Adaptive and self-averaging Thouless-Anderson-Palmer mean-field theory for probabilistic modeling.
暂无分享,去创建一个
[1] S. Kirkpatrick,et al. Solvable Model of a Spin-Glass , 1975 .
[2] R. Palmer,et al. Solution of 'Solvable model of a spin glass' , 1977 .
[3] C. Dominicis. Toward a mean field theory of spin glasses: The TAP route revisited , 1980 .
[4] A. Bray,et al. Metastable states in spin glasses , 1980 .
[5] T. Plefka. Convergence condition of the TAP equation for the infinite-ranged Ising spin glass model , 1982 .
[6] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.
[7] M. Mézard,et al. Replicas and optimization , 1985 .
[8] M. Mézard,et al. Spin Glass Theory and Beyond , 1987 .
[9] Terrence J. Sejnowski,et al. Analysis of hidden units in a layered network trained to classify sonar targets , 1988, Neural Networks.
[10] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[11] M. Mézard. The space of interactions in neural networks: Gardner's computation with the cavity method , 1989 .
[12] Sompolinsky,et al. Statistical mechanics of learning from examples. , 1992, Physical review. A, Atomic, molecular, and optical physics.
[13] Radford M. Neal. Connectionist Learning of Belief Networks , 1992, Artif. Intell..
[14] Carsten Peterson,et al. Neural Networks for Optimization Problems with Inequality Constraints: The Knapsack Problem , 1993, Neural Computation.
[15] G. Parisi,et al. Replica field theory for deterministic models: II. A non-random spin glass with glassy behaviour , 1994, cond-mat/9406074.
[16] K. Y. Wong. Microscopic Equations and Stability Conditions in Optimal Neural Networks , 1995 .
[17] G. Parisi,et al. Mean-field equations for spin models with orthogonal interaction matrices , 1995, cond-mat/9503009.
[18] Michael I. Jordan,et al. Mean Field Theory for Sigmoid Belief Networks , 1996, J. Artif. Intell. Res..
[19] Opper. On-line versus Off-line Learning from Random Examples: General Results. , 1996, Physical review letters.
[20] Opper,et al. Mean field approach to Bayes learning in feed-forward neural networks. , 1996, Physical review letters.
[21] Yoshiyuki Kabashima,et al. Belief propagation vs. TAP for decoding corrupted messages , 1998 .
[22] Hilbert J. Kappen,et al. Efficient Learning in Boltzmann Machines Using Linear Response Theory , 1998, Neural Computation.
[23] Toshiyuki TANAKA. Mean-field theory of Boltzmann machine learning , 1998 .
[24] Te-Won Lee. Independent Component Analysis , 1998, Springer US.
[25] S. Keerthi,et al. Information geometry and Plefka's mean-field theory , 2000 .
[26] Ole Winther,et al. Gaussian Processes for Classification: Mean-Field Algorithms , 2000, Neural Computation.
[27] Tom Minka,et al. Expectation Propagation for approximate Bayesian inference , 2001, UAI.
[28] M. Opper,et al. Tractable approximations for probabilistic models: the adaptive Thouless-Anderson-Palmer mean field approach. , 2001, Physical review letters.
[29] William H. Press,et al. Numerical recipes in C , 2002 .