Optimal perceptron learning: as online Bayesian approach
On-line learning in neural networks
Statistical Mechanics of Learning
Statistical Mechanics of Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Assessing Approximate Inference for Binary Gaussian Process Classification
The Journal of Machine Learning Research
Expectation Consistent Approximate Inference
The Journal of Machine Learning Research
Information, Physics, and Computation
Information, Physics, and Computation
Expectation propagation for approximate Bayesian inference
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
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We discuss the expectation propagation EP algorithm for approximate Bayesian inference using a factorizing posterior approximation. For neural network models, we use a central limit theorem argument to make EP tractable when the number of parameters is large. For two types of models, we show that EP can achieve optimal generalization performance when data are drawn from a simple distribution.