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195105 (2016)
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Phys. Soc. Jpn. 86(6), 063001 (2017)
109. Kashiwa, K., Kikuchi, Y., Tomiya, A.: Phase transition encoded in neural network. PTEP
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111. Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. arXiv preprint
arXiv:1505.00387 (2015)
112. Weinan, E.: A proposal on machine learning via dynamical systems. Commun. Math. Stat.
5(1), 1–11 (2017)
113. Abarbanel, H.D.I., Rozdeba, P.J., Shirman, S.: Machine learning; deepest learning as
statistical data assimilation problems. Neural Comput. 30(Early Access), 1–31 (2018)
114. Gomez, A.N., Ren, M., Urtasun, R., Grosse, R.B.: The reversible residual network: back
propagation without storing activations. In: Advances in Neural Information Processing
Systems, pp. 2214–2224 (2017)
115. Haber, E., Ruthotto, L.: Stable architectures for deep neural networks. Inverse Prob. 34(1),
014004 (2017)
116. Chang, B., Meng, L., Haber, E., Ruthotto, L., Begert, D., Holtham, E.: Reversible architectures for arbitrarily deep residual neural networks. arXiv preprint arXiv:1709.03698 (2017)
117. Chen, T.Q., Rubanova, Y., Bettencourt, J., Duvenaud, D.: Neural ordinary differential
equations. arXiv preprint arXiv:1806.07366 (2018)
118. Hashimoto, K., Sugishita, S., Tanaka, A., Tomiya, A.: Deep learning and the AdS/CFT
correspondence. Phys. Rev. D 98(4), 046019 (2018)
119. Lin, H.W., Tegmark, M., Rolnick, D.: Why does deep and cheap learning work so well? J.
Stat. Phys. 168(6), 1223–1247 (2017)
120. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational
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