[R] Pointers to (deep) latent variable models that admit analytical approximations

Hi everyone.

I am aware that there is a plethora of deep generative models out there (e.g. variational autoencoders (VAE), GANs) that can model high-dimensional data as the images of latent variables under a non-linear mapping (typically neural network).

In more traditional methods such as probabilistic PCA, the latent variables can be marginalised analytically. In Bayesian PCA (BPCA), we can additionally integrate out the linear mapping, from the latent space to the observation space, by adopting the variational lower bound …

autoencoders data deep generative models gans generative generative models images linear machinelearning mapping network neural network non-linear vae variables variational autoencoders