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Deep Probabilistic Models (Wake/Sleep)

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Many of the recent breakthroughs in training deep neural networks have used supervised learning. However, these methods do not exploit the vast amount of unlabeled training data that is becoming available. A solution to this problem is to use unsupervised learning, but this is difficult in deep neural networks because inferring hidden variables in deep layers is usually intractable.

The wake-sleep algorithm is an unsupervised learning algorithm that learns to infer hidden variables using two modes of operation: a wake phase, in which a network is driven by training data, and a sleep phase in which the network generates fantasy data. We will describe the wake-sleep algorithm for two neural network architectures: The Helmholtz Machine and Deep Belief Nets. These are two of the most influential neural network architectures for unsupervised learning in neural networks.

This talk is part of the Machine Learning Reading Group @ CUED series.

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