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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...
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01/31/2019
Functional Regularisation for Continual Learning using Gaussian Processes
We introduce a novel approach for supervised continual learning based on...
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07/05/2018
Variational Bayesian dropout: pitfalls and fixes
Dropout, a stochastic regularisation technique for training of neural ne...
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04/30/2018
Gaussian Process Behaviour in Wide Deep Neural Networks
Whilst deep neural networks have shown great empirical success, there is...
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11/08/2017
Variational Gaussian Dropout is not Bayesian
Gaussian multiplicative noise is commonly used as a stochastic regularis...
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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 ...
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10/27/2016
GPflow: A Gaussian process library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core c...
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06/12/2015
MCMC for Variationally Sparse Gaussian Processes
Gaussian process (GP) models form a core part of probabilistic machine l...
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04/27/2015
On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes
The variational framework for learning inducing variables (Titsias, 2009...
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05/16/2014
