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Ryan Lagerquist

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2020 – today

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2024
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  - journals/corr/abs-2408-02161
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  - https://dblp.org/rec/journals/corr/abs-2408-02161
Tom Beucler, Arthur Grundner, Sara Shamekh, Peter Ukkonen, Matthew Chantry, Ryan Lagerquist:
Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications. CoRR abs/2408.02161 (2024)
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  - journals/corr/abs-2409-16507
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Ryan Lagerquist, Galina Chirokova, Robert DeMaria, Mark DeMaria, Imme Ebert-Uphoff:
Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery. CoRR abs/2409.16507 (2024)
2022
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  - journals/corr/abs-2203-11141
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Ryan Lagerquist, Imme Ebert-Uphoff:
Can we integrate spatial verification methods into neural-network loss functions for atmospheric science? CoRR abs/2203.11141 (2022)
2021
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  - journals/corr/abs-2106-09757
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Imme Ebert-Uphoff, Ryan Lagerquist, Kyle Hilburn, Yoonjin Lee, Katherine Haynes, Jason Stock, Christina Kumler, Jebb Q. Stewart:
CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences - Version 1. CoRR abs/2106.09757 (2021)

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