tractable approximate gaussian inference
Tractable Approximate Gaussian Inference for Bayesian Neural Networks
Goulet, James-A., Nguyen, Luong Ha, Amiri, Saeid
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.
2004.09281
Country:
- North America > Canada > Ontario > Toronto (0.14)
- North America > Canada > Quebec > Montreal (0.04)
- North America > United States > New Mexico > Los Alamos County > Los Alamos (0.04)
- (2 more...)
Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.68)