Fetching the paper…
Reading the bibliography…
Spin-glasses are universal models that can capture complex behavior of many-body systems at the interface of statistical physics and computer science including discrete optimization, inference in graphical models, and automated reasoning.
D. Sherrington and S. Kirkpatrick, Solvable model of a spin-glass
1975
Earlier work this paper cites.
G. Parisi, The order parameter for spin glasses: a function on the interval 0-1
1980
Earlier work this paper cites.
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Optimization by simulated annealing
1983
Earlier work this paper cites.
M. Mézard, G. Parisi, N. Sourlas, G. Toulouse, and M. Virasoro, Nature of the spin-glass phase
1984
Earlier work this paper cites.
T. Castellani and A. Cavagna, Spin-glass theory for pedestrians
2005
Earlier work this paper cites.
D. J. Earl and M. W. Deem, Parallel tempering: Theory, applications, and new perspectives
2005
Earlier work this paper cites.
Y. LeCun, S. Chopra, R. Hadsell, F. J. Huang, and et al., A tutorial on energy-based learning
2006
Earlier work this paper cites.
Oxford University Press, Inc., New York, NY, USA, 2009
M. Mezard and A. Montanari, Information, Physics, and Computation · 2009
Earlier work this paper cites.
H. Nishimori and G. Ortiz, Phase transitions and critical phenomena
2010
Earlier work this paper cites.
H. G. Katzgraber, Introduction to monte carlo methods
2010
Earlier work this paper cites.
Oxford University Press, Inc., 2011
C. Moore and S. Mertens, The Nature of Computation · 2011
Earlier work this paper cites.
Y. Zhang, Z. Ghahramani, A. J. Storkey, and C. A. Sutton, Continuous relaxations for discrete hamiltonian monte carlo
2012
Earlier work this paper cites.
MIT press, 2012
K. P. Murphy, Machine learning: a probabilistic perspective · 2012
Earlier work this paper cites.
Princeton University Press, 2013
D. L. Stein and C. M. Newman, Spin Glasses and Complexity · 2013
Cited alongside, same era.
M. Castellana, The renormalization group for disordered systems
2013
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets
2014
Cited alongside, same era.
2014
Cited alongside, same era.
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization
2014
Cited alongside, same era.
2017
Later among the works it cites.
C. Doersch and A. Zisserman, Multi-task self-supervised visual learning
2017
Later among the works it cites.
2017
Later among the works it cites.
H. Shen, J. Liu, and L. Fu, Self-learning monte carlo with deep neural networks
2018
Later among the works it cites.
S.-H. Li and L. Wang, Neural network renormalization group
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
L. Dinh, J. Sohl-Dickstein, and S. Bengio, Density estimation using real nvp
2016
Cited alongside, same era.
S. Boixo et al., Computational multiqubit tunnelling in programmable quantum annealers
2016
Cited alongside, same era.
J. Carrasquilla and R. G. Melko, Machine learning phases of matter
2017
Cited alongside, same era.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Quantum machine learning
2017
Cited alongside, same era.
G. Carleo and M. Troyer, Solving the quantum many-body problem with artificial neural networks
2017
Cited alongside, same era.
L. Huang and L. Wang, Accelerated monte carlo simulations with restricted boltzmann machines
2017
Cited alongside, same era.
G. S. Hartnett, E. Parker, and E. Geist, Replica symmetry breaking in bipartite spin glasses and neural networks
2018
Later among the works it cites.
T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, Neural ordinary differential equations
2018
Later among the works it cites.
T. Albash and D. A. Lidar, Adiabatic quantum computation
2018
Later among the works it cites.
M. Mohseni, J. Strumpfer, and M. M. Rams, Engineering non-equilibrium quantum phase transitions via causally gapped Hamiltonians
2018
Later among the works it cites.
2019
Later among the works it cites.
D. Tran, K. Vafa, K. Agrawal, L. Dinh, and B. Poole, Discrete flows: Invertible generative models of discrete data
2019
Later among the works it cites.
M. S. Albergo, G. Kanwar, and P. E. Shanahan, Flow-based generative models for markov chain monte carlo in lattice field theory
2019
Later among the works it cites.