TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - nicht begutachtet (unreviewed)
A1  - Hörtling, Stefan
A1  - Dold, Daniel
A1  - Dürr, Oliver
A1  - Sick, Beate
T1  - Transformation Models for Flexible Posteriors in Variational Bayes
N2  - The main challenge in Bayesian models is to determine the posterior for the model parameters. Already, in models with only one or few parameters, the analytical posterior can only be determined in special settings. In Bayesian neural networks, variational inference is widely used to approximate difficult-to-compute posteriors by variational distributions. Usually, Gaussians are used as variational distributions (Gaussian-VI) which limits the quality of the approximation due to their limited flexibility. Transformation models on the other hand are flexible enough to fit any distribution. Here we present transformation model-based variational inference (TM-VI) and demonstrate that it allows to accurately approximate complex posteriors in models with one parameter and also works in a mean-field fashion for multi-parameter models like neural networks.
KW  - Machine learning
Y1  - 2021
UR  - https://arxiv.org/abs/2106.00528v1
SP  - 5
S1  - 5
ER  -