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

Zafiirah13/Imbalance-Learning-for-Variable-Star-Classification-using-Machine-Learning 27 Feb 2020

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

cmackenziek/tsfl 29 Feb 2016

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

lezorich/variational-gmm 4 Dec 2019

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

iebecker/Scalable_RNN 3 Feb 2020

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.