Convergence of the Wake-Sleep Algorithm

Ikeda, Shiro, Amari, Shun-ichi, Nakahara, Hiroyuki

Neural Information Processing Systems 

The WS (Wake-Sleep) algorithm is a simple learning rule for the models with hidden variables. It is shown that this algorithm can be applied to a factor analysis model which is a linear version of the Helmholtz machine. Buteven for a factor analysis model, the general convergence is not proved theoretically.

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