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Related Experiment Video

Updated: Jun 5, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

Expectation propagation with factorizing distributions: a Gaussian approximation and performance results for simple

Fabiano Ribeiro¹, Manfred Opper

¹Instituto de Física, Universidade de São Paulo, São Paulo, 05508-090, Brazil.

Neural Computation

|January 13, 2011

Summary

The expectation propagation (EP) algorithm offers efficient approximate Bayesian inference for neural networks. This method achieves optimal generalization performance, particularly with large datasets and simple distributions.

Related Experiment Videos

Last Updated: Jun 5, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

Area of Science:

Artificial Intelligence
Machine Learning
Statistical Inference

Background:

Approximate Bayesian inference is crucial for complex models like neural networks.
Tractable inference methods are needed for large-scale machine learning.
Expectation Propagation (EP) is a powerful approximate inference technique.

Purpose of the Study:

To adapt the Expectation Propagation (EP) algorithm for neural network models.
To demonstrate the tractability of EP for models with numerous parameters.
To evaluate the generalization performance of EP in specific scenarios.

Main Methods:

Utilizing a factorizing posterior approximation within the EP framework.
Applying a central limit theorem argument to ensure EP's scalability for large neural networks.
Testing EP on two distinct model types with data from a simple distribution.

Main Results:

The EP algorithm is rendered computationally tractable for neural networks with a large number of parameters.
EP demonstrates the ability to attain optimal generalization performance.
The effectiveness of EP is confirmed for models trained on data from a simple distribution.

Conclusions:

The expectation propagation algorithm provides an efficient and scalable approach for approximate Bayesian inference in neural networks.
EP achieves state-of-the-art generalization performance under specific data distribution assumptions.
This work highlights the potential of EP for advancing Bayesian deep learning.