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Massive scale deep learning for detecting extreme climate events

, , , , , и . Climate Informatics, (2017)

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Sasha Dehghani

Werner Ames

Sandra Ames

Katrin Ames

Lisa Ames

 

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DI-MMAP - a scalable memory-map runtime for out-of-core data-intensive applications., , , , и . Clust. Comput., 18 (1): 15-28 (2015)Design and Optimization of a Metagenomics Analysis Workflow for NVRAM., , , , и . IPDPS Workshops, стр. 556-565. IEEE Computer Society, (2014)DI-MMAP: A High Performance Memory-Map Runtime for Data-Intensive Applications., , , и . SC Companion, стр. 731-735. IEEE Computer Society, (2012)Scalable metagenomic taxonomy classification using a reference genome database., , , , , и . Bioinform., 29 (18): 2253-2260 (2013)Resolution Reconstruction of Climate Data with Pixel Recursive Model., , , , , и . ICDM Workshops, стр. 313-321. IEEE Computer Society, (2017)Massive scale deep learning for detecting extreme climate events, , , , , и . Climate Informatics, (2017)QMDS: a file system metadata management service supporting a graph data model-based query language., , и . Int. J. Parallel Emergent Distributed Syst., 28 (2): 159-183 (2013)Searching and navigating petabyte-scale file systems based on facets., , , , , и . PDSW, стр. 21-25. ACM Press, (2007)On the Role of NVRAM in Data-intensive Architectures: An Evaluation., , , и . IPDPS, стр. 703-714. IEEE Computer Society, (2012)QMDS: A File System Metadata Management Service Supporting a Graph Data Model-Based Query Language., , и . NAS, стр. 268-277. IEEE Computer Society, (2011)