GPflow: A Gaussian process library using TensorFlow

The variational Gaussian approximation revisited

Manfred Opper and Cédric Archambeau · 2009

Earlier work this paper cites.

Variational learning of inducing variables in sparse Gaussian processes

Michalis K. Titsias · 2009

Earlier work this paper cites.

MCMC using Hamiltonian dynamics

Radford M. Neal · 2010

Earlier work this paper cites.

Gaussian Processes for machine learning (GPML) toolbox

C. E. Rasmussen and H. Nickisch · 2010

Earlier work this paper cites.

GPy: A Gaussian process framework in Python

GPy · 2012

Cited alongside, same era.

Gaussian processes for Big Data

James Hensman, Nicolo Fusi, and Neil D Lawrence · 2013

Cited alongside, same era.

GPstuff: Bayesian modeling with Gaussian processes

Original

Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, and Aki Vehtari · 2013

Cited alongside, same era.

Gaussian process models with parallelization and GPU acceleration

Zhenwen Dai, Andreas Damianou, James Hensman, and Neil Lawrence · 2014

Cited alongside, same era.

MCMC for variationally Sparse Gaussian Processes

James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, and Zoubin Ghahramani

Cited in the paper.

Scalable Variational Gaussian Process Classification

James Hensman, Alexander G. de G. Matthews, and Zoubin Ghahramani

Cited in the paper.

TensorFlow: Large-scale machine learning on heterogeneous systems, 2015

Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015

Later among the works it cites.

On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes

Alexander G. de G. Matthews, James Hensman, Richard E. Turner, and Zoubin Ghahramani · 2016

Closest in time.

Differentiation of the Cholesky decomposition

I. Murray · 2016

Closest in time.