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Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing
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Show, attend and tell: neural image caption generation with visual attention. In: International
Conference on Machine Learning, pp. 2048–2057 (2015)
51. Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial
networks. arXiv preprint arXiv:1805.08318 (2018)
52. Olah, C., Carter, S.: Attention and augmented recurrent neural networks. Distill (2016)
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54. Wolfram, S.: Theory and Applications of Cellular Automata: Including Selected Papers 1983–
1986. World scientific (1986)
55. Cook, M.: Universality in elementary cellular automata. Complex Syst. 15(1), 1–40 (2004)
56. Cook, M.: A concrete view of rule 110 computation. arXiv preprint arXiv:0906.3248 (2009)
57. Langton, C.G.: Computation at the edge of chaos: phase transitions and emergent computation. Physica D: Nonlinear Phenomena 42(1–3), 12–37 (1990)
58. Lyons, L.: Discovering the significance of 5 sigma. arXiv preprint arXiv:1310.1284 (2013)
59. Jona-Lasinio, G.: Renormalization group and probability theory. Phys. Rep. 352(4–6), 439–
458 (2001)
60. Ferrenberg, A.M., Landau, D.P., Wong, Y.J.: Monte carlo simulations: hidden errors from
“good” random number generators. Phys. Rev. Lett. 69(23), 3382 (1992)
61. Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed
uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. (TOMACS)
8(1), 3–30 (1998)
62. Savvidy, G.K., Ter-Arutyunyan-Savvidy, N.G.: On the monte carlo simulation of physical
systems. J. Comput. Phys. 97(2), 566–572 (1991)
63. Marsaglia, G., et al.: Xorshift RNGS. J. Stat. Softw. 8(14), 1–6 (2003)
64. Box, G.E.P.: A note on the generation of random normal deviates. Ann. Math. Stat. 29, 610–
611 (1958)
65. Häggström, O.: Finite Markov Chains and Algorithmic Applications, vol. 52. Cambridge
University Press (2002)
66. Tierney, L.: Markov chains for exploring posterior distributions. Ann. Stat. 22(4), 1701–1728
(1994)
67. Chowdhury, A., De, A.K., De Sarkar, S., Harindranath, A., Maiti, J., Mondal, S., Sarkar, A.:
Exploring autocorrelations in two-flavour Wilson Lattice QCD using DD-HMC algorithm.
Comput. Phys. Commun. 184, 1439–1445 (2013)
68. Luscher, M.: Computational strategies in Lattice QCD. In: Modern perspectives in lattice
QCD: Quantum Field Theory and High Performance Computing. Proceedings, International
School, 93rd Session, Les Houches, France, 3–28 Aug 2009, pp. 331–399 (2010)
69. Madras, N., Sokal, A.D.: The pivot algorithm: a highly efficient monte carlo method for the
self-avoiding walk. J. Stat. Phys. 50(1–2), 109–186 (1988)
70. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of
state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087–1092 (1953)
71. Creutz, M., Jacobs, L., Rebbi, C.: Monte carlo study of abelian lattice gauge theories. Phys.
Rev. D 20(8), 1915 (1979)