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Robust simulation-based inference in cosmology with Bayesian neural networks.

, , , , , , , and . Mach. Learn. Sci. Technol., 4 (1): 01 (March 2023)

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Search for CII Emission on Cosmological Scales at Redshift Z~2.6, , , , and . (2017)cite arxiv:1707.06172Comment: 14 pages, 9 figures, to be submitted to MNRAS.Multiple Physics Pretraining for Physical Surrogate Models., , , , , , , , , and 4 other author(s). CoRR, (2023)Learnable wavelet neural networks for cosmological inference, , and . (2023)cite arxiv:2307.14362Comment: Accepted at ICML 2022 Workshop on Machine Learning for Astrophysics, Baltimore, Maryland, USA, 2022.Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes., , , , , , , and . CoRR, (2021)Luminosity function of OII emission-line galaxies in the MassiveBlack-II simulation, , , , , , and . (2015)cite arxiv:1508.05106Comment: 12 pages, 13 figures, 3 tables, accepted for publication in MNRAS.Baryon Acoustic Oscillations in the Ly-\alpha\ forest of BOSS quasars, , , , , , , , , and 53 other author(s). (2012)cite arxiv:1211.2616Comment: submitted to Astronomy and Astrophysics.The Sloan Digital Sky Survey quasar catalog: ninth data release, , , , , , , , , and 66 other author(s). (2012)cite arxiv:1210.5166Comment: Accepted for publication in A&A, Catalog available online at http://www.sdss3.org/dr9/algorithms/qso_catalog.php.Analysis of Cosmic Microwave Background with Deep Learning., , and . ICLR (Workshop), OpenReview.net, (2018)Rediscovering orbital mechanics with machine learning., , , , and . Mach. Learn. Sci. Technol., 4 (4): 45002 (December 2023)AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models., , , , , , , , , and 4 other author(s). CoRR, (2023)