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Learning Deep Sigmoid Belief Networks with Data Augmentation.
JMLR Workshop and Conference Proceedings, (2015): 268-276
Deep directed generative models are developed. The multi-layered model is designed by stacking sigmoid belief networks, with sparsity-encouraging priors placed on the model parameters. Learning and inference of layer-wise model parameters are implemented in a Bayesian setting. By exploring the idea of data augmentation and introducing aux...More
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