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Accelerating GAN training using highly parallel hardware on public cloud
With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and efficient R&D. This work explores different types of cloud services to train a Generative Adversarial Network (GAN) in a para...
| Autores principales: | , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1051/epjconf/202125102073 http://cds.cern.ch/record/2780109 |