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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
Deep learning, Precipitation, Autoencoders, Dynamical adjustment
Deep learning, Precipitation, Autoencoders, Dynamical adjustment
| selected citations These citations are derived from selected sources. 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). | 0 | |
| 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. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 1 |

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Downloads provided by UsageCounts