Scale-invariant Feature Extraction of Neural Network and Renormalization Group Flow

S. Amari, “Theory of adaptive pattern classifiers”, IEEE Trans. Elect. Comput. EC- 16

1967

Earlier work this paper cites.

K. G. Wilson, “Renormalization group and critical phenomena. 1. Renormalization group and the Kadanoff scaling picture,” Phys. Rev. B 4

1971

Earlier work this paper cites.

G. E. Hinton, “Training products of experts by minimizing contrastive divergence,” Neural computation 14

2002

Earlier work this paper cites.

M. Á. Carreira-Perpiñán and G. E. Hinton, “On contrastive divergence learning,” Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS, 2005) pp. 33-40

2005

Earlier work this paper cites.

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313

2006

Earlier work this paper cites.

R. Salakhutdinov, A. Mnih and G. Hinton, “Restricted Boltzmann machines for collaborative filtering,” Proceedings of the 24th international conference on Machine learning (ACM, 2007) pp. 791-798

2007

Earlier work this paper cites.

H. Larochelle and Y. Bengio, “Classification using discriminative restricted Boltzmann machines,” Proceedings of the 25th international conference on Machine learning (ACM, 2008) pp. 536-543

2008

Earlier work this paper cites.

Y. Bengio and O. Delalleau, “Justifying and generalizing contrastive divergence.” Neural Computation 21

2009

Cited alongside, same era.

G. Hinton, “A practical guide to training restricted Boltzmann machines,” Momentum 9

2010

Cited alongside, same era.

G. E. Hinton, “A principal guide to training restricted Boltzmann machines,” Neural networks: Tricks of the Trade

2012

Cited alongside, same era.

S. Saremi and T. J. Sejnowski, “Hierarchical model of natural images and the origin of scale invariance,” Proceedings of the National Academy of Sciences 110

2013

Cited alongside, same era.

C. H. Lee, Y. Yamada, T. Kumamoto and H. Matsueda, “Exact Mapping from Singular Value Spectrum of Fractal Images to Entanglement Spectrum of One-Dimensional Quantum Systems,” J. Phys. Soc. Jap. 84

Original

2015

Cited alongside, same era.

G. Torlai and R. G. Melko, “Learning thermodynamics with Boltzmann machines,” Phys. Rev. B 94

Original

2016

Later among the works it cites.

H. W. Lin, M. Tegmark and D. Rolnick, “Why Does Deep and Cheap Learning Work So Well?,” J. Stat. Phys. 168

Original

2017

Later among the works it cites.

D. S. P. Salazar, “Nonequilibrium Thermodynamics of Restricted Boltzmann Machines,” Phys. Rev. E 96

Original

2017

Later among the works it cites.

A. Tanaka and A. Tomiya, “Detection of phase transition via convolutional neural network,” J. Phys. Soc. Jap. 86

Original

2017

Later among the works it cites.

S. J. Wetzel, “Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders,” Phys. Rev. E 96