diff --git a/joss.05776/10.21105.joss.05776.crossref.xml b/joss.05776/10.21105.joss.05776.crossref.xml new file mode 100644 index 0000000000..34743d38f8 --- /dev/null +++ b/joss.05776/10.21105.joss.05776.crossref.xml @@ -0,0 +1,369 @@ + + + + 20230928T193704-b7b93635dec6c91a17a6a3c2950078839a3e3195 + 20230928193704 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 09 + 2023 + + + 8 + + 89 + + + + BlackBIRDS: Black-Box Inference foR Differentiable +Simulators + + + + Arnau + Quera-Bofarull + https://orcid.org/0000-0001-5055-9863 + + + Joel + Dyer + https://orcid.org/0000-0002-8304-8450 + + + Anisoara + Calinescu + https://orcid.org/0000-0003-2082-734X + + + J. Doyne + Farmer + https://orcid.org/0000-0001-7871-073X + + + Michael + Wooldridge + https://orcid.org/0000-0002-9329-8410 + + + + 09 + 28 + 2023 + + + 5776 + + + 10.21105/joss.05776 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.8377044 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5776 + + + + 10.21105/joss.05776 + https://joss.theoj.org/papers/10.21105/joss.05776 + + + https://joss.theoj.org/papers/10.21105/joss.05776.pdf + + + + + + PyTorch: An imperative style, +high-performance deep learning library + Paszke + Proceedings of the 33rd international +conference on neural information processing systems + 2019 + Paszke, A., Gross, S., Massa, F., +Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, +N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, +M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., … Chintala, S. +(2019). PyTorch: An imperative style, high-performance deep learning +library. In Proceedings of the 33rd international conference on neural +information processing systems. Curran Associates +Inc. + + + Normflows: A PyTorch package for normalizing +flows + Stimper + Journal of Open Source +Software + 86 + 8 + 10.21105/joss.05361 + 2023 + Stimper, V., Liu, D., Campbell, A., +Berenz, V., Ryll, L., Schölkopf, B., & Hernández-Lobato, J. M. +(2023). Normflows: A PyTorch package for normalizing flows. Journal of +Open Source Software, 8(86), 5361. +https://doi.org/10.21105/joss.05361 + + + Differentiable agent-based +epidemiology + Chopra + Proceedings of the 2023 international +conference on autonomous agents and multiagent systems + 978-1-4503-9432-1 + 2023 + Chopra, A., Rodríguez, A., +Subramanian, J., Quera-Bofarull, A., Krishnamurthy, B., Prakash, B. A., +& Raskar, R. (2023). 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ICLR Workshop on AI +for Agent-Based Modelling. + + + Some challenges of calibrating differentiable +agent-based models + Quera-Bofarull + ICML Differentiable Almost Everything +Workshop + 2023 + Quera-Bofarull, A., Dyer, J., +Calinescu, A., & Wooldridge, M. (2023). Some challenges of +calibrating differentiable agent-based models. ICML Differentiable +Almost Everything Workshop. + + + Sbi: A toolkit for simulation-based +inference + Tejero-Cantero + Journal of Open Source +Software + 52 + 5 + 10.21105/joss.02505 + 2020 + Tejero-Cantero, A., Boelts, J., +Deistler, M., Lueckmann, J.-M., Durkan, C., Gonçalves, P. J., Greenberg, +D. S., & Macke, J. H. (2020). Sbi: A toolkit for simulation-based +inference. Journal of Open Source Software, 5(52), 2505. +https://doi.org/10.21105/joss.02505 + + + Black-it: A ready-to-use and easy-to-extend +calibration kit for agent-based models + Benedetti + Journal of Open Source +Software + 79 + 7 + 10.21105/joss.04622 + 2022 + Benedetti, M., Catapano, G., Sclavis, +F. D., Favorito, M., Glielmo, A., Magnanimi, D., & Muci, A. (2022). +Black-it: A ready-to-use and easy-to-extend calibration kit for +agent-based models. Journal of Open Source Software, 7(79), 4622. +https://doi.org/10.21105/joss.04622 + + + PyVBMC: Efficient Bayesian inference in +python + Huggins + Journal of Open Source +Software + 86 + 8 + 10.21105/joss.05428 + 2023 + Huggins, B., Li, C., Tobaben, M., +Aarnos, M. J., & Acerbi, L. (2023). PyVBMC: Efficient Bayesian +inference in python. Journal of Open Source Software, 8(86), 5428. +https://doi.org/10.21105/joss.05428 + + + ABCpy: A high-performance computing +perspective to approximate Bayesian computation + Dutta + Journal of Statistical +Software + 7 + 100 + 10.18637/jss.v100.i07 + 2021 + Dutta, R., Schoengens, M., +Pacchiardi, L., Ummadisingu, A., Widmer, N., Künzli, P., Onnela, J.-P., +& Mira, A. (2021). ABCpy: A high-performance computing perspective +to approximate Bayesian computation. Journal of Statistical Software, +100(7), 1–38. +https://doi.org/10.18637/jss.v100.i07 + + + pyABC: Efficient and robust easy-to-use +approximate Bayesian computation + Schälte + Journal of Open Source +Software + 74 + 7 + 10.21105/joss.04304 + 2022 + Schälte, Y., Klinger, E., Alamoudi, +E., & Hasenauer, J. (2022). pyABC: Efficient and robust easy-to-use +approximate Bayesian computation. Journal of Open Source Software, +7(74), 4304. https://doi.org/10.21105/joss.04304 + + + BayesFlow: Amortized Bayesian Workflows With +Neural Networks + Radev + Journal of Open Source +Software + 89 + 8 + 10.21105/joss.05702 + 2023 + Radev, S. T., Schmitt, M., +Schumacher, L., Elsemüller, L., Pratz, V., Schälte, Y., Köthe, U., & +Bürkner, P.-C. (2023). BayesFlow: Amortized Bayesian Workflows With +Neural Networks. Journal of Open Source Software, 8(89), 5702. +https://doi.org/10.21105/joss.05702 + + + + + +