Add awesome field information (#26)

* Add awesome field information

* Fix minor typo

Author: elseml

.github/workflows/bib2readme.py

@@ -29,7 +29,7 @@
 Contributions are always welcome, see [`CONTRIBUTING.md`](https://github.com/bayesflow-org/awesome-amortized-inference/blob/main/CONTRIBUTING.md) for a contribution guide 🧡
 
 This list currently has some overlap with the `awesome-neural-sbi` list ([Link](https://github.com/smsharma/awesome-neural-sbi)) because
-amortized inference has gained populatity in the context of simulation-based inference (SBI) with neural networks.
+amortized inference has gained popularity in the context of simulation-based inference (SBI) with neural networks.
 However, there is a trend towards broader amortized inference methods that are not necessarily simulation-based.
 This list aims to cover all amortized inference methods, including but not limited to simulation-based inference.
 We highly recommend checking out these lists for more resources on modern simulation-based inference:

resources.bib

@@ -107,6 +107,9 @@ @article{elsemuller2024deep
   issn = {1939-1463, 1082-989X},
   doi = {10.1037/met0000645},
   awesome-category = {method},
+  awesome-tldr = {Proposes a multilevel neural architecture for compressing hierarchical data structures in Bayesian model comparison.},
+  awesome-link-paper = {https://arxiv.org/abs/2301.11873},
+  awesome-link-code = {https://github.com/bayesflow-org/Hierarchical-Model-Comparison},
   langid = {english}
 }
 
@@ -116,7 +119,10 @@ @article{elsemuller2024sensitivityaware
   year = {2024},
   journal = {Transactions on Machine Learning Research},
   issn = {2835-8856},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tldr = {Proposes a framework for amortized and thus efficient sensitivity analyses on all major dimensions of a Bayesian model.},
+  awesome-link-paper = {https://openreview.net/forum?id=Kxtpa9rvM0},
+  awesome-link-code = {https://github.com/bayesflow-org/SA-ABI},
 }
 
 @inproceedings{falkiewicz2023calibrating,
@@ -292,7 +298,9 @@ @article{radev2023bayesflow
   pages = {5702},
   publisher = {The Open Journal},
   doi = {10.21105/joss.05702},
-  awesome-category = {software}
+  awesome-category = {software},
+  awesome-link-paper = {https://joss.theoj.org/papers/10.21105/joss.05702},
+  awesome-link-code = {https://github.com/bayesflow-org/bayesflow}
 }
 
 @inproceedings{radev2023jana,
@@ -453,7 +461,9 @@ @article{tejero-cantero2020sbi
   pages = {2505},
   publisher = {The Open Journal},
   doi = {10.21105/joss.02505},
-  awesome-category = {software}
+  awesome-category = {software},
+  awesome-link-paper = {https://joss.theoj.org/papers/10.21105/joss.02505},
+  awesome-link-code = {https://github.com/sbi-dev/sbi}
 }
 
 @inproceedings{tsilifis2022inverse,