Wake-sleep algorithm

The Wake-Sleep algorithm is a neural network training technique proposed by Geoffrey Hinton and his colleagues, which is specifically designed for training generative models, particularly in the context of unsupervised learning. The algorithm is particularly useful for training models that consist of multiple layers, such as deep belief networks (DBNs) or other types of hierarchical models. The Wake-Sleep algorithm consists of two main phases: the "wake" phase and the "sleep" phase.