Classification Of Variable Stars
4 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
Imbalance Learning for Variable Star Classification
In this work, we attempt to further improve hierarchical classification performance by applying 'data-level' approaches to directly augment the training data so that they better describe under-represented classes.
Clustering Based Feature Learning on Variable Stars
Representatives of these patterns, called exemplars, are then used to transform lightcurves of a labeled set into a new representation that can then be used to train an automatic classifier.
Streaming Classification of Variable Stars
Naively re-training from scratch is not an option in streaming settings, mainly because of the expensive pre-processing routines required to obtain a vector representation of light curves (features) each time we include new observations.
Scalable End-to-end Recurrent Neural Network for Variable star classification
Our method uses minimal data preprocessing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive datasets.