no code implementations • 30 Mar 2024 • Anna Vaughan, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, J. Scott Hosking, Richard E. Turner
Machine learning is revolutionising medium-range weather prediction.
no code implementations • 16 Nov 2023 • Lorenzo Bonito, James Requeima, Aliaksandra Shysheya, Richard E. Turner
Over the last few years, Neural Processes have become a useful modelling tool in many application areas, such as healthcare and climate sciences, in which data are scarce and prediction uncertainty estimates are indispensable.
1 code implementation • 30 Oct 2023 • Jonas Scholz, Tom R. Andersson, Anna Vaughan, James Requeima, Richard E. Turner
On held-out weather stations, Sim2Real training substantially outperforms the same model architecture trained only with reanalysis data or only with station data, showing that reanalysis data can serve as a stepping stone for learning from real observations.
1 code implementation • 18 Nov 2022 • Tom R. Andersson, Wessel P. Bruinsma, Stratis Markou, James Requeima, Alejandro Coca-Castro, Anna Vaughan, Anna-Louise Ellis, Matthew A. Lazzara, Dani Jones, J. Scott Hosking, Richard E. Turner
This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
no code implementations • 7 Jul 2022 • Ambrish Rawat, James Requeima, Wessel Bruinsma, Richard Turner
Machine unlearning refers to the task of removing a subset of training data, thereby removing its contributions to a trained model.
no code implementations • 16 Mar 2022 • Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner
Existing approaches which model output dependencies, such as Neural Processes (NPs; Garnelo et al., 2018b) or the FullConvGNP (Bruinsma et al., 2021), are either complicated to train or prohibitively expensive.
no code implementations • ICLR 2022 • Stratis Markou, James Requeima, Wessel Bruinsma, Anna Vaughan, Richard E Turner
Existing approaches which model output dependencies, such as Neural Processes (NPs; Garnelo et al., 2018) or the FullConvGNP (Bruinsma et al., 2021), are either complicated to train or prohibitively expensive.
no code implementations • 22 Aug 2021 • Stratis Markou, James Requeima, Wessel Bruinsma, Richard Turner
Conditional Neural Processes (CNP; Garnelo et al., 2018) are an attractive family of meta-learning models which produce well-calibrated predictions, enable fast inference at test time, and are trainable via a simple maximum likelihood procedure.
1 code implementation • pproximateinference AABI Symposium 2021 • Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong, Jonathan Gordon, Richard E. Turner
Neural Processes (NPs; Garnelo et al., 2018a, b) are a rich class of models for meta-learning that map data sets directly to predictive stochastic processes.
2 code implementations • NeurIPS 2020 • Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner
Stationary stochastic processes (SPs) are a key component of many probabilistic models, such as those for off-the-grid spatio-temporal data.
2 code implementations • ICML 2020 • John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard E. Turner
Modern meta-learning approaches for image classification rely on increasingly deep networks to achieve state-of-the-art performance, making batch normalization an essential component of meta-learning pipelines.
3 code implementations • ICLR 2020 • Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data.
1 code implementation • NeurIPS 2019 • James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner
We introduce a conditional neural process based approach to the multi-task classification setting for this purpose, and establish connections to the meta-learning and few-shot learning literature.
Ranked #6 on Few-Shot Image Classification on Meta-Dataset Rank
1 code implementation • 20 Feb 2018 • James Requeima, Will Tebbutt, Wessel Bruinsma, Richard E. Turner
Multi-output regression models must exploit dependencies between outputs to maximise predictive performance.
no code implementations • ICML 2017 • José Miguel Hernández-Lobato, James Requeima, Edward O. Pyzer-Knapp, Alán Aspuru-Guzik
These results show that PDTS is a successful solution for large-scale parallel BO.