1 citations · 1 across the 4 of their papers we have counts for
5 papers
Why Heavy-Tailed Weight Spectra Predict Model Quality
Joseph Wilson, Chris van der Heide, Liam Hodgkinson +3
A solid understanding of the predictive success of deep neural networks (DNNs) remains elusive. For example, while many data-dependent metrics exist that seek to predict model qual…
Is the Last Layer Sufficient for Uncertainty Quantification?
Joseph Wilson, Chris van der Heide, Liam Hodgkinson +1
Epistemic uncertainty quantification (UQ) for deep neural networks (DNNs) is a requirement for safe adoption of AI in mission-critical settings. Several leading methods for UQ line…
Gradient-enhanced deep Gaussian processes for multifidelity modelling
Viv Bone, Chris van der Heide, Kieran Mackle +3
Multifidelity models integrate data from multiple sources to produce a single approximator for the underlying process. Dense low-fidelity samples are used to reduce interpolation e…
A PAC-Bayesian Perspective on the Interpolating Information Criterion
Liam Hodgkinson, Chris van der Heide, Robert Salomone +2
Deep learning is renowned for its theory-practice gap, whereby principled theory typically fails to provide much beneficial guidance for implementation in practice. This has been h…
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
Liam Hodgkinson, Chris van der Heide, Fred Roosta +1
Despite their importance for assessing reliability of predictions, uncertainty quantification (UQ) measures for machine learning models have only recently begun to be rigorously ch…