9 citations · 17 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 8 cited
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Lisa Schut, Oscar Key, Rory McGrath +4
Counterfactual explanations (CEs) are a practical tool for demonstrating why machine learning classifiers make particular decisions. For CEs to be useful, it is important that they…
cs.LG2020★ 9 cited
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…
cs.LG2020
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…