A recurrent neural network for classification of unevenly sampled variable stars
暂无分享,去创建一个
Brett Naul | Joshua S. Bloom | Fernando Pérez | Stéfan van der Walt | J. Bloom | Brett Naul | Stéfan J. van der Walt | F. Pérez
[1] A. Lapedes,et al. Nonlinear Signal Processing Using Neural Networks , 1987 .
[2] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[3] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..
[4] C. Bailer-Jones,et al. A package for the automated classification of periodic variable stars , 2015, 1512.01611.
[5] Christopher W. Stubbs,et al. The MACHO Project LMC Variable Star Inventory.II.LMC RR Lyrae Stars- Pulsational Characteristics and Indications of a Global Youth of the LMC , 1996 .
[6] J. Scargle. Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data , 1982 .
[7] Joshua S. Bloom,et al. Data Mining and Machine-Learning in Time-Domain Discovery & Classification , 2011, 1104.3142.
[8] Gracjan Maciejewski,et al. The All Sky Automated Survey. Catalog of Variable Stars. I. 0 h - 6 hQuarter of the Southern Hemisphere , 2002 .
[9] Stephen T. Ridgway,et al. THE VARIABLE SKY OF DEEP SYNOPTIC SURVEYS , 2014, 1409.3265.
[10] Ciro Donalek,et al. Real-time data mining of massive data streams from synoptic sky surveys , 2016, Future Gener. Comput. Syst..
[11] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[12] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[13] N. Lomb. Least-squares frequency analysis of unequally spaced data , 1976 .
[14] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[15] Pavlos Protopapas,et al. SUPERVISED DETECTION OF ANOMALOUS LIGHT CURVES IN MASSIVE ASTRONOMICAL CATALOGS , 2014, ArXiv.
[16] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[17] A. Schwarzenberg-Czerny,et al. Accuracy of period determination , 1991 .
[18] Brett Naul,et al. cesium: Open-Source Platform for Time-Series Inference , 2016, SciPy.
[19] Nathaniel R. Butler,et al. CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY , 2012, 1204.4180.
[20] J. S. Stuart,et al. EXPLORING THE VARIABLE SKY WITH LINEAR. II. HALO STRUCTURE AND SUBSTRUCTURE TRACED BY RR LYRAE STARS TO 30 kpc , 2013, 1305.2160.
[21] P. Dubath,et al. Random forest automated supervised classification of Hipparcos periodic variable stars , 2011, 1101.2406.
[22] Sao,et al. A MACHINE-LEARNING METHOD TO INFER FUNDAMENTAL STELLAR PARAMETERS FROM PHOTOMETRIC LIGHT CURVES , 2014, 1411.1073.
[23] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[24] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[25] Kai Lars Polsterer,et al. Featureless Classification of Light Curves , 2015, 1504.04455.
[26] Yoshua Bengio,et al. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.
[27] Razvan Pascanu,et al. On the difficulty of training recurrent neural networks , 2012, ICML.
[28] Laurent Eyer,et al. EXPLORING THE VARIABLE SKY WITH LINEAR. III. CLASSIFICATION OF PERIODIC LIGHT CURVES , 2013, 1308.0357.
[29] G. Beylkin. On the Fast Fourier Transform of Functions with Singularities , 1995 .
[30] J. Friedman,et al. FLEXIBLE PARSIMONIOUS SMOOTHING AND ADDITIVE MODELING , 1989 .
[31] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[32] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[33] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[34] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[35] Pavlos Protopapas,et al. CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS , 2016, ArXiv.
[36] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[37] J. Curran,et al. VAST: An ASKAP Survey for Variables and Slow Transients , 2012, Publications of the Astronomical Society of Australia.
[38] Patrice Y. Simard,et al. Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..
[39] J. G. Jernigan,et al. First Results from the All-Sky Monitor on the Rossi X-Ray Timing Explorer , 1996, astro-ph/9608109.
[40] J. Richards,et al. ON MACHINE-LEARNED CLASSIFICATION OF VARIABLE STARS WITH SPARSE AND NOISY TIME-SERIES DATA , 2011, 1101.1959.
[41] Charles Elkan,et al. Learning to Diagnose with LSTM Recurrent Neural Networks , 2015, ICLR.
[42] Lukás Burget,et al. Recurrent neural network based language model , 2010, INTERSPEECH.