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Ibrahim Ahmad Almosallam - Publications

Affiliations: 
University of Oxford, Oxford, United Kingdom 
Area:
Machine Learning
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5 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2020 Hatfield P, Rose S, Scott R, Almosallam I, Roberts S, Jarvis M. Using Sparse Gaussian Processes for Predicting Robust Inertial Confinement Fusion Implosion Yields Ieee Transactions On Plasma Science. 48: 14-21. DOI: 10.1109/Tps.2019.2944416  0.474
2020 Schmidt SJ, Malz AI, Soo JYH, Almosallam IA, Brescia M, Cavuoti S, Cohen-Tanugi J, Connolly AJ, DeRose J, Freeman PE, Graham ML, Iyer KG, Jarvis MJ, Kalmbach JB, Kovacs E, et al. Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST) Monthly Notices of the Royal Astronomical Society. DOI: 10.1093/Mnras/Staa2799  0.493
2018 Gomes Z, Jarvis MJ, Almosallam IA, Roberts SJ. Improving photometric redshift estimation using GPz: size information, post processing and improved photometry Monthly Notices of the Royal Astronomical Society. 475: 331-342. DOI: 10.1093/Mnras/Stx3187  0.538
2016 Almosallam IA, Jarvis MJ, Roberts SJ. GPZ: Non-stationary sparse Gaussian processes for heteroscedastic uncertainty estimation in photometric redshifts Monthly Notices of the Royal Astronomical Society. 462: 726-739. DOI: 10.1093/Mnras/Stw1618  0.58
2016 Almosallam IA, Lindsay SN, Jarvis MJ, Roberts SJ. A sparse Gaussian process framework for photometric redshift estimation Monthly Notices of the Royal Astronomical Society. 455: 2387-2401. DOI: 10.1093/Mnras/Stv2425  0.538
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