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