308 citations · 980 across the 64 of their papers we have counts for
9 papers · 1 filter
Design of Experiments for Verifying Biomolecular Networks
Ruby Sedgwick, John Goertz, Molly Stevens +2
There is a growing trend in molecular and synthetic biology of using mechanistic (non machine learning) models to design biomolecular networks. Once designed, these networks need t…
Understanding Variational Inference in Function-Space
David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso +1
Recent work has attempted to directly approximate the `function-space' or predictive posterior distribution of Bayesian models, without approximating the posterior distribution ove…
A Bayesian Perspective on Training Speed and Model Selection
Clare Lyle, Lisa Schut, Binxin Ru +2
We take a Bayesian perspective to illustrate a connection between training speed and the marginal likelihood in linear models. This provides two major insights: first, that a measu…
Convergence of Sparse Variational Inference in Gaussian Processes Regression
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Gaussian processes are distributions over functions that are versatile and mathematically convenient priors in Bayesian modelling. However, their use is often impeded for data with…
Variational Orthogonal Features
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Sparse stochastic variational inference allows Gaussian process models to be applied to large datasets. The per iteration computational cost of inference with this method is $\math…
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro +5
In image segmentation, there is often more than one plausible solution for a given input. In medical imaging, for example, experts will often disagree about the exact location of o…
On the Benefits of Invariance in Neural Networks
Clare Lyle, Mark van der Wilk, Marta Kwiatkowska +2
Many real world data analysis problems exhibit invariant structure, and models that take advantage of this structure have shown impressive empirical performance, particularly in de…
Capsule Networks -- A Probabilistic Perspective
Lewis Smith, Lisa Schut, Yarin Gal +1
'Capsule' models try to explicitly represent the poses of objects, enforcing a linear relationship between an object's pose and that of its constituent parts. This modelling assump…
A Framework for Interdomain and Multioutput Gaussian Processes
Mark van der Wilk, Vincent Dutordoir, ST John +3
One obstacle to the use of Gaussian processes (GPs) in large-scale problems, and as a component in deep learning system, is the need for bespoke derivations and implementations for…