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RG inspired Machine Learning for lattice field theory · Around

2017

RG inspired Machine Learning for lattice field theory

Foreman, S., Giedt, J., Meurice, Y. et al.

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Machine learning has been a fast growing field of research in several areas dealing with large datasets.

We report recent attempts to use Renormalization Group (RG) ideas in the context of machine learning.
We examine coarse graining procedures for perceptron models designed to identify the digits of the MNIST data.
We discuss the correspondence between principal components analysis (PCA) and RG flows across the transition for worm configurations of the 2D Ising model.

Built on

F. Rosenblatt, The perceptron , psychological Review, Vol. 65, No. 6 (1958)
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Y. Liu, Y. Meurice, M.P. Qin, J. Unmuth-Yockey, T. Xiang, Z.Y. Xie, J.F. Yu, H. Zou, Phys. Rev. D88
2013
Earlier work this paper cites.

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https://arxiv.org/abs/1202.2745v1
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M. Luscher, Stochastic locality and master-field simulations of very large lattices , in Proceedings, 35th International Symposium on Lattice Field Theory (Lattice2017) : Granada, Spain , to appear in EPJ Web Conf., 1707.09758
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