add tags in bulk

Please edit if I got some wrong!
bayesflow-org · Nov 1, 2024 · 926a9d6 · 926a9d6
1 parent ed512b2
commit 926a9d6
Showing 1 changed file with 71 additions and 37 deletions.
diff --git a/resources.bib b/resources.bib
@@ -4,7 +4,8 @@ @article{altekruger2023conditional
   year = {2023},
   journal = {Transactions on Machine Learning Research},
   issn = {2835-8856},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {diagnostics; theory}
 }
 
 @inproceedings{arruda2024anamortized,
@@ -15,7 +16,8 @@ @inproceedings{arruda2024anamortized
   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}
+  awesome-link-code = {https://github.com/arrjon/Amortized-NLME-Models/tree/ICML2024},
+  awesome-tags = {parameter estimation; hierarchical models}
 }
 
 @misc{bahl2024advancing,
@@ -27,7 +29,8 @@ @misc{bahl2024advancing
   primaryclass = {hep-ph},
   publisher = {arXiv},
   archiveprefix = {arXiv},
-  awesome-category = {application}
+  awesome-category = {application},
+  awesome-tags = {physics; simulation-based}
 }
 
 @article{bieringer2021measuring,
@@ -38,7 +41,8 @@ @article{bieringer2021measuring
   volume = {10},
   pages = {126},
   doi = {10.21468/SciPostPhys.10.6.126},
-  awesome-category = {application}
+  awesome-category = {application},
+  awesome-tags = {simulation-based; physics; parameter estimation}
 }
 
 @article{bischoff2024practical,
@@ -47,7 +51,8 @@ @article{bischoff2024practical
   year = {2024},
   journal = {Transactions on Machine Learning Research},
   issn = {2835-8856},
-  awesome-category = {review}
+  awesome-category = {method},
+  awesome-tags = {diagnostics; model evaluation}
 }
 
 @misc{cannon2022investigating,
@@ -59,7 +64,8 @@ @misc{cannon2022investigating
   primaryclass = {stat},
   publisher = {arXiv},
   archiveprefix = {arXiv},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {diagnostics; misspecification; simulation-based}
 }
 
 @article{cranmer2020frontier,
@@ -86,7 +92,8 @@ @article{dax2023neural
   pages = {171403},
   doi = {10.1103/PhysRevLett.130.171403},
   awesome-category = {method},
-  awesome-link-paper = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.130.171403}
+  awesome-link-paper = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.130.171403},
+  awesome-tags = {likelihood-based; physics; parameter estimation}
 }
 
 @article{dingeldein2024amortized,
@@ -96,7 +103,8 @@ @article{dingeldein2024amortized
   journal = {bioRxiv : the preprint server for biology},
   pages = {2024.07.23.604154},
   doi = {10.1101/2024.07.23.604154},
-  awesome-category = {application}
+  awesome-category = {application},
+  awesome-tags = {biology; simulation-based; parameter estimation}
 }
 
 @article{elsemuller2024deep,
@@ -123,14 +131,16 @@ @article{elsemuller2024sensitivityaware
   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},
+  awesome-tags = {sensitivity analysis; simulation-based; meta learning}
 }
 
 @inproceedings{falkiewicz2023calibrating,
   title = {Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability},
   booktitle = {Thirty-Seventh Conference on Neural Information Processing Systems},
   author = {Falkiewicz, Maciej and Takeishi, Naoya and Shekhzadeh, Imahn and Wehenkel, Antoine and Delaunoy, Arnaud and Louppe, Gilles and Kalousis, Alexandros},
   year = {2023},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {diagnostics; simulation-based}
 }
 
 @inproceedings{foster2021deep,
@@ -141,7 +151,7 @@ @inproceedings{foster2021deep
   volume = {139},
   publisher = {PMLR},
   awesome-category = {method},
-  awesome-tags = {BED; adaptive design}
+  awesome-tags = {experimental design; adaptive design}
 }
 
 @article{ghaderi-kangavari2023general,
@@ -155,7 +165,7 @@ @article{ghaderi-kangavari2023general
   issn = {2522-0861, 2522-087X},
   doi = {10.1007/s42113-023-00167-4},
   awesome-category = {application},
-  awesome-tags = {cognitive modeling},
+  awesome-tags = {cognitive modeling; simulation-based; parameter estimation},
 }
 
 @misc{habermann2024amortized,
@@ -167,7 +177,7 @@ @misc{habermann2024amortized
   publisher = {arXiv},
   archiveprefix = {arXiv},
   awesome-category = {method},
-  awesome-tags = {parameter estimation}
+  awesome-tags = {parameter estimation; hierarchical models; simulation-based}
 }
 
