MacKay and Gibbs briefly report using stochastic approximation to learn a generative MLP network which they called a density network because the model defines a density of the observations . Although the results are encouraging, they do not prove the advantages of the method over SOM or GTM because the model is very simple; noise level is not estimated from the observations and the latent space had only two dimensions. The computational complexity of the method is significantly greater than in the parametric approximation of the posterior presented here, but it might be possible to combine the methods by finding initial approximation of the posterior probability with parametric approximation and then refining it with more elaborate stochastic approximation.
In , a generative MLP network was optimised by gradient based learning. The cost function was reconstruction error of the data and a point estimate was used for all the unknown variables. As argued in Sect. 2, this means that it is not possible to optimise model structure and the method is prone to overfitting.