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Ce Yu, Kun Li, Yanxia Zhang, Jian Xiao, Chenzhou Cui, Yihan Tao, Shanjiang Tang, Chao Sun and Chongke Bi WIREs Data Mining and Knowledge Discovery 11(5) (2021) https://doi.org/10.1002/widm.1425
LAMOST Time-Domain survey: first results of four K2 plates
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Milky Way archaeology using RR Lyrae and type II Cepheids
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Imbalance learning for variable star classification
Vanessa McBride, Arrykrishna Mootoovaloo, Benjamin Stappers, Robert Lyon and Zafiirah Hosenie Monthly Notices of the Royal Astronomical Society 493(4) 6050 (2020) https://doi.org/10.1093/mnras/staa642
Rare Object Search From Low-S/N Stellar Spectra in SDSS
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Image-based Classification of Variable Stars: First Results from Optical Gravitational Lensing Experiment Data
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Near-infrared Search for Fundamental-mode RR Lyrae Stars toward the Inner Bulge by Deep Learning
Unsupervised machine learning for transient discovery in deeper, wider, faster light curves
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The ASAS-SN catalogue of variable stars – V. Variables in the Southern hemisphere
T Jayasinghe, K Z Stanek, C S Kochanek, et al. Monthly Notices of the Royal Astronomical Society 491(1) 13 (2020) https://doi.org/10.1093/mnras/stz2711
Scalable end-to-end recurrent neural network for variable star classification
F Nikzat, C Aguirre, P Protopapas, et al. Monthly Notices of the Royal Astronomical Society 493(2) 2981 (2020) https://doi.org/10.1093/mnras/staa350
On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
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Gary Segal, David Parkinson, Ray P Norris and Jesse Swan Publications of the Astronomical Society of the Pacific 131(1004) 108007 (2019) https://doi.org/10.1088/1538-3873/ab0068
Machine Learning for the Zwicky Transient Facility
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Comparing Multiclass, Binary, and Hierarchical Machine Learning Classification schemes for variable stars
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Deep Neural Network Classifier for Variable Stars with Novelty Detection Capability
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Paul R. McWhirter, Abir Hussain, Dhiya Al-Jumeily, Iain A. Steele and Marley M.B.R. Vellasco Expert Systems with Applications 131 94 (2019) https://doi.org/10.1016/j.eswa.2019.04.035
Variability search in M 31 using principal component analysis and the Hubble Source Catalogue
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From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
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Detecting Variability in Massive Astronomical Time-series Data. III. Variable Candidates in the SuperWASP DR1 Found by Multiple Clustering Algorithms and a Consensus Clustering Method
The ASAS-SN catalogue of variable stars I: The Serendipitous Survey
T Jayasinghe, C S Kochanek, K Z Stanek, et al. Monthly Notices of the Royal Astronomical Society 477(3) 3145 (2018) https://doi.org/10.1093/mnras/sty838
ASAS-SN Discovery of 4880 Bright RR Lyrae Variable Stars
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A recurrent neural network for classification of unevenly sampled variable stars
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E. Chapellier, D. Mékarnia, L. Abe, T. Guillot, K. Agabi, J.-P. Rivet, F.-X. Schmider, N. Crouzet and E. Aristidi The Astrophysical Journal Supplement Series 226(2) 21 (2016) https://doi.org/10.3847/0067-0049/226/2/21