Cargando…
THE SUCCESS OF DEEP GENERATIVE MODELS
<!--HTML--><p>Deep generative models allow us to learn hidden representations of data and generate new examples. There are two major families of models that are exploited in current applications: Generative Adversarial Networks (GANs), and Variational Auto-Encoders (VAE). The principle o...
Autor principal: | Tomczak, Jakub |
---|---|
Lenguaje: | eng |
Publicado: |
2018
|
Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2628487 |
Ejemplares similares
-
Approximate Inference and Deep Generative Models
por: Rezende, Danilo J.
Publicado: (2018) -
Model/dataset compression for optimizing the efficiency of deep networks
por: Osadchy, Margarita
Publicado: (2023) -
Hyperparameter Optimization for Deep Learning Models Using High Performance Computing
por: Wulff, Eric
Publicado: (2023) -
Automatic Differentiation and Deep Learning
por: Chintala, Soumith
Publicado: (2018) -
Scalable Deep Learning with Apache MXNet
por: Rauschmayr, Nathalie
Publicado: (2019)