5 papers
Retrieval-augmented reasoning with lean language models
Ryan Sze-Yin Chan, Federico Nanni, Tomas Lazauskas +6
This technical report details a novel approach to combining reasoning and retrieval augmented generation (RAG) within a single, lean language model architecture. While existing RAG…
Efficient Deconvolution in Populational Inverse Problems
Arnaud Vadeboncoeur, Mark Girolami, Andrew M. Stuart
This work is focussed on the inversion task of inferring the distribution over parameters of interest leading to multiple sets of observations. The potential to solve such distribu…
Probabilistic Super-Resolution for High-Fidelity Physical System Simulations with Uncertainty Quantification
Pengyu Zhang, Connor Duffin, Alex Glyn-Davies +2
Super-resolution (SR) is a promising tool for generating high-fidelity simulations of physical systems from low-resolution data, enabling fast and accurate predictions in engineeri…
Statistical Finite Elements via Interacting Particle Langevin Dynamics
Alex Glyn-Davies, Connor Duffin, Ieva Kazlauskaite +2
In this paper, we develop a class of interacting particle Langevin algorithms to solve inverse problems for partial differential equations (PDEs). In particular, we leverage the st…
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling
Alex Glyn-Davies, Arnaud Vadeboncoeur, O. Deniz Akyildiz +2
Variational inference (VI) is a computationally efficient and scalable methodology for approximate Bayesian inference. It strikes a balance between accuracy of uncertainty quantifi…