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Alexander G. de G. Matthews
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks

Sep 05, 2019
David Pfau, James S. Spencer, Alexander G. de G. Matthews, W. M. C. Foulkes


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Functional Regularisation for Continual Learning using Gaussian Processes

Jan 31, 2019
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh


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Gaussian Process Behaviour in Wide Deep Neural Networks

Aug 16, 2018
Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani

* This work substantially extends the work of Matthews et al. (2018) published at the International Conference on Learning Representations (ICLR) 2018 

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Variational Bayesian dropout: pitfalls and fixes

Jul 05, 2018
Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani

* Extended version of the paper accepted to ICML 2018: more details in the proofs, few minor modifications 

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Variational Gaussian Dropout is not Bayesian

Nov 08, 2017
Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani


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Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks

Jul 08, 2017
John Bradshaw, Alexander G. de G. Matthews, Zoubin Ghahramani


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GPflow: A Gaussian process library using TensorFlow

Oct 27, 2016
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman


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

Dec 04, 2015
Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani

* 9 pages. No figures 

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MCMC for Variationally Sparse Gaussian Processes

Jun 12, 2015
James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani

* 16 pages 

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