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T. Toyoizumi and H. Huang, Structure of attractors in randomly connected networks, Phys. Rev. E 91, 032802 (2015).

统计物理与神经计算 , 中山大学

H. Huang and T. Toyoizumi, Advanced mean field theory of the restricted Boltzmann machine, Phys. Rev. E 91, 050101(Rapid Communication) (2015).

统计物理与神经计算 , 中山大学

H. Huang, Effects of hidden nodes on network structure inference, J. Phys. A: Math. Theor. 48 355002 (2015).

统计物理与神经计算 , 中山大学

H. Huang, Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons, arXiv: 1602.08299, J. Stat. Mech. (2017) 033501.

统计物理与神经计算 , 中山大学

H. Huang and T. Toyoizumi, Clustering of neural codewords revealed by a first-order phase transition, Phys. Rev. E 93, 062416 (2016) arXiv: 1602.06630. selected as one of the most interesting and intriguing arXiv papers from the past week by MIT Technology Review (www.technologyreview.com/s/600956/the-best-of-the-physics-arxiv-week-ending-march-5-2016/).

统计物理与神经计算 , 中山大学

H. Huang and T. Toyoizumi, Unsupervised feature learning from finite data by message passing: discontinuous versus continuous phase transition, Phys. Rev. E 94, 062310 (2016), arXiv:1608.03714 (2016) discovered spontaneous symmetry breaking in unsupervised learning

统计物理与神经计算 , 中山大学

H. Huang, Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses, arXiv:1612.01717 (2016), J. Stat. Mech. (2017) 053302, recommended in Quora

统计物理与神经计算 , 中山大学

H. Huang and T. Toyoizumi, Reinforced stochastic gradient descent for deep neural network learning, arXiv: 1701.07974 (2017)

统计物理与神经计算 , 中山大学

H. Huang, Role of zero synapses in unsupervised feature learning, arXiv: 1703.07943, J. Phys. A: Math. Theor. 51 08LT01 (2018) zero synapses act as indicators of concept formation, Published as a Letter.

统计物理与神经计算 , 中山大学

Information‐based summary statistics for spatial genetic structure inference

Lab of Machine Learning in Ecology, Evolution and Population Genomics , 北京林业大学

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