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Celeste: Variational inference for a generative model of astronomical images., , , , , , , and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 2095-2103. JMLR.org, (2015)Improving parallel I/O autotuning with performance modeling., , , , and . HPDC, page 253-256. ACM, (2014)Optimizing I/O Performance of HPC Applications with Autotuning., , , and . ACM Trans. Parallel Comput., 5 (4): 15:1-15:27 (2019)A Gaussian Process Model of Quasar Spectral Energy Distributions., , , , , , , and . NIPS, page 2494-2502. (2015)Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction., , , , , , , , , and . NIPS, page 1078-1086. (2017)Etalumis: bringing probabilistic programming to scientific simulators at scale., , , , , , , , , and 7 other author(s). SC, page 29:1-29:24. ACM, (2019)Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs., , , , , , , , , and 1 other author(s). DLS@SC, page 1-11. IEEE, (2019)Experiences with 100Gbps network applications., , , , , , , , and . DICT@HPDC, page 33-42. ACM, (2012)Performance analysis of emerging data analytics and HPC workloads., , , and . PDSW-DISCS@SC, page 43-48. ACM, (2017)Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks., , , , , , , and . ICMLA, page 892-897. IEEE Computer Society, (2016)