3 accepted papers
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 giv…
A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data. Such structure can be expressed in the pattern of interactions between explanatory latent variables captured through a probabilistic graphical model. Al…
The firing of neural populations is coordinated across cells, in time, and across experimental conditions or repeated experimental trials; and so a full understanding of the computational significance of neural responses must be based on a separation of these different contributions to structured ac…