346 citations · 800 across the 6 of their papers we have counts for
6 papers
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…
Fast and accurate approximate inference of transcript expression from RNA-seq data
James Hensman, Panagiotis Papastamoulis, Peter Glaus +2
Motivation: Assigning RNA-seq reads to their transcript of origin is a fundamental task in transcript expression estimation. Where ambiguities in assignments exist due to transcrip…
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…
Gaussian Process Models with Parallelization and GPU acceleration
Zhenwen Dai, Andreas Damianou, James Hensman +1
In this work, we present an extension of Gaussian process (GP) models with sophisticated parallelization and GPU acceleration. The parallelization scheme arises naturally from the…
Fast Variational Inference in the Conjugate Exponential Family
James Hensman, Magnus Rattray, Neil D. Lawrence
We present a general method for deriving collapsed variational inference algo- rithms for probabilistic models in the conjugate exponential family. Our method unifies many existing…