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Dark Energy Survey Year 3 Results: Cosmology from Cosmic Shear and Robustness to Data Calibration, , , , , , , , , and 138 other author(s). (2021)cite arxiv:2105.13543Comment: 42 pages, 19 figures.Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and galaxy-galaxy lensing using the MagLim lens sample, , , , , , , , , and 134 other author(s). (2021)cite arxiv:2105.13546Comment: 25 pages, 16 figures, to be submitted to PRD. See https://www.darkenergysurvey.org/des-year-3-cosmology-results-papers/ for the full DES Y3 cosmology release.Dark Energy Survey Year 3 Results: Cosmology from Cosmic Shear and Robustness to Modeling Uncertainty, , , , , , , , , and 144 other author(s). (2021)cite arxiv:2105.13544Comment: 32+9 pages, 13+4 figures, see https://www.darkenergysurvey.org/des-year-3-cosmology-results-papers/ for the full DES Y3 cosmology release.Dark Energy Survey Year 3 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing, , , , , , , , , and 160 other author(s). (2021)cite arxiv:2105.13549Comment: See https://www.darkenergysurvey.org/des-year-3-cosmology-results-papers/ for the full DES Y3 3x2pt cosmology release.Dark Energy Survey Year 3 Results: Constraints on extensions to $Łambda$CDM with weak lensing and galaxy clustering, , , , , , , , , and 152 other author(s). (2022)cite arxiv:2207.05766Comment: 45 pages, 25 figures, data available at https://dev.des.ncsa.illinois.edu/releases/y3a2/Y3key-extensions.Dark Energy Survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions, , , , , , , , , and 78 other author(s). (2020)cite arxiv:2011.03410.Joint analysis of DES Year 3 data and CMB lensing from SPT and Planck II: Cross-correlation measurements and cosmological constraints, , , , , , , , , and 156 other author(s). (2022)cite arxiv:2203.12440Comment: 25 pages, 19 figures, to be submitted to PRD.An affinity matrix approach for structure selection of extreme learning machines., , and . ESANN, (2015)MILKDE: A new approach for multiple instance learning based on positive instance selection and kernel density estimation., , , , , , and . Eng. Appl. Artif. Intell., (2017)Adaptive Input Selection and Evolving Neural Fuzzy Networks Modeling., , , and . Int. J. Comput. Intell. Syst., 8 (sup1): 3-14 (2015)