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	Why the hidden configurations should be treated as data when learning the next layer of weights
		• After learning the first layer of weights: • If we freeze the generative weights that define the likelihood term and the recognition weights that define the distribution over hidden configurations, we get: • Maximizing the RHS is equivalent to maximizing the log prob of “data”     that occurs with probability