GFlowOut: Dropout with Generative Flow Networks. (arXiv:2210.12928v2 [cs.LG] UPDATED)

Bayesian Inference offers principled tools to tackle many critical problems
with modern neural networks such as poor calibration and generalization, and
data inefficiency. However, scaling Bayesian inference to large architectures
is challenging and requires restrictive approximations. Monte Carlo Dropout has
been widely used as a relatively cheap way for approximate Inference and to
estimate uncertainty with deep neural networks. Traditionally, the dropout mask
is sampled independently from a fixed distribution. Recent works show that the
dropout mask can be viewed …

arxiv dropout flow networks