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Alexander G. De G. Matthews
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9
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
10 March 2020 by
David Pfau
and
others
Chemical Physics
,
Machine Learning
Functional Regularisation for Continual Learning
11 June 2019 by
Michalis Titsias
and
others
Machine Learning
Gaussian Process Behaviour in Wide Deep Neural Networks
16 August 2018 by
Alexander G. De G. Matthews
and
others
Machine Learning
Variational Bayesian dropout: pitfalls and fixes
5 July 2018 by
Jiri Hron
and
others
Machine Learning
Variational Gaussian Dropout is not Bayesian
8 November 2017 by
Jiri Hron
and
others
at
University of Cambridge
Machine Learning
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
8 July 2017 by
John Bradshaw
and
others
Machine Learning
GPflow: A Gaussian process library using TensorFlow
27 October 2016 by
Alexander G. De G. Matthews
and
others
Machine Learning
On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes
4 December 2015 by
Alexander G. De G. Matthews
and
others
Machine Learning
MCMC for Variationally Sparse Gaussian Processes
12 June 2015 by
James Hensman
and
others
Machine Learning
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Machine Learning
Chemical Physics
Computational Physics