-
[1]
A. Tanaka, A. Tomiya, and K. Hashimoto, Deep Learning and Physics, Springer (to appear in February 2021)
-
[2]
K. Hashimoto, S. Sugishita, A. Tanaka et al., Phys. Rev. D 98, 046019 (2018), arXiv:1802.08313
-
[3]
K. Hashimoto, S. Sugishita, A. Tanaka et al., Phys. Rev. D 98, 106014 (2018), arXiv:1809.10536
-
[4]
J. Zaanen, Y.-W. Sun, Y. Liu et al., Holographic Duality in Condensed Matter Physics, (Cambridge Univ. Press, 2015)
-
[5]
M. Ammon and J. Erdmenger, Gauge/gravity duality: Foundations and applications, Cambridge (University Press, Cambridge, 4, 2015)
-
[6]
Y. K. Yan, S. F. Wu, X. H. Ge et al., Phys. Rev. D 102(10), 101902 (2020), arXiv: 2004.12112 [hep-th]
-
[7]
K. Hornik, M. Stinchcombe, and H. White, Neural Networks 2, 359 (1989)
-
[8]
C.M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), (Springer-Verlag, Berlin, Heidelberg, 2006)
-
[9]
D.P. Kingma and J. Ba, Adam: A method for stochastic optimization, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Y. Bengio and Y. LeCun, eds., 2015, http://arxiv.org/abs/1412.6980
-
[10]
K. Hashimoto, H.-Y. Hu and Y.-Z. You, Neural ODE and Holographic QCD, 2006.00712
-
[11]
S.P.N.a. Ernst Hairer, Gerhard Wanner, Solving Ordinary Differential Equations I: Nonstiff Problems, Springer Series in Computational Mathematics 8, Springer-Verlag Berlin Heidelberg, 2 ed. (1993)