Unsupervised representational learning with recognition-parametrised probabilistic models. (arXiv:2209.05661v1 [cs.LG])

We introduce a new approach to probabilistic unsupervised learning based on
the recognition-parametrised model (RPM): a normalised semi-parametric
hypothesis class for joint distributions over observed and latent variables.
Under the key assumption that observations are conditionally independent given
the latents, RPMs directly encode the "recognition" process, parametrising both
the prior distribution on the latents and their conditional distributions given
observations. This recognition model is paired with non-parametric descriptions
of the marginal distribution of each observed variable. Thus, the focus is …

arxiv unsupervised