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ZENODO
Software . 2020
License: CC BY
Data sources: Datacite
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Latent Linear Adjustment autoencoder: Pre-trained models

Authors: Heinze-Deml, Christina;

Latent Linear Adjustment autoencoder: Pre-trained models

Abstract

These are the checkpoints of the pre-trained models used for the applications of the Latent Linear Adjustment autoencoder as described in Heinze-Deml et al., 2020. More details can be found in the following GitHub repository: https://github.com/christinaheinze/latent-linear-adjustment-autoencoders. References Heinze-Deml C., Sippel, S., Pendergrass, A. G., Lehner, F., and Meinshausen, N., 2020: Latent Linear Adjustment autoencoders: A novel method for estimating and emulating dynamic precipitation at high resolution. arXiV preprint

Related Organizations
Keywords

Deep learning, Precipitation, Autoencoders, Dynamical adjustment

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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