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论文

H. Huang and H. Zhou, Combined local search strategy for learning in networks of binary synapses. Europhysics Letters 96, 58003 (2011).

统计物理与神经计算 , 中山大学

H. Huang, State sampling dependence of Hopfield network inference. Commun. Theor. Phys. 57, 169 (2012).

统计物理与神经计算 , 中山大学

H. Huang and H. Zhou, Counting solutions from finite samplings. Phys. Rev. E 85, 026118 (2012).

统计物理与神经计算 , 中山大学

H. Huang, and Y. Kabashima, Adaptive Thouless-Anderson-Palmer approach to inverse Ising problems with quenched random fields. Phys. Rev. E 87, 062129 (2013).

统计物理与神经计算 , 中山大学

H. Huang, K. Y. Michael Wong and Y. Kabashima, Entropy landscape of solutions in the binary perceptron problem. J. Phys. A: Math. Theor. 46 375002 (2013). Selected in the Research Highlights section of J. Phys. A. (iopscience.iop.org/1751-8121/labtalk-article/54799).

统计物理与神经计算 , 中山大学

H. Huang, Sparse Hopfield network reconstruction with l1 regularization. Eur. Phys. J. B 86, 484 (2013).

统计物理与神经计算 , 中山大学

H. Huang, J. Raymond and K.Y. Michael Wong, The network source location problem: ground state energy, entropy and effects of freezing. J. Stat. Phys 156, 301 (2014).

统计物理与神经计算 , 中山大学

H. Huang and Y. Kabashima, Dynamics of asymmetric kinetic Ising systems revisited. J. Stat. Mech.: Theory Exp. P05020 (2014). Proposed back-action field concept in asymmetric kinetic Ising system

统计物理与神经计算 , 中山大学

H. Huang, and Y. Kabashima, Origin of the computational hardness for learning with binary synapses, Phys. Rev. E 90, 052813 (2014). Solved a long standing problem—why is a binary perceptron hard to learn

统计物理与神经计算 , 中山大学

H. Huang, Code optimization, frozen glassy phase and improved decoding algorithms for low-density parity-check codes, Commun. Theor. Phys. 63, 115 (2015).

统计物理与神经计算 , 中山大学

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