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It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks, very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher-order statistical structure of adoi:10.1111/cogs.12049 pmid:23800216 fatcat:gsqrcryoazeivp2vjr44n6mv2q