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Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligence.
M. Born, “Zur Quantenmechanik der Stoßvorgänge,” Zeitschrift fur Physik 37
1926
Earlier work this paper cites.
S. S. Wilks, “The large-sample distribution of the likelihood ratio for testing composite hypotheses,” Ann. Math. Statist. 9
1938
Earlier work this paper cites.
S. Kullback and R. A. Leibler, “On Information and Sufficiency,” The Annals of Mathematical Statistics 22
1951
Earlier work this paper cites.
J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” (1982) p. 2554
1982
Earlier work this paper cites.
David H. Ackley, Geoffrey E. Hinton, and Terrence J. Sejnowski, “A Learning Algorithm for Boltzmann Machines,” Cognitive Science 9
1985
Earlier work this paper cites.
D.J. Amit, H. Gutfreund, and H. Sompolinsky, “Spin-glass models of neural networks,” Phys. Rev. A 32
1985
Earlier work this paper cites.
P Smolensky, “Information processing in dynamical systems: foundations of harmony theory,” in Parallel distributed processing: explorations in the microstructure of cognition, vol. 1 (MIT Press, 1986) pp. 194–281
1986
Earlier work this paper cites.
Steven R. White, “Density matrix formulation for quantum renormalization groups,” Phys. Rev. Lett. 69
1992
Earlier work this paper cites.
Hilbert J. Kappen and Francisco de Borja Rodríguez Ortiz, “Boltzmann machine learning using mean field theory and linear response correction,” in Advances in Neural Information Processing Systems 10 (MIT Press, 1998) pp. 280–286
1998
Earlier work this paper cites.
W. M. C. Foulkes, L. Mitas, R. J. Needs, and G. Rajagopal, “Quantum monte carlo simulations of solids,” Rev. Mod. Phys. 73
2001
Earlier work this paper cites.
David JC MacKay, Information theory, inference and learning algorithms (Cambridge University Press, 2003)
2003
Earlier work this paper cites.
Frank Verstraete and Juan Ignacio Cirac, “Renormalization algorithms for quantum-many body systems in two and higher dimensions,” arXiv preprint cond-mat/0407066 (2004)
2004
Earlier work this paper cites.
Aapo Hyvärinen, “Consistency of pseudolikelihood estimation of fully visible boltzmann machines,” Neural Computation 18
2006
Earlier work this paper cites.
M B Hastings, “An area law for one-dimensional quantum systems,” Journal of Statistical Mechanics: Theory and Experiment 2007
2007
Earlier work this paper cites.
D Perez-Garcia, F Verstraete, M M Wolf, and J I Cirac, “Matrix Product State Representations,” Quantum Info. Comput. 7
2007
Earlier work this paper cites.
Norbert Schuch, Michael M. Wolf, Frank Verstraete, and Juan I. Cirac, “Computational complexity of projected entangled pair states,” Phys. Rev. Lett. 98
2007
Earlier work this paper cites.
Michael Levin and Cody P. Nave, “Tensor renormalization group approach to two-dimensional classical lattice models,” Phys. Rev. Lett. 99
2007
Earlier work this paper cites.
Martin J Wainwright and Michael I Jordan, “Graphical models, exponential families, and variational inference,” Foundations and Trends® in Machine Learning 1
2008
Earlier work this paper cites.
Y. Roudi, J. Tyrcha, and J. Hertz, “Ising model for neural data: Model quality and approximate methods for extracting functional connectivity,” Phys. Rev. E 79
2009
Earlier work this paper cites.
Daphne Koller and Nir Friedman, Probabilistic Graphical Models, Principles and Techniques (The MIT Press, 2009)
2009
Earlier work this paper cites.
Z. Y. Xie, H. C. Jiang, Q. N. Chen, Z. Y. Weng, and T. Xiang, “Second renormalization of tensor-network states,” Phys. Rev. Lett. 103
2009
Earlier work this paper cites.
2010
Earlier work this paper cites.
Ming-Jie Zhao and Herbert Jaeger, “Norm-observable operator models,” Neural Computation 22
2010
Cited alongside, same era.
Kristan Temme and Frank Verstraete, “Stochastic matrix product states,” Phys. Rev. Lett. 104
2010
Cited alongside, same era.
Ulrich Schollwöck, “The density-matrix renormalization group in the age of matrix product states,” Annals of Physics 326
2011
Cited alongside, same era.
Ivan V Oseledets, “Tensor-train decomposition,” SIAM Journal on Scientific Computing 33
2011
Cited alongside, same era.