 @article{heringhaus2022reliable,
@@ -181,6 +191,7 @@ @article{heringhaus2022reliable
   issn = {1424-8220},
   doi = {10.3390/s22145408},
   awesome-category = {application},
+  awesome-tags = {parameter estimation; simulation-based; engineering}
 }
 
 @misc{lavin2022simulation,
@@ -217,7 +228,7 @@ @inproceedings{moon2023amortized
   publisher = {Association for Computing Machinery},
   doi = {10.1145/3544548.3581439},
   awesome-category = {application},
-  awesome-tags = {human-computer interaction}
+  awesome-tags = {human-computer interaction, simulation-based; user interfaces}
 }
 
 @article{noever-castelos2022model,
@@ -231,7 +242,8 @@ @article{noever-castelos2022model
   issn = {1095-4244, 1099-1824},
   doi = {10.1002/we.2687},
   awesome-category = {application},
-  langid = {english}
+  langid = {english},
+  awesome-tags = {simulation-based}
 }
 
 @article{papamakarios2021normalizing,
@@ -258,7 +270,8 @@ @article{radev2021outbreakflow
   issn = {1553-7358},
   doi = {10.1371/journal.pcbi.1009472},
   awesome-category = {application},
-  langid = {english}
+  langid = {english},
+  awesome-tags = {epidemiology; public health; simulation-based}
 }
 
 @article{radev2020bayesflow,
@@ -272,7 +285,8 @@ @article{radev2020bayesflow
   pages = {1452--1466},
   issn = {2162-237X, 2162-2388},
   doi = {10.1109/TNNLS.2020.3042395},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {simulation-based; summary learning; parameter estimation}
 }
 
 @article{radev2023amortized,
@@ -285,7 +299,8 @@ @article{radev2023amortized
   pages = {4903--4917},
   issn = {2162-237X, 2162-2388},
   doi = {10.1109/TNNLS.2021.3124052},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {model comparison}
 }
 
 @article{radev2023bayesflow,
@@ -313,7 +328,8 @@ @inproceedings{radev2023jana
   volume = {216},
   pages = {1695--1706},
   publisher = {PMLR},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {joint learning; simulation-based; diagnostics}
 }
 
 @article{sainsbury-dale2024likelihoodfree,
@@ -328,7 +344,7 @@ @article{sainsbury-dale2024likelihoodfree
   doi = {10.1080/00031305.2023.2249522},
   awesome-category = {method},
   langid = {english},
-  awesome-tags = {parameter estimation}
+  awesome-tags = {parameter estimation; point estimation; simulation-based}
 }
 
 @misc{schmitt2023fuse,
@@ -340,7 +356,8 @@ @misc{schmitt2023fuse
   eprint = {2311.10671},
   publisher = {arXiv},
   archiveprefix = {arXiv},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {summary learning; parameter estimation}
 }
 
 @inproceedings{schmitt2024amortized,
@@ -349,15 +366,17 @@ @inproceedings{schmitt2024amortized
   author = {Schmitt, Marvin and Li, Chengkun and Vehtari, Aki and Acerbi, Luigi and B{\"u}rkner, Paul-Christian and Radev, Stefan T.},
   year = {2024},
   publisher = {arXiv},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {likelihood-based; workflow}
 }
 
 @inproceedings{schmitt2024consistency,
   title = {Consistency {{Models}} for {{Scalable}} and {{Fast Simulation-Based Inference}}},
   booktitle = {Proceedings of the 38th International Conference on Neural Information Processing Systems},
   author = {Schmitt, Marvin and Pratz, Valentin and K{\"o}the, Ullrich and B{\"u}rkner, Paul-Christian and Radev, Stefan T.},
   year = {2024},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {simulation-based}
 }
 
 @inproceedings{schmitt2024detecting,
@@ -369,7 +388,8 @@ @inproceedings{schmitt2024detecting
   publisher = {Springer Nature Switzerland},
   address = {Cham},
   awesome-category = {method},
-  isbn = {978-3-031-54605-1}
+  isbn = {978-3-031-54605-1},
+  awesome-tags = {diagnostics; workflow}
 }
 
 @inproceedings{schmitt2024leveraging,
@@ -382,7 +402,8 @@ @inproceedings{schmitt2024leveraging
   volume = {235},
   pages = {43723--43741},
   publisher = {PMLR},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {likelihood-based; simulation-based}
 }
 
 @article{schumacher2023neural,
@@ -396,7 +417,8 @@ @article{schumacher2023neural
   issn = {2045-2322},
   doi = {10.1038/s41598-023-40278-3},
   awesome-category = {application},
-  langid = {english}
+  langid = {english},
+  awesome-tags = {simulation-based; dynamic modeling; parameter estimation}
 }
 
