1 code implementation • 4 Nov 2021 • Vít Růžička, Anna Vaughan, Daniele De Martini, James Fulton, Valentina Salvatelli, Chris Bridges, Gonzalo Mateo-Garcia, Valentina Zantedeschi
In this paper, we introduce RaVAEn, a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment.
1 code implementation • 20 Jan 2021 • Anna Vaughan, Will Tebbutt, J. Scott Hosking, Richard E. Turner
A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs).
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.
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 • 25 Mar 2023 • Wessel P. Bruinsma, Stratis Markou, James Requiema, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner
Our work provides an example of how ideas from neural distribution estimation can benefit neural processes, and motivates research into the AR deployment of other neural process models.
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 • 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.