Deep belief network

Schematic overview of a deep belief net. Arrows represent directed connections in the graphical model that the net represents.

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In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a type of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.^[1]

When trained on a set of examples in an unsupervised way, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors on inputs.^[1] After this learning step, a DBN can be further trained in a supervised way to perform classification.^[2]

DBNs can be viewed as a composition of simple, unsupervised networks such as restricted Boltzmann machines (RBMs)^[1] or autoencoders,^[3] where each sub-network's hidden layer serves as the visible layer for the next. This also leads to a fast, layer-by-layer unsupervised training procedure, where contrastive divergence is applied to each sub-network in turn, starting from the "lowest" pair of layers (the lowest visible layer being a training set).

The observation, due to Yee-Whye Teh, Geoffrey Hinton's student,^[2] that DBNs can be trained greedily, one layer at a time, led to one of the first effective deep learning algorithms.^[4]^:6

Training algorithm

The training algorithm for DBNs proceeds as follows.^[2] Let $X$ be a matrix of inputs, regarded as a set of feature vectors.

Train a restricted Boltzmann machine on $X$ to obtain its weight matrix, $W$ . Use this as the weight matrix between the lower two layers of the network.
Transform $X$ by the RBM to produce new data $X'$ , either by sampling or by computing the mean activation of the hidden units.
Repeat this procedure with $X$ ← $X'$ for the next pair of layers, until the top two layers of the network are reached.
Fine-tune all the parameters of this deep architecture with respect to a proxy for the DBN log- likelihood, or with respect to a supervised training criterion (after adding extra learning machinery to convert the learned representation into supervised predictions, e.g. a linear classifier).

References

1 2 3 Hinton, G. (2009). "Deep belief networks". Scholarpedia 4 (5): 5947. doi:10.4249/scholarpedia.5947.
1 2 3 Hinton, G. E.; Osindero, S.; Teh, Y. W. (2006). "A Fast Learning Algorithm for Deep Belief Nets" (PDF). Neural Computation 18 (7): 1527–1554. doi:10.1162/neco.2006.18.7.1527. PMID 16764513.
↑ Yoshua Bengio; Pascal Lamblin; Dan Popovici; Hugh Larochelle (2007). Greedy Layer-Wise Training of Deep Networks (PDF). NIPS.
↑ Bengio, Y. (2009). "Learning Deep Architectures for AI" (PDF). Foundations and Trends in Machine Learning 2. doi:10.1561/2200000006.

External links

"Deep Belief Networks". Deep Learning Tutorials.
"Deep Belief Network Example". Deeplearning4j Tutorials.

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Deep belief network

Training algorithm

See also

References

External links