Search Results for author: Anna Vaughan

Found 7 papers, 5 papers with code

Unsupervised Change Detection of Extreme Events Using ML On-Board

1 code implementation4 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.

Change Detection Management +2

Convolutional conditional neural processes for local climate downscaling

1 code implementation20 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).

Gaussian Processes

Sim2Real for Environmental Neural Processes

1 code implementation30 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.

Autoregressive Conditional Neural Processes

1 code implementation25 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.

Meta-Learning

Practical Conditional Neural Process Via Tractable Dependent Predictions

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.

Decision Making Meta-Learning

Practical Conditional Neural Processes Via Tractable Dependent Predictions

no code implementations16 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.

Decision Making Meta-Learning

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