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science_cases:gmap_science_cases:mounds [2022/02/17 11:01] – adminscience_cases:gmap_science_cases:mounds [2022/02/22 12:06] (current) – admin
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 A simulator of such likes can be used for controlled generation. Another advantage of **latent space learning** is that it can offer benefits in downstream tasks, which is an added advantage for storage and efficient searching. We have developed this simulator and we plan to disseminate the method as a publication in the coming months. A simulator of such likes can be used for controlled generation. Another advantage of **latent space learning** is that it can offer benefits in downstream tasks, which is an added advantage for storage and efficient searching. We have developed this simulator and we plan to disseminate the method as a publication in the coming months.
  
-Results of this science case were presented at the {{:wiki:egu2021-julka_etal.pdf|EGU21}}. The ML pipeline is available on our GitHub repository.+Results of this science case were presented at the {{:wiki:egu2021-julka_etal.pdf|EGU21}}. The [[https://github.com/epn-ml/GMAP-mound-classification-|ML pipeline]] is available on our GitHub repository.
  
 **References:** **References:**
science_cases/gmap_science_cases/mounds.1645092096.txt.gz · Last modified: 2022/02/17 11:01 by admin