346 citations · 800 across the 7 of their papers we have counts for
4 papers · 1 filter
Sparse Gaussian Processes with Spherical Harmonic Features Revisited
Stefanos Eleftheriadis, Dominic Richards, James Hensman
We revisit the Gaussian process model with spherical harmonic features and study connections between the associated RKHS, its eigenstructure and deep models. Based on this, we intr…
GPflow: A Gaussian process library using TensorFlow
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson +5
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational…
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…
Scalable Variational Gaussian Process Classification
James Hensman, Alex Matthews, Zoubin Ghahramani
Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperformi…