Built on

M. A. Ruderman and C. Kittel, Phys. Rev. 96
1954
Earlier work this paper cites.
T. Kasuya, Progr. Theoret. Phys. 16
1956
Earlier work this paper cites.
K. Yosida, Phys. Rev. 106
1957
Earlier work this paper cites.
J. A. Mydosh, Spin Glasses: An Experimental Introduction (Taylor & Francis,, London, 1993)
1993
Earlier work this paper cites.
N. Furukawa, Y. Motome, and H. Nakata, Computer Physics Communications 142
2000
Earlier work this paper cites.
J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, “Scaling laws for neural language models,” (2020), arXiv:2001.08361 [cs.LG]
Original
2001
Earlier work this paper cites.
J. Alonso, L. Fernández, F. Guinea, V. Laliena, and V. Martín-Mayor, Nuclear Physics B 596
2001
Earlier work this paper cites.
N. Furukawa and Y. Motome, Journal of the Physical Society of Japan 73
2004
Earlier work this paper cites.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” (2020), arXiv:2005.14165 [cs.CL]
Original
2005
Earlier work this paper cites.
G. Alvarez, C. Şen, N. Furukawa, Y. Motome, and E. Dagotto, Computer Physics Communications 168
2005
Earlier work this paper cites.
G. Alvarez, P. K. V. V. Nukala, and E. D’Azevedo, Journal of Statistical Mechanics: Theory and Experiment 2007
2007
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” (2021), arXiv:2010.11929 [cs.CV]
Original
2010
Earlier work this paper cites.
K. Barros and Y. Kato, Phys. Rev. B 88
2013
Earlier work this paper cites.
J. Bezanson, A. Edelman, S. Karpinski, and V. B. Shah, “Julia: A fresh approach to numerical computing,” (2015), arXiv:1411.1607 [cs.MS]
Original
2015
Earlier work this paper cites.
D. Silver, A. Huang, C. Maddison, et al. , Nature 529
2016
Earlier work this paper cites.
T. S. Cohen and M. Welling, “Group equivariant convolutional networks,” (2016), arXiv:1602.07576 [cs.LG]
Original
2016
Earlier work this paper cites.

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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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2023

Self-learning Monte Carlo with equivariant Transformer

Nagai, Yuki, Tomiya, Akio

Understand

Machine learning and deep learning have revolutionized computational physics, particularly the simulation of complex systems.

Equivariance is essential for simulating physical systems because it imposes a strong inductive bias on the probability distribution described by a machine learning model.
However, imposing symmetry on the model can sometimes lead to poor acceptance rates in self-learning Monte Carlo (SLMC).
Here, we introduce a symmetry equivariant attention mechanism for SLMC, which can be systematically improved.