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Astronomy light curves are sparse, gappy, and heteroscedastic.
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning , ser. Springer Series in Statistics. New York, NY, USA: Springer New York Inc., 2001
2001
Earlier work this paper cites.
A. J. Drake, S. G. Djorgovski, A. Mahabal et al. , “First Results from the Catalina Real-Time Transient Survey,” ApJ , vol. 696, pp. 870–884, May 2009
2009
Earlier work this paper cites.
N. M. Law, S. R. Kulkarni, R. G. Dekany et al. , “The Palomar Transient Factory: System Overview, Performance, and First Results,” PASP , vol. 121, p. 1395, Dec. 2009
2009
Earlier work this paper cites.
J. W. Richards, D. L. Starr, N. R. Butler et al. , “On Machine-learned Classification of Variable Stars with Sparse and Noisy Time-series Data,” ApJ , vol. 733, p. 10, May 2011
2011
Earlier work this paper cites.
A. A. Mahabal, S. G. Djorgovski, A. J. Drake et al. , “Discovery, classification, and scientific exploration of transient events from the Catalina Real-time Transient Survey,” Bulletin of the Astronomical Society of India , vol. 39, pp. 387–408, Sep. 2011
2011
Earlier work this paper cites.
S. G. Djorgovski, C. Donalek, A. Mahabal et al. , “Towards an Automated Classification of Transient Events in Synoptic Sky Surveys,” ArXiv e-prints , Oct. 2011
2011
Earlier work this paper cites.
P. Dubath, L. Rimoldini, M. Süveges et al. , “Random forest automated supervised classification of Hipparcos periodic variable stars,” MNRAS , vol. 414, pp. 2602–2617, Jul. 2011
2011
Cited alongside, same era.
A. A. Mahabal, C. Donalek, S. G. Djorgovski et al. , “Real-Time Classification of Transient Events in Synoptic Sky Surveys,” in New Horizons in Time Domain Astronomy , ser. IAU Symposium, E. Griffin, R. Hanisch, and R. Seaman, Eds., vol. 285, Apr. 2012, pp. 355–357
2012
Cited alongside, same era.
K. P. Murphy, Machine Learning: A Probabilistic Perspective . The MIT Press, 2012
2012
Cited alongside, same era.
C. Donalek, A. Arun Kumar, S. G. Djorgovski et al. , “Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets,” ArXiv e-prints , Oct. 2013
2013
Cited alongside, same era.
2014
Later among the works it cites.
Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , vol. 521, no. 7553, pp. 436–444, 5 2015
2015
Later among the works it cites.
S. Dieleman, K. W. Willett, and J. Dambre, “Rotation-invariant convolutional neural networks for galaxy morphology prediction,” MNRAS , vol. 450, pp. 1441–1459, Jun. 2015
2015
Later among the works it cites.
S. G. Djorgovski, M. J. Graham, C. Donalek et al. , “Real-Time Data Mining of Massive Data Streams from Synoptic Sky Surveys,” ArXiv e-prints , Jan. 2016
2016
Later among the works it cites.
G. Cabrera-Vives, I. Reyes, F. Förster, P. A. Estévez, and J. Maureira, “Supernovae detection by using convolutional neural networks,” in 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, BC, Canada, July 24-29, 2016 , 2016, pp. 251–258
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M. J. Graham, S. G. Djorgovski, A. J. Drake et al. , “A novel variability-based method for quasar selection: evidence for a rest-frame ∼ \sim 54 d characteristic time-scale,” MNRAS , vol. 439, pp. 703–718, Mar. 2014
2014
Cited alongside, same era.
A. J. Drake, M. J. Graham, S. G. Djorgovski et al. , “The Catalina Surveys Periodic Variable Star Catalog,” ApJS , vol. 213, p. 9, Jul. 2014
2014
Cited alongside, same era.
2016
Later among the works it cites.
A. J. Drake, S. G. Djorgovski, M. Catelan et al. , “The Catalina Surveys Southern periodic variable star catalogue,” MNRAS , vol. 469, pp. 3688–3712, Aug. 2017
2017
Closest in time.