  Bayesian deep learning approaches assume model parameters to be latent random
variables and infer posterior distributions to quantify uncertainty, increase
safety and trust, and prevent overconfident and unpredictable behavior.
However, weight-space priors are model-specific, can be difficult to interpret
and are hard to specify. Instead, we apply a Dirichlet prior in predictive
space and perform approximate function-space variational inference. To this
end, we interpret conventional categorical predictions from stochastic neural
network classifiers as samples from an implicit Dirichlet distribution. By
adapting the inference, the same function-space prior can be combined with
different models without affecting model architecture or size. We illustrate
the flexibility and efficacy of such a prior with toy experiments and
demonstrate scalability, improved uncertainty quantification and adversarial
robustness with large-scale image classification experiments.
