Related Works In This Field

01.  Carrasquilla J, Melko RG. Machine learning phases of matter. arXiv preprint arXiv:160501735 . 2016 May ; http://arxiv.org/abs/1605.01735


02.  Stoudenmire EM, Schwab DJ. Supervised Learning with Quantum-Inspired Tensor Networks. arXiv:160505775 [cond-mat, stat] . 2016 May 18 ;http://arxiv.org/abs/1605.05775


03.  Carleo G, Troyer M. Solving the quantum many-body problem with artificial neural networks. Science . 2016 Jun;355(6325):602–6.http://science.sciencemag.org/content/355/6325/602


04.  Wang L. Discovering Phase Transitions with Unsupervised Learning. arXiv:160600318 [cond-mat, stat] . 2016 Jun 1 ; http://arxiv.org/abs/1606.00318


05.  Lin HW, Tegmark M. Why does deep and cheap learning work so well? arXiv:160808225 [cond-mat, stat] . 2016 Aug 29 ; http://arxiv.org/abs/1608.08225


06.  Cichocki A, Lee N, Oseledets IV, Phan A-H, Zhao Q, Mandic D. Low-Rank Tensor Networks for Dimensionality Reduction and Large-Scale Optimization Problems: Perspectives and Challenges PART 1. Foundations and Trends® in Machine Learning . 2016 Sep;9(4–5):249–429.http://arxiv.org/abs/1609.00893


07.  Ch’ng K, Carrasquilla J, Melko RG, Khatami E. Machine Learning Phases of Strongly Correlated Fermions. arXiv:160902552 [cond-mat] . 2016 Sep 8 ;http://arxiv.org/abs/1609.02552


08.  Nieuwenburg V, L EP, Liu Y-H, Huber SD. Learning phase transitions by confusion. 2016 Oct 6 ; https://arxiv.org/abs/1610.02048


09.  Huang L, Wang L. Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines. arXiv:161002746 [cond-mat, physics:physics, stat] . 2016 Oct 9 ; http://arxiv.org/abs/1610.02746


10.  Poggio T, Mhaskar H, Rosasco L, Miranda B, Liao Q. Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review. arXiv:161100740 [cs] . 2016 Nov 2 ; http://arxiv.org/abs/1611.00740

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