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Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference

, , , , , , and . (2022)cite arxiv:2211.05836Comment: Accepted to the Workshop on Machine Learning and the Physical Sciences at the 36th Conference on Neural Information Processing Systems 2022 (NeurIPS 2022).

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Machine Learning and Cosmology, , , , , , , , , and 1 other author(s). (2022)cite arxiv:2203.08056Comment: Contribution to Snowmass 2021. 32 pages.Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference, , , , , , and . (2022)cite arxiv:2211.05836Comment: Accepted to the Workshop on Machine Learning and the Physical Sciences at the 36th Conference on Neural Information Processing Systems 2022 (NeurIPS 2022).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)Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection., , , , , , and . CoRR, (2022)DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning., , and . CoRR, (2020)DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification., , , , , , , , and . Mach. Learn. Sci. Technol., 3 (3): 35007 (2022)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)DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains., , , , , , , , and . CoRR, (2021)