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Unsupervised classification of simulated magnetospheric regions
We present an unsupervised classification method for magnetospheric regions, that could constitute the first-step of a multi-step method for the automatic identification of magnetospheric processes of interest.
A Framework using Contrastive Learning for Classification with Noisy Labels
We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels.
Visualizing and Interpreting Unsupervised Solar Wind Classifications
The manual and semi-automatic classification of millions of hours of solar wind data from multiple missions can be replaced by automatic algorithms that can discover, in mountains of multi-dimensional data, the real differences in the solar wind properties.
Space Physics Solar and Stellar Astrophysics
