8 papers
Modelling Gas-Phase Reaction Kinetics with Guided Particle Diffusion Sampling
Andrew Millard, Zheng Zhao, Henrik Pedersen
Physics-guided sampling with diffusion priors has recently shown strong performance in solving complex systems of partial differential equations (PDEs) from sparse observations. Ho…
Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AI
Sophia N. Wilson, Andrew Millard, Guðrún Fjóla Guðmundsdóttir +2
This position paper argues that the machine learning community must move from preaching to practising data frugality for responsible artificial intelligence (AI) development. For t…
Particle-Guided Diffusion for Gas-Phase Reaction Kinetics
Andrew Millard, Henrik Pedersen
Physics-guided sampling with diffusion model priors has shown promise for solving partial differential equation (PDE) governed problems, but applications to chemically meaningful r…
Investigating Batch Inference in a Sequential Monte Carlo Framework for Neural Networks
Andrew Millard, Joshua Murphy, Peter Green +1
Bayesian inference allows us to define a posterior distribution over the weights of a generic neural network (NN). Exact posteriors are usually intractable, in which case approxima…
Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks
Andrew Millard, Joshua Murphy, Simon Maskell +1
Partial Bayesian neural networks (pBNNs) have been shown to perform competitively with fully Bayesian neural networks while only having a subset of the parameters be stochastic. Us…
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers
Andrew Millard, Joshua Murphy, Daniel Frisch +1
Markov chain Monte Carlo (MCMC) methods are a powerful but computationally expensive way of performing non-parametric Bayesian inference. MCMC proposals which utilise gradients, su…