121 citations · 205 across the 3 of their papers we have counts for
7 papers
Annealed Flow Transport Monte Carlo
Michael Arbel, Alexander G. D. G. Matthews, Arnaud Doucet
Annealed Importance Sampling (AIS) and its Sequential Monte Carlo (SMC) extensions are state-of-the-art methods for estimating normalizing constants of probability distributions. W…
Functional Regularisation for Continual Learning with Gaussian Processes
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews +2
We introduce a framework for Continual Learning (CL) based on Bayesian inference over the function space rather than the parameters of a deep neural network. This method, referred…
Variational Bayesian dropout: pitfalls and fixes
Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani
Dropout, a stochastic regularisation technique for training of neural networks, has recently been reinterpreted as a specific type of approximate inference algorithm for Bayesian n…
Gaussian Process Behaviour in Wide Deep Neural Networks
Alexander G. de G. Matthews, Mark Rowland, Jiri Hron +2
Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties. In this paper, we study the relation…
Variational Gaussian Dropout is not Bayesian
Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani
Gaussian multiplicative noise is commonly used as a stochastic regularisation technique in training of deterministic neural networks. A recent paper reinterpreted the technique as…
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
John Bradshaw, Alexander G. de G. Matthews, Zoubin Ghahramani
Deep neural networks (DNNs) have excellent representative power and are state of the art classifiers on many tasks. However, they often do not capture their own uncertainties well…