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Scalable Unsupervised Learning for Deep Discrete Generative Models
Efficient, scalable training of probabilistic generative models is a highly sought after goal in the field of machine learning. One core challenge is that maximum likelihood optimization of generative parameters is computationally intractable for all but a few mostly elementary models. Variational a...
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| Lenguaje: | eng |
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University of Oldenburg
2021
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| Acceso en línea: | http://cds.cern.ch/record/2775417 |