1 citations · 1 across the 11 of their papers we have counts for
4 papers · 2 filters
Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data
Matthew Ashman, Cristiana Diaconu, Eric Langezaal +2
Many important problems require modelling large-scale spatio-temporal datasets, with one prevalent example being weather forecasting. Recently, transformer-based approaches have sh…
Translation Equivariant Transformer Neural Processes
Matthew Ashman, Cristiana Diaconu, Junhyuck Kim +5
The effectiveness of neural processes (NPs) in modelling posterior prediction maps -- the mapping from data to posterior predictive distributions -- has significantly improved sinc…
Approximately Equivariant Neural Processes
Matthew Ashman, Cristiana Diaconu, Adrian Weller +2
Equivariant deep learning architectures exploit symmetries in learning problems to improve the sample efficiency of neural-network-based models and their ability to generalise. How…
Variance-Reducing Couplings for Random Features
Isaac Reid, Stratis Markou, Krzysztof Choromanski +2
Random features (RFs) are a popular technique to scale up kernel methods in machine learning, replacing exact kernel evaluations with stochastic Monte Carlo estimates. They underpi…