no code implementations • 25 Oct 2023 • Lisa Schut, Nenad Tomasev, Tom McGrath, Demis Hassabis, Ulrich Paquet, Been Kim
Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human performance across various domains.
no code implementations • 17 Aug 2023 • Tom Zahavy, Vivek Veeriah, Shaobo Hou, Kevin Waugh, Matthew Lai, Edouard Leurent, Nenad Tomasev, Lisa Schut, Demis Hassabis, Satinder Singh
In particular, we investigate whether a team of diverse AI systems can outperform a single AI in challenging tasks by generating more ideas as a group and then selecting the best ones.
1 code implementation • 29 Nov 2021 • Benedikt Höltgen, Lisa Schut, Jan M. Brauner, Yarin Gal
This is the aim of algorithms generating counterfactual explanations.
1 code implementation • 16 Mar 2021 • Lisa Schut, Oscar Key, Rory McGrath, Luca Costabello, Bogdan Sacaleanu, Medb Corcoran, Yarin Gal
Counterfactual explanations (CEs) are a practical tool for demonstrating why machine learning classifiers make particular decisions.
no code implementations • NeurIPS 2020 • Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk
This provides two major insights: first, that a measure of a model's training speed can be used to estimate its marginal likelihood.
no code implementations • 28 Sep 2020 • Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal
Reliable yet efficient evaluation of generalisation performance of a proposed architecture is crucial to the success of neural architecture search (NAS).
2 code implementations • NeurIPS 2021 • Binxin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal
Reliable yet efficient evaluation of generalisation performance of a proposed architecture is crucial to the success of neural architecture search (NAS).
no code implementations • 7 Apr 2020 • Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk
'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.
no code implementations • 25 Sep 2019 • Lisa Schut, Yarin Gal
Adversarial perturbations cause a shift in the salient features of an image, which may result in a misclassification.