Deterministic Boltzmann learning performs steepest descent in weight-space
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Introduction to the theory of neural computation
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Algorithms for random generation and counting: a Markov chain approach
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Deterministic learning rules for Boltzmann machines
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Mean field theory for sigmoid belief networks
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Mean-field approaches to independent component analysis
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Approximate inference in Boltzmann machines
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Analyzing Boltzmann Machine Parameters for Fast Convergence
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Linear response algorithms for approximate inference in graphical models
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Lack of Consistency of Mean Field and Variational break Bayes Approximations for State Space Models
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Information Geometry of Mean-Field Approximation
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Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Variational cumulant expansions for intractable distributions
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Approximate learning algorithm in boltzmann machines
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IPF for discrete chain factor graphs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Boltzmann machine learning with the latent maximum entropy principle
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
The Ising decoder: reading out the activity of large neural ensembles
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