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Robust marginalization of baryonic effects for cosmological inference at the field level., , , , , , , , , and 4 other author(s). CoRR, (2021)Multifield Cosmology with Artificial Intelligence., , , , , , , , , and 3 other author(s). CoRR, (2021)Can galaxy evolution mimic cosmic reionization?, and . (2020)cite arxiv:2010.00023Comment: to be submitted to ApJ, comments are welcome.ICE: an iterative combinatorial exchange., , , , , , , , and . EC, page 249-258. ACM, (2005)The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations, , , , , , , , , and 11 other author(s). (2020)cite arxiv:2010.00619Comment: 33 pages, 18 figures, CAMELS webpage at https://www.camel-simulations.org.The CAMELS project: public data release., , , , , , , , , and 37 other author(s). CoRR, (2022)Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations., , , and . CoRR, (2023)The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence., , , , , , , , , and 18 other author(s). CoRR, (2021)Reionization with Simba: How much does astrophysics matter in modeling cosmic reionization?, , , , , , , and . (2021)cite arxiv:2109.03840Comment: 13 pages, 7figures, submitted to ApJ.Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes, , , , and . (2020)cite arxiv:2012.05820Comment: 13 pages, 8 figures, preprint submitted to MNRAS.