Density Networks and their Application to Protein Modelling

. I define a latent variable model in the form of a neural network for which only target outputs are specified; the inputs are unspecified. Although the inputs are missing, it is still possible to train this model by placing a simple probability distribution on the unknown inputs and maximizing the probability of the data given the parameters. The model can then discover for itself a description of the data in terms of an underlying latent variable space of lower dimensionality. I present preliminary results of the application of these models to protein data. 1 Density Modelling The most popular supervised neural networks, multilayer perceptrons (MLPs), are well established as probabilistic models for regression and classification, both of which are conditional modelling tasks: the input variables are assumed given, and we condition on their values when modelling the distribution over the output variables; no model of the density over input variables is constructed. Density modelling..