Machine learning determination of dynamical parameters: The Ising model case

Markov random fields and their applications , volume 1

Ross Kindermann and Laurie Snell · 1980

Earlier work this paper cites.

Massively parallel architectures for ai: NETL, Thistle, and Boltzmann Machines

Scott E. Fahlman, Geoffrey E. Hinton, and Terrence J. Sejnowski · 1983

Earlier work this paper cites.

Optimization by simulated annealing

Scott Kirkpatrick, C. D. Gelatt, and Mario P. Vecchi · 1983

Earlier work this paper cites.

Boltzmann machines: Constraint satisfaction networks that learn

Geoffrey E. Hinton, Terrence J. Sejnowski, and David H. Ackley · 1984

Earlier work this paper cites.

A learning algorithm for boltzmann machines

David H. Ackley, Geoffrey E. Hinton, and Terrence J. Sejnowski · 1985

Earlier work this paper cites.

Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1, information processing in dynamical systems: foundations of harmony theory

P. Smolensky · 1986

Earlier work this paper cites.

Nonequilibrium equality for free energy differences

C. Jarzynski · 1997

Earlier work this paper cites.

Annealed importance sampling

Radford M. Neal · 2001

Cited alongside, same era.

Training products of experts by minimizing contrastive divergence

Geoffrey E. Hinton · 2002

Cited alongside, same era.

Information Theory, Inference & Learning Algorithms

David J. C. MacKay · 2002

Cited alongside, same era.

Learning and evaluating boltzmann machines

Ruslan Salakhutdinov · 2008

Cited alongside, same era.

A practical guide to training restricted boltzmann machines, 2010

Geoffrey Hinton · 2010

Cited alongside, same era.

An introduction to restricted boltzmann machines

Asja Fischer and Christian Igel · 2012

Cited alongside, same era.

An exact mapping between the variational renormalization group and deep learning

Deep Learning

Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016

Later among the works it cites.

Learning thermodynamics with boltzmann machines

Giacomo Torlai and Roger G. Melko · 2016

Later among the works it cites.

Deep learning the ising model near criticality

Alan Morningstar and Roger G. Melko · 2017

Later among the works it cites.

https://github.com/s9w/magneto

Magneto: 2D Ising model in C++ · 2018

Closest in time.

Scale-invariant feature extraction of neural network and renormalization group flow

Satoshi Iso, Shotaro Shiba, and Sumito Yokoo · 2018

Closest in time.

A high-bias, low-variance introduction to machine learning for physicists

Original

Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G. R. Day, Clint Richardson, Charles K. Fisher, and David J. Schwab · 2018