Unsupervised discovery of nonlinear structure using contrastive backpropagation.
We describe a way of modeling high-dimensional data vectors by using an unsupervised, nonlinear, multilayer neural network in which the activity of each neuron-like unit makes an additive contribution to a global energy score that indicates how surprised the network is by the data vector. The connec...
| Hauptverfasser: | , , , |
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| Format: | Journal article |
| Sprache: | English |
| Veröffentlicht: |
2006
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