uCdcXRuHnC},
+  awesome-link-code = {https://github.com/arrjon/Amortized-NLME-Models/tree/ICML2024}
+}
+
 @misc{bahl2024advancing,
   title = {Advancing {{Tools}} for {{Simulation-Based Inference}}},
   author = {Bahl, Henning and Bres{\'o}, Victor and Crescenzo, Giovanni De and Plehn, Tilman},
@@ -480,6 +491,23 @@ @inproceedings{ward2022robust
   awesome-category = {method}
 }
 
+@article{wang2024missing,
+  doi = {10.1371/journal.pcbi.1012184},
+  author = {Wang, Zijian and Hasenauer, Jan and Schälte, Yannik},
+  journal = {PLOS Computational Biology},
+  publisher = {Public Library of Science},
+  title = {Missing data in amortized simulation-based neural posterior estimation},
+  year = {2024},
+  month = {06},
+  volume = {20},
+  pages = {1-17},
+  number = {6},
+  awesome-category = {method},
+  awesome-tldr = {Encoding missing data in a time series by augmenting the data vector with binary indicators for presence or absence yields the most robust performance.},
+  awesome-link-paper = {https://doi.org/10.1371/journal.pcbi.1012184},
+  awesome-link-code = {https://github.com/emune-dev/Data-missingness-paper}
+}
+
 @inproceedings{wehenkel2024simulationbased,
   title = {Simulation-Based Inference for Cardiovascular Models},
   booktitle = {{{NeurIPS}} Workshop},