Abstract: Traditional neural networks are simple to train but they typically produce overconfident predictions. In contrast, Bayesian neural networks provide good uncertainty quantification but optimizing them is time consuming due to the large parameter space. This paper proposes to combine the advantages of both approaches by performing Variational Inference in the Final layer Output space (VIFO), because the output space is much smaller than the parameter space. We use neural networks to learn the mean and the variance of the probabilistic output. Using the Bayesian formulation we incorporate collapsed variational inference with VIFO which significantly improves the performance in practice. On the other hand, like standard, non-Bayesian models, VIFO enjoys simple training and one can use Rademacher complexity to provide risk bounds for the model. Experiments show that VIFO provides a good tradeoff in terms of run time and uncertainty quantification, especially for out of distribution data.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: Add author information and acknowledgment for camera ready revision.
Add experimental details and code link.
Code: https://github.com/weiyadi/VIFO.git
Assigned Action Editor: ~Jasper_Snoek1
Submission Number: 2774
Loading