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| data_mining:neural_network:belief_nets [2017/04/30 10:02] – [Discriminative fine-tuning for DBNs] phreazer | data_mining:neural_network:belief_nets [2017/07/30 16:05] (current) – [Structure] phreazer | ||
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| ==== Model real-valued data with RBMS ==== | ==== Model real-valued data with RBMS ==== | ||
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| + | Mean-field logistic units cannot represent precise inetermediate values (e.g. pixel intensity in image). | ||
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| + | Model pixels as Gaussian variables. Alternating Gibbs sampling, with lower learning rate. | ||
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| + | Parabolic containment function. (keep visible unit close to b_i). | ||
| + | Energy-gradient. | ||
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| + | Stepped sigmoid units. Many copies of a stochastic binary unist. All copies have same weiths and bias, b, but they have different fixed offsets to the bias (b-0.5, b-1.5, ...). | ||
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| + | ==== Structure ==== | ||
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| + | Autoencoder, | ||