Found: 9
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Performance–energy trade-offs of deep learning convolution algorithms on ARM processors.
- Published in:
- Journal of Supercomputing, 2023, v. 79, n. 9, p. 9819, doi. 10.1007/s11227-023-05050-4
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- Article
BestOf: an online implementation selector for the training and inference of deep neural networks.
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- Journal of Supercomputing, 2022, v. 78, n. 16, p. 17543, doi. 10.1007/s11227-022-04577-2
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- Article
PyDTNN: A user-friendly and extensible framework for distributed deep learning.
- Published in:
- Journal of Supercomputing, 2021, v. 77, n. 9, p. 9971, doi. 10.1007/s11227-021-03673-z
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- Article
HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs.
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- Sensors (14248220), 2018, v. 18, n. 1, p. 115, doi. 10.3390/s18010115
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- Article
Statistical Approaches to Computer-Assisted Translation.
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- Computational Linguistics, 2009, v. 35, n. 1, p. 3, doi. 10.1162/coli.2008.07-055-R2-06-29
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- Article
Toward the parallelization of GSL.
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- Journal of Supercomputing, 2009, v. 48, n. 1, p. 88, doi. 10.1007/s11227-008-0207-z
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- Article
Exploiting the capabilities of modern GPUs for dense matrix computations.
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- Concurrency & Computation: Practice & Experience, 2009, v. 21, n. 18, p. 2457, doi. 10.1002/cpe.1472
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- Article
Deploying cloud‐native experimental platforms for zero‐touch management 5G and beyond networks.
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- IET Networks (Wiley-Blackwell), 2023, v. 12, n. 6, p. 305, doi. 10.1049/ntw2.12095
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- Article
Using machine learning to model the training scalability of convolutional neural networks on clusters of GPUs.
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- Computing, 2023, v. 105, n. 5, p. 915, doi. 10.1007/s00607-021-00997-9
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- Article