625 citations · 2.7k across the 69 of their papers we have counts for
3 papers · 2 filters
Nested Variational Compression in Deep Gaussian Processes
James Hensman, Neil D. Lawrence
Deep Gaussian processes provide a flexible approach to probabilistic modelling of data using either supervised or unsupervised learning. For tractable inference approximations to t…
Metrics for Probabilistic Geometries
Alessandra Tosi, Søren Hauberg, Alfredo Vellido +1
We investigate the geometrical structure of probabilistic generative dimensionality reduction models using the tools of Riemannian geometry. We explicitly define a distribution ove…
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied. However, the current approach…