 @inproceedings{sharrock2024sequential,
@@ -409,7 +431,8 @@ @inproceedings{sharrock2024sequential
   volume = {235},
   pages = {44565--44602},
   publisher = {PMLR},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {simulation-based}
 }
 
 @article{shiono2021estimation,
@@ -422,7 +445,8 @@ @article{shiono2021estimation
   issn = {01651889},
   doi = {10.1016/j.jedc.2021.104082},
   awesome-category = {application},
-  langid = {english}
+  langid = {english},
+  awesome-tags = {agent modeling; simulation-based}
 }
 
 @article{siahkoohi2023reliable,
@@ -436,7 +460,8 @@ @article{siahkoohi2023reliable
   issn = {0016-8033, 1942-2156},
   doi = {10.1190/geo2022-0472.1},
   awesome-category = {application},
-  langid = {english}
+  langid = {english},
+  awesome-tags = {physics; correction; misspecification}
 }
 
 @misc{starostin2024fast,
@@ -448,7 +473,8 @@ @misc{starostin2024fast
   primaryclass = {cond-mat, physics:physics, stat},
   publisher = {arXiv},
   archiveprefix = {arXiv},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {physics; meta learning}
 }
 
 @article{tejero-cantero2020sbi,
@@ -476,7 +502,8 @@ @inproceedings{tsilifis2022inverse
   doi = {10.2514/6.2022-0631},
   awesome-category = {application},
   isbn = {978-1-62410-631-6},
-  langid = {english}
+  langid = {english},
+  awesome-tags = {engineering; aerospace; simulation-based}
 }
 
 @article{vonkrause2022mental,
@@ -490,15 +517,17 @@ @article{vonkrause2022mental
   issn = {2397-3374},
   doi = {10.1038/s41562-021-01282-7},
   awesome-category = {application},
-  langid = {english}
+  langid = {english},
+  awesome-tags = {cognitive modeling; parameter estimation}
 }
 
 @inproceedings{ward2022robust,
   title = {Robust Neural Posterior Estimation and Statistical Model Criticism},
   booktitle = {Proceedings of the 36th International Conference on Neural Information Processing Systems},
   author = {Ward, Daniel and Cannon, Patrick and Beaumont, Mark and Fasiolo, Matteo and Schmon, Sebastian M.},
   year = {2022},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {model evaluation; simulation-based}
 }
 
 @article{wang2024missing,
@@ -515,15 +544,17 @@ @article{wang2024missing
   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}
+  awesome-link-code = {https://github.com/emune-dev/Data-missingness-paper},
+  awesome-tags = {missing data; simulation-based; parameter estimation}
 }
 
 @inproceedings{wehenkel2024simulationbased,
   title = {Simulation-Based Inference for Cardiovascular Models},
   booktitle = {{{NeurIPS}} Workshop},
   author = {Wehenkel, Antoine and Behrmann, Jens and Miller, Andrew C. and Sapiro, Guillermo and Sener, Ozan and Cameto, Marco Cuturi and Jacobsen, J{\"o}rn-Henrik},
   year = {2024},
-  awesome-category = {application}
+  awesome-category = {application},
+  awesome-tags = {medicine; simulation-based; parameter estimation}
 }
 
 @inproceedings{wildberger2023flow,
@@ -534,7 +565,8 @@ @inproceedings{wildberger2023flow
   year = {2023},
   volume = {36},
   pages = {16837--16864},
-  awesome-category = {method}
+  awesome-category = {method},
+  awesome-tags = {simulation-based}
 }
 
 @article{zeng2023probabilistic,
@@ -548,13 +580,15 @@ @article{zeng2023probabilistic
   issn = {2190-5452, 2190-5479},
   doi = {10.1007/s13349-022-00638-5},
   awesome-category = {application},
-  langid = {english}
+  langid = {english},
+  awesome-tags = {structural health monitoring}
 }
 
 @inproceedings{zhou2024evaluating,
   title = {Evaluating {{Sparse Galaxy Simulations}} via {{Out-of-Distribution Detection}} and {{Amortized Bayesian Model Comparison}}},
   booktitle = {38th {{Conference}} on {{Neural Information Processing Systems}}},
   author = {Zhou, Lingyi and Radev, Stefan T. and Oliver, William H. and Obreja, Aura and Jin, Zehao and Buck, Tobias},
   year = {2024},
-  awesome-category = {application}
+  awesome-category = {application},
+  awesome-tags = {physics; model evaluation; model comparison}
 }