Algorithme wake-sleep
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Wake-sleep algorithm
The wake-sleep algorithm[1] is an unsupervised learning algorithm for a stochastic multilayer[clarification needed] neural network. The algorithm adjusts the parameters so as to produce a good density estimator.[2] There are two learning phases, the “wake” phase and the “sleep” phase, which are performed alternately.[3] It was first designed as a model for brain functioning using variational Bayesian learning. After that, the algorithm was adapted to machine learning. It can be viewed as a way to train a Helmholtz Machine[4][5]. It can also be used in Deep Belief Networks (DBN).
Contributeurs: Claire Gorjux, Imane Meziani, wiki