9 citations · 16 across the 5 of their papers we have counts for
6 papers
A Step Towards Forgetting: Optimiser History and the Loss of Answer Mass
Valeria Ruscio, Seth Nabarro, Keiran Thompson
During fine-tuning, a language model can assign less probability to previously learned answers even when the current gradient acts to preserve that probability. With momentum, each…
A Distributed Gaussian Process Model for Multi-Robot Mapping
Seth Nabarro, Mark van der Wilk, Andrew J. Davison
We propose DistGP: a multi-robot learning method for collaborative learning of a global function using only local experience and computation. We utilise a sparse Gaussian process (…
Learning in Deep Factor Graphs with Gaussian Belief Propagation
Seth Nabarro, Mark van der Wilk, Andrew J Davison
We propose an approach to do learning in Gaussian factor graphs. We treat all relevant quantities (inputs, outputs, parameters, latents) as random variables in a graphical model, a…
Data augmentation in Bayesian neural networks and the cold posterior effect
Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso +3
Bayesian neural networks that incorporate data augmentation implicitly use a ``randomly perturbed log-likelihood [which] does not have a clean interpretation as a valid likelihood…
Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19
Sourabh Kulkarni, Mario Michael Krell, Seth Nabarro +1
Epidemiology models are central in understanding and controlling large scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian…
Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression
Seth Nabarro, Tristan Fletcher, John Shawe-Taylor
Accurately predicting when and where ambulance call-outs occur can reduce response times and ensure the patient receives urgent care sooner. Here we present a novel method for ambu…