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On Scalable Deep Learning and Parallelizing Gradient Descent
Speeding up gradient based methods has been a subject of interest over the past years with many practical applications, especially with respect to Deep Learning. Despite the fact that many optimizations have been done on a hardware level, the convergence rate of very large models remains problematic...
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| Lenguaje: | eng |
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2017
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| Acceso en línea: | http://cds.cern.ch/record/2276711 |