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Learning Deep Sigmoid Belief Networks with Data Augmentation.

JMLR Workshop and Conference Proceedings, (2015): 268-276

Cited by: 92|Views30
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Abstract

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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