417 citations · 1.7k across the 36 of their papers we have counts for
5 papers · 1 filter
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 nonparametric clustering of structured time-series
James Hensman, Magnus Rattray, Neil D. Lawrence
In this publication, we combine two Bayesian non-parametric models: the Gaussian Process (GP) and the Dirichlet Process (DP). Our innovation in the GP model is to introduce a varia…