12 research outputs found
Approximate blocked Gibbs sampling for Bayesian neural networks
In this work, minibatch MCMC sampling for feedforward neural networks is made
more feasible. To this end, it is proposed to sample subgroups of parameters
via a blocked Gibbs sampling scheme. By partitioning the parameter space,
sampling is possible irrespective of layer width. It is also possible to
alleviate vanishing acceptance rates for increasing depth by reducing the
proposal variance in deeper layers. Increasing the length of a non-convergent
chain increases the predictive accuracy in classification tasks, so avoiding
vanishing acceptance rates and consequently enabling longer chain runs have
practical benefits. Moreover, non-convergent chain realizations aid in the
quantification of predictive uncertainty. An open problem is how to perform
minibatch MCMC sampling for feedforward neural networks in the presence of
augmented data