Abstract
Deep leaning has been developing very fast in recent years due to big data, high-performance computing and the breakthrough of neural network training techniques. It has been particularly successful in computer vision, machine translation, speech recognition and natural language processing. Modern astronomy concerns a big data challenge owning to high-resolution, high-precision and high-cadence telescopes. The big data presents a great challenge to data processing, statistical analysis and scientific discovery. Therefore, it is highly demanded to develop artificial intelligent algorithms to process big data aromatically, further discover complex relationship and mine knowledge hidden in massive data. As the best representative of artificial intelligence, a bunch of classical models have been developed for processing single image, video, speech and natural language. Among of them, convolutional neural network has been verified most efficient for processing image. To process time series input, like video, recurrent neural network, e.g., long short-term memory (LSTM), was developed, which was widely known for forecasting the future, e.g., event occurrence, physical parameter prediction. An overview of artificial intelligence, deep learning and astronomical big data is presented in this chapter, as the background of this book.
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References
Zhang J, Liu T, Tao D. 2018 An Information-Theoretic View for Deep Learning.
Novak R, Bahri Y, Abolafia DA, Pennington J, Sohl-Dickstein J. 2018 Sensitivity and Generalization in Neural Networks: an Empirical Study.
Canziani A, Paszke A, Culurciello E. 2017 An Analysis of Deep Neural Network Models for Practical Applications.
Neyshabur B, Tomioka R, Srebro N. 2014 In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning.
SDO Obseratory. [EB/OL]. https://sdo.gsfc.nasa.gov/ Accessed Oct. 31, 2020.
SOHO Obseratory. [EB/OL]. https://sohowww.nascom.nasa.gov/ Accessed Oct. 31, 2020.
Stereo Obseratory. [EB/OL]. https://stereo.gsfc.nasa.gov/ Accessed Oct. 31, 2020.
SKA Obseratory. [EB/OL]. https://www.skatelescope.org/the-ska-project/ Accessed Oct. 31, 2020.
ARECIBO Obseratory. [EB/OL]. https://www.almaobservatory.org/en/home/ Accessed Oct. 31, 2020.
ALMA Obseratory. [EB/OL]. https://www.almaobservatory.org/en/home/ Accessed Oct. 31, 2020.
Mingantu Solar Radioheliograph. [EB/OL]. http://klsa.bao.ac.cn/ Accessed Oct. 31, 2020.
FAST Telescope. [EB/OL]. https://fast.bao.ac.cn/ Accessed Oct. 31, 2020.
Nobeyama Radio Telescope. [EB/OL]. https://www.nro.nao.ac.jp/ Accessed Oct. 31, 2020.
Siberian Solar Radio Telescope. [EB/OL]. http://en.iszf.irk.ru/ Accessed Oct. 31, 2020.
Bengio Y et al.. 2009 Learning deep architectures for AI. Foundations and trends® in Machine Learning 2, 1–127.
LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD. 1989 Backpropagation applied to handwritten zip code recognition. Neural computation 1, 541–551.
Simonyan K, Zisserman A. 2015 Very Deep Convolutional Networks for Large-Scale Image Recognition. In International Conference on Learning Representations.
Vincent P, Larochelle H, Bengio Y, Manzagol PA. 2008 Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning pp. 1096–1103. ACM.
Hinton GE, Salakhutdinov RR. 2006 Reducing the Dimensionality of Data with Neural Networks. Science 313, 504–507.
Hinton GE, Osindero S, Teh YW. 2006 A fast learning algorithm for deep belief nets. Neural computation 18, 1527–1554.
Hinton GE. 2012 A practical guide to training restricted Boltzmann machines. In Neural networks: Tricks of the trade pp. 599–619. Springer.
Sohn K, Jung DY, Lee H, Hero AO. 2011 Efficient learning of sparse, distributed, convolutional feature representations for object recognition. In Computer Vision (ICCV), 2011 IEEE International Conference on pp. 2643–2650. IEEE.
Mohamed Ar, Dahl GE, Hinton G et al.. 2012 Acoustic modeling using deep belief networks. IEEE Trans. Audio, Speech & Language Processing 20, 14–22.
Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. 2011 Natural language processing (almost) from scratch. Journal of Machine Learning Research 12, 2493–2537.
Krizhevsky A, Sutskever I, Hinton G. 2012 ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems 25.
Szegedy C, Wei Liu, Yangqing Jia, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. 2015 Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1–9.
He K, Zhang X, Ren S, Sun J. 2016 Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition pp. 770–778.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. 2017 Densely Connected Convolutional Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 2261–2269.
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L. 2015 ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115, 211–252.
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y. 2014 Generative Adversarial Networks. ArXiv abs/1406.2661.
Hochreiter S, Schmidhuber J. 1997 Long short-term memory. Neural computation 9, 1735–1780.
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Xu, L., Yan, Y., Huang, X. (2022). Introduction. In: Deep Learning in Solar Astronomy. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-19-2746-1_1
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