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Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks., , and . CoRR, (2022)Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets., , , , and . CoRR, (2023)DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning., , and . CoRR, (2020)Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection., , , , , , and . CoRR, (2022)DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification., , , , , , , , and . Mach. Learn. Sci. Technol., 3 (3): 35007 (2022)Machine Learning and Cosmology, , , , , , , , , and 1 other author(s). (2022)cite arxiv:2203.08056Comment: Contribution to Snowmass 2021. 32 pages.DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification., , , , , , , , and . CoRR, (2021)Domain adaptation techniques for improved cross-domain study of galaxy mergers., , , , , , , and . CoRR, (2020)DIGS: deep inference of galaxy spectra with neural posterior estimation., , , , and . Mach. Learn. Sci. Technol., 3 (4): 4 (December 2022)DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection., , , , , , and . Mach. Learn. Sci. Technol., 4 (2): 25013 (June 2023)