The hidden layer is subject to drop-out and elastic net regularisation (with equal l 1 and l 2 shares).
a mixture density network ("MDN": Bishop (1994)) is a latent variable model where the conditional probability of p(y|x) is modelled as a mixture of K Gaussians where the mixing coefficients π and the parameters of Gaussian distributions µ and Σ are computed as a function of input using a neural network: In the bivariate case of latitude/longitude, the number of parameters of each Gaussian is 6 (π , which are learnt in the output layer of a regular neural network as a function of input x.