1k citations · 5.3k across the 132 of their papers we have counts for
138 papers
Steering Diffusion Models to Rare Events with Sequential Monte Carlo
Aavash Subedi, Tim Reichelt, Christopher Williams +3
Diffusion models are increasingly used as surrogates for expensive simulators in weather prediction, molecular dynamics, and materials design. In these models, computing the probab…
Continual Learning via Sequential Function-Space Variational Inference
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng +2
Sequential Bayesian inference over predictive functions is a natural framework for continual learning from streams of data. However, applying it to neural networks has proved chall…
Tractable Function-Space Variational Inference in Bayesian Neural Networks
Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh +1
Reliable predictive uncertainty estimation plays an important role in enabling the deployment of neural networks to safety-critical settings. A popular approach for estimating the…
SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning
Ning Miao, Yee Whye Teh, Tom Rainforth
The recent progress in large language models (LLMs), especially the invention of chain-of-thought prompting, has made it possible to automatically answer questions by stepwise reas…
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
Leo Klarner, Tim G. J. Rudner, Michael Reutlinger +4
Accelerating the discovery of novel and more effective therapeutics is an important pharmaceutical problem in which deep learning is playing an increasingly significant role. Howev…
Geometric Neural Diffusion Processes
Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson +3
Denoising diffusion models have proven to be a flexible and effective paradigm for generative modelling. Their recent extension to infinite dimensional Euclidean spaces has allowed…