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Real-time detection of anomalies in large-scale transient surveys., , , , and . CoRR, (2021)Rubin-Euclid Derived Data Products: Initial Recommendations, , , , , , , , , and 110 other author(s). (2022)cite arxiv:2201.03862Comment: Report of the Rubin-Euclid Derived Data Products Working Group, 78 pages, 11 figures.RAPID: Early Classification of Explosive Transients using Deep Learning., , , , and . CoRR, (2019)A machine learning classifier for microlensing in wide-field surveys., , , and . Astron. Comput., (2019)The Simulated Catalogue of Optical Transients and Correlated Hosts (SCOTCH), , , , , , , , , and 2 other author(s). (2022)cite arxiv:2206.02815Comment: 27 pages, 19 figures, to be submitted to MNRAS. Comments welcome.Sub-percent Photometry: Faint DA White Dwarf Spectophotometric Standards for Astrophysical Observatories, , , , , , , , , and 11 other author(s). (2018)cite arxiv:1811.12534Comment: 45 pages, 23 figures, 8 tables, submitted to ApJS.Preliminary Report on Mantis Shrimp: a Multi-Survey Computer Vision Photometric Redshift Model., , and . CoRR, (2024)Machine Learning and Cosmology, , , , , , , , , and 1 other author(s). (2022)cite arxiv:2203.08056Comment: Contribution to Snowmass 2021. 32 pages.Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data., , , , and . CoRR, (2021)Machine Learning and Cosmology., , , , , , , , , and 1 other author(s). CoRR, (2022)