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Evaluating POWER Architecture for Distributed Training of Generative Adversarial Networks
The increased availability of High-Performance Computing resources can enable data scientists to deploy and evaluate data-driven approaches, notably in the field of deep learning, at a rapid pace. As deep neural networks become more complex and are ingesting increasingly larger datasets, it becomes...
| Autores principales: | , , , |
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| Lenguaje: | eng |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1007/978-3-030-34356-9_32 http://cds.cern.ch/record/2799446 |