统计物理与神经计算

Emergence of hierarchical modes from deep learning

2022

期刊 arXiv:2208.09859

作者 Chan Li · Haiping Huang

Spectrum of non-Hermitian deep-Hebbian neural networks

2022

期刊 arXiv:2208.11411

作者 Zijian Jiang · Ziming Chen · Tianqi Hou · Haiping Huang

Statistical mechanics of continual learning: variational principle and mean-field potential

2022

期刊 arXiv:2212.02846

作者 Chan Li · Zhenyue Huang · Wenxuan Zou · Haiping Huang

Associative memory model with arbitrary Hebbian length

2021

期刊 PHYSICAL REVIEW E 104, 064306 (2021); arXiv: 2103.14317

作者 Zijian Jiang · Jianwen Zhou · Tianqi Hou · K Y Michael Wong · Haiping Huang

Eigenvalue spectrum of neural networks with arbitrary Hebbian length

2021

期刊 PHYSICAL REVIEW E 104, 064307 (2021) ; arXiv: 2103.14324

作者 Jianwen Zhou · Zijian Jiang · Tianqi Hou · Ziming Chen · K Y Michael Wong · Haiping Huang

Ensemble perspective for understanding temporal credit assignment

2021

期刊 arXiv: 2102.03740

作者 Wenxuan Zou · Chan Li · Haiping Huang

Equivalence between belief propagation instability and transition to replica symmetry breaking in perceptron learning systems

2021

期刊 PHYSICAL REVIEW RESEARCH 4, 023023 (2022)

作者 Yang Zhao · Junbin Qiu · Mingshan Xie · Haiping Huang

Data-driven effective model shows a liquid-like deep learning

2020

期刊 arXiv:2007.08093, Phys. Rev. Research 3, 033290 (2021)

作者 Wenxuan Zou · Haiping Huang

Learning credit assignment

2020

期刊 Phys. Rev. Lett. 125, 178301 (2020)

作者 Chan Li · Haiping Huang

Relationship between manifold smoothness and adversarial vulnerability in deep learning with local errors

2020

期刊 arXiv: 2007.02047 Chin. Phys. B Vol. 30, No. 4 (2021) 048702

作者 Zijian Jiang · Jianwen Zhou · Haiping Huang

Variational mean-field theory for training restricted Boltzmann machines with binary synapses

2020

期刊 Phys. Rev. E 102, 030301(R) (2020)

作者 Haiping Huang

Weakly-correlated synapses promote dimension reduction in deep neural networks

2020

期刊 arXiv: 2006.11569 Phys. Rev. E 103, 012315 (2021)

作者 Jianwen Zhou · Haiping Huang

Minimal model of permutation symmetry in unsupervised learning

2019

期刊 J. Phys. A: Math. Theor. 有全文

作者 Tianqi Hou · K Y Michael Wong · Haiping Huang

Statistical physics of unsupervised learning with prior knowledge in neural networks

2019

期刊 arXiv: 1911.02344，Phys. Rev. Lett. 124, 248302 （2020）

作者 Tianqi Hou · Haiping Huang

H. Huang and A. Goudarzi, Random active path model of deep neural networks with diluted binary synapses, to appear in Phys Rev E (2018)

2018

期刊 PHYSICAL REVIEW E 98, 042311 (2018) 有全文

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.

2018

期刊

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

2017

期刊

H. Huang, Mechanisms of dimensionality reduction and decorrelation in deep neural networks, arXiv:1710.01467 (2017)

2017

期刊 PHYSICAL REVIEW E 98, 062313 (2018) 有全文

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

2017

期刊

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.

2017

期刊

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/).

2016

期刊

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

2016

期刊

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

2015

期刊

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

2015

期刊

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

2015

期刊

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

2015

期刊

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

2014

期刊

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

2014

期刊

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).

2014

期刊

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

2013

期刊

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搜索结果: 30 (39)

Emergence of hierarchical modes from deep learning

Spectrum of non-Hermitian deep-Hebbian neural networks

Statistical mechanics of continual learning: variational principle and mean-field potential

Associative memory model with arbitrary Hebbian length

Eigenvalue spectrum of neural networks with arbitrary Hebbian length

Ensemble perspective for understanding temporal credit assignment

Equivalence between belief propagation instability and transition to replica symmetry breaking in perceptron learning systems

Data-driven effective model shows a liquid-like deep learning

Learning credit assignment

Relationship between manifold smoothness and adversarial vulnerability in deep learning with local errors

Variational mean-field theory for training restricted Boltzmann machines with binary synapses

Weakly-correlated synapses promote dimension reduction in deep neural networks

Minimal model of permutation symmetry in unsupervised learning

Statistical physics of unsupervised learning with prior knowledge in neural networks

H. Huang and A. Goudarzi, Random active path model of deep neural networks with diluted binary synapses, to appear in Phys Rev E (2018)

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.

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

H. Huang, Mechanisms of dimensionality reduction and decorrelation in deep neural networks, arXiv:1710.01467 (2017)

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

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.

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

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

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

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

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

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

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

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).

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