Alexander G. de G. Matthews | DeepAI

AI Chat

Featured Co-authors

Yee Whye Teh
122 publications
Zoubin Ghahramani
90 publications
Razvan Pascanu
87 publications
Richard E. Turner
75 publications
Mark van der Wilk
44 publications
Maurizio Filippone
42 publications
Mark Rowland
40 publications
Keisuke Fujii
36 publications
James Hensman
34 publications
Michalis K. Titsias
31 publications
Jiri Hron
15 publications

∙ 09/05/2019

Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks

Given access to accurate solutions of the many-electron Schrödinger equa...

0 David Pfau, et al. ∙

∙ 01/31/2019

Functional Regularisation for Continual Learning using Gaussian Processes

We introduce a novel approach for supervised continual learning based on...

14 Michalis K. Titsias, et al. ∙

∙ 07/05/2018

Variational Bayesian dropout: pitfalls and fixes

Dropout, a stochastic regularisation technique for training of neural ne...

0 Jiri Hron, et al. ∙

∙ 04/30/2018

Gaussian Process Behaviour in Wide Deep Neural Networks

Whilst deep neural networks have shown great empirical success, there is...

0 Alexander G. de G. Matthews, et al. ∙

∙ 11/08/2017

Variational Gaussian Dropout is not Bayesian

Gaussian multiplicative noise is commonly used as a stochastic regularis...

0 Jiri Hron, et al. ∙

∙ 07/08/2017

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

Deep neural networks (DNNs) have excellent representative power and are ...

0 John Bradshaw, et al. ∙

∙ 10/27/2016

GPflow: A Gaussian process library using TensorFlow

GPflow is a Gaussian process library that uses TensorFlow for its core c...

0 Alexander G. de G. Matthews, et al. ∙

∙ 06/12/2015

MCMC for Variationally Sparse Gaussian Processes

Gaussian process (GP) models form a core part of probabilistic machine l...

0 James Hensman, et al. ∙

∙ 04/27/2015

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

The variational framework for learning inducing variables (Titsias, 2009...

0 Alexander G. de G. Matthews, et al. ∙

∙ 05/16/2014

Classification using log Gaussian Cox processes

McCullagh and Yang (2006) suggest a family of classification algorithms ...

0 Alexander G. de G. Matthews, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world