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CITATION.cff
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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: 'DeepSensor: A Python package for modelling environmental data with convolutional neural processes'
message: >-
If you use DeepSensor in your research, please cite it
using the information below.
type: software
authors:
- given-names: Tom Robin
family-names: Andersson
email: [email protected]
affiliation: Google DeepMind
orcid: 'https://orcid.org/0000-0002-1556-9932'
repository-code: 'https://github.com/alan-turing-institute/deepsensor'
abstract: >-
DeepSensor is a Python package for modelling environmental
data with convolutional neural processes (ConvNPs).
ConvNPs are versatile deep learning models capable of
ingesting multiple environmental data streams of varying
modalities and resolutions, handling missing data, and
predicting at arbitrary target locations with uncertainty.
DeepSensor allows users to tackle a diverse array of
environmental prediction tasks, including downscaling
(super-resolution), sensor placement, gap-filling, and
forecasting. The library includes a user-friendly
pandas/xarray interface, automatic unnormalisation of
model predictions, active learning functionality,
integration with both PyTorch and TensorFlow, and model
customisation. DeepSensor streamlines and simplifies the
environmental data modelling pipeline, enabling
researchers and practitioners to harness the potential of
ConvNPs for complex environmental prediction challenges.
keywords:
- machine learning
- environmental science
- neural processes
- active learning
license: MIT
version: 0.4.2
date-released: '2024-10-20'