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ICML
2009
IEEE

Using fast weights to improve persistent contrastive divergence

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Using fast weights to improve persistent contrastive divergence
The most commonly used learning algorithm for restricted Boltzmann machines is contrastive divergence which starts a Markov chain at a data point and runs the chain for only a few iterations to get a cheap, low variance estimate of the sufficient statistics under the model. Tieleman (2008) showed that better learning can be achieved by estimating the model’s statistics using a small set of persistent ”fantasy particles” that are not reinitialized to data points after each weight update. With sufficiently small weight updates, the fantasy particles represent the equilibrium distribution accurately but to explain why the method works with much larger weight updates it is necessary to consider the interaction between the weight updates and the Markov chain. We show that the weight updates force the Markov chain to mix fast, and using this insight we develop an even faster mixing chain that uses an auxiliary set of ”fast weights” to implement a temporary overlay on the energy la...
Tijmen Tieleman, Geoffrey E. Hinton
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Where ICML
Authors Tijmen Tieleman, Geoffrey E. Hinton
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