Raphael Bailly, “Quadratic weighted automata:spectral algorithm and likelihood maximization,” in Proceedings of the Asian Conference on Machine Learning , Proceedings of Machine Learning Research, Vol. 20, edited by Chun-Nan Hsu and Wee Sun Lee (PMLR, South Garden Hotels and Resorts, Taoyuan, Taiwain, 2011) pp. 147–163
2011
2015
Later among the works it cites.
Zeph Landau, Umesh Vazirani, and Thomas Vidick, “A polynomial time algorithm for the ground state of 1d gapped local hamiltonians,” Nature Physics 11
2015
Later among the works it cites.
Marylou Gabrie, Eric W Tramel, and Florent Krzakala, “Training restricted boltzmann machine via the thouless-anderson-palmer free energy,” in Advances in Neural Information Processing Systems 28 , edited by C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett (Curran Associates, Inc., 2015) pp. 640–648
2015
Later among the works it cites.
G. Evenbly and G. Vidal, “Tensor network renormalization,” Phys. Rev. Lett. 115
2015
Later among the works it cites.
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Cited alongside, same era.
Yi Zhang, Tarun Grover, and Ashvin Vishwanath, “Entanglement entropy of critical spin liquids,” Phys. Rev. Lett. 107
2011
Cited alongside, same era.
E.M. Stoudenmire and Steven R. White, “Studying two-dimensional systems with the density matrix renormalization group,” Annual Review of Condensed Matter Physics 3
2012
Cited alongside, same era.
Andrew J. Ferris and Guifre Vidal, “Perfect sampling with unitary tensor networks,” Phys. Rev. B 85
2012
Cited alongside, same era.
Geoffrey E Hinton, “A practical guide to training restricted boltzmann machines,” in Neural networks: Tricks of the trade (Springer, 2012) pp. 599–619
2012
Cited alongside, same era.
Martin Schwarz, Kristan Temme, and Frank Verstraete, “Preparing projected entangled pair states on a quantum computer,” Phys. Rev. Lett. 108
2012
Cited alongside, same era.
Diederik P Kingma and Max Welling, “Auto-encoding variational bayes,” arXiv:1312.6114 (2013)
2013
Cited alongside, same era.
2014
Cited alongside, same era.
Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, and Hugo Larochelle, “Neural autoregressive distribution estimation,” Journal of Machine Learning Research 17
2016
Later among the works it cites.
Aaron Van Oord, Nal Kalchbrenner, and Koray Kavukcuoglu, “Pixel recurrent neural networks,” in Proceedings of The 33rd International Conference on Machine Learning , Proceedings of Machine Learning Research, Vol. 48 (PMLR, New York, New York, USA, 2016) pp. 1747–1756
2016
Later among the works it cites.
2016
Later among the works it cites.
Andrzej Cichocki, Namgil Lee, Ivan Oseledets, Anh-Huy Phan, Qibin Zhao, Danilo P Mandic, et al. , “Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions,” Foundations and Trends® in Machine Learning 9
2016
Later among the works it cites.
2016
Later among the works it cites.
2016
Later among the works it cites.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning (MIT Press, 2016) http://www.deeplearningbook.org
2016
Later among the works it cites.
Dmitry Krotov and John J Hopfield, “Dense associative memory for pattern recognition,” in Advances in Neural Information Processing Systems (2016) pp. 1172–1180
2016
Later among the works it cites.
Giuseppe Carleo and Matthias Troyer, “Solving the quantum many-body problem with artificial neural networks,” Science 355
2017
Closest in time.
2017
Closest in time.
2017
Closest in time.
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang, “A tutorial on energy-based learning,” (2006), http://yann.lecun.com/exdb/publis/orig/lecun-06.pdf (Accessed on 2-September-2017)
2017
Closest in time.
Yann LeCun, Corinnai Cortes, and Christopher J.C. Burges, “The mnist database of handwritten digits,” (1998), http://yann.lecun.com/exdb/mnist (Accessed on 2-September-2017)
2017
Closest in time.
2017
Closest in time.
Jing Chen, Song Cheng, Haidong Xie, Lei Wang, and Tao Xiang, “Equivalence of restricted boltzmann machines and tensor network states,” Phys. Rev. B 97
2018
Closest in time.
Edwin Miles Stoudenmire, “Learning relevant features of data with multi-scale tensor networks,” Quantum Science and Technology (2018)
2018
Closest in time.
Alejandro Perdomo-Ortiz, Marcello Benedetti, John Realpe-Gómez, and Rupak Biswas, “Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers,” Quantum Science and Technology 3
2018
Closest in time.