Home Page

Papers

Submissions

News

Editorial Board

Proceedings (PMLR)

Transactions (TMLR)

Open Source Software

Search

Statistics

Login

Frequently Asked Questions

Contact Us



RSS Feed

Tractable Approximate Gaussian Inference for Bayesian Neural Networks

James-A. Goulet, Luong Ha Nguyen, Saeid Amiri; 22(251):1−23, 2021.

Abstract

In this paper, we propose an analytical method for performing tractable approximate Gaussian inference (TAGI) in Bayesian neural networks. The method enables the analytical Gaussian inference of the posterior mean vector and diagonal covariance matrix for weights and biases. The method proposed has a computational complexity of $\mathcal{O}(n)$ with respect to the number of parameters $n$, and the tests performed on regression and classification benchmarks confirm that, for a same network architecture, it matches the performance of existing methods relying on gradient backpropagation.

[abs][pdf][bib]        [code]
© JMLR 2021. (edit, beta)