Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Elements of information theory
Elements of information theory
Connectionist learning of belief networks
Artificial Intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Neural Computation
Blocking Gibbs sampling in very large probabilistic expert systems
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
A hierarchical model of binocular rivalry
Neural Computation
Efficient learning in Boltzmann machines using linear response theory
Neural Computation
Variational learning in nonlinear Gaussian belief networks
Neural Computation
Neural Computation
Recurrent sampling models for the Helmholtz machine
Neural Computation
An Introduction to Variational Methods for Graphical Models
Machine Learning
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Deterministic Generative Models for Fast Feature Discovery
Data Mining and Knowledge Discovery
Bayesian parameter estimation via variational methods
Statistics and Computing
Mean-field approaches to independent component analysis
Neural Computation
A Double-Loop Algorithm to Minimize the Bethe Free Energy
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Handbook of data mining and knowledge discovery
Lack of Consistency of Mean Field and Variational break Bayes Approximations for State Space Models
Neural Processing Letters
A Tighter Bound for Graphical Models
Neural Computation
Neural Computation
Attractor Dynamics in Feedforward Neural Networks
Neural Computation
Noisy-OR Component Analysis and its Application to Link Analysis
The Journal of Machine Learning Research
Approximate algorithms for credal networks with binary variables
International Journal of Approximate Reasoning
Bayesian networks with a logistic regression model for the conditional probabilities
International Journal of Approximate Reasoning
Parameter Learning in Probabilistic Databases: A Least Squares Approach
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Cluster Selection Based on Coupling for Gaussian Mean Fields
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Latent tree models and approximate inference in Bayesian networks
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Latent tree models and approximate inference in Bayesian networks
Journal of Artificial Intelligence Research
Mean-field methods for a special class of belief networks
Journal of Artificial Intelligence Research
Variational cumulant expansions for intractable distributions
Journal of Artificial Intelligence Research
Challenge: what is the impact of Bayesian networks on learning?
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
Approximate learning in temporal Hidden Hopfield Models
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Incremental Sigmoid Belief Networks for Grammar Learning
The Journal of Machine Learning Research
Domain adaptation by constraining inter-domain variability of latent feature representation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Mixture approximations to Bayesian networks
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Variational approximations between mean field theory and the junction tree algorithm
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Large deviation methods for approximate probabilistic inference
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Structure and parameter learning for causal independence and causal interaction models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Computing upper and lower bounds on likelihoods in intractable networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Super-Gaussian mixture source model for ICA
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Stochastic variational inference
The Journal of Machine Learning Research
Gaussian Kullback-Leibler approximate inference
The Journal of Machine Learning Research
Multilingual joint parsing of syntactic and semantic dependencies with a latent variable model
Computational Linguistics
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We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition-the classification of handwritten digits.