3 citations · 3 across the 9 of their papers we have counts for
4 papers · 1 filter
Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines
Edward Milsom, Ben Anson, Laurence Aitchison
Recent work developed convolutional deep kernel machines, achieving 92.7% test accuracy on CIFAR-10 using a ResNet-inspired architecture, which is SOTA for kernel methods. However,…
Machine learning emulation of precipitation from km-scale UK regional climate simulations using a diffusion model
Henry Addison, Elizabeth Kendon, Suman Ravuri +2
High-resolution climate simulations are valuable for understanding climate change impacts. This has motivated use of regional convection-permitting climate models (CPMs), but these…
Questionable practices in machine learning
Gavin Leech, Juan J. Vazquez, Niclas Kupper +2
Evaluating modern ML models is hard. The strong incentive for researchers and companies to report a state-of-the-art result on some metric often leads to questionable research prac…
Flexible Infinite-Width Graph Convolutional Neural Networks
Ben Anson, Edward Milsom, Laurence Aitchison
A common theoretical approach to understanding neural networks is to take an infinite-width limit, at which point the outputs become Gaussian process (GP) distributed. This is know…