What's the verdict on this? Does Dropout do parameter uncertainty or risk estimation? Gal seems to be claiming the first, while the paper you linked claims the second.
This seems to be a novel application of dropout for uncertainty. The author's 2015 post linked by matheweis [0] gives an approachable walkthrough:
> I think that's why I was so surprised that dropout – a ubiquitous technique that's been in use in deep learning for several years now – can give us principled uncertainty estimates. Principled in the sense that the uncertainty estimates basically approximate those of our Gaussian process. Take your deep learning model in which you used dropout to avoid over-fitting – and you can extract model uncertainty without changing a single thing. Intuitively, you can think about your finite model as an approximation to a Gaussian process. When you optimise your objective, you minimise some "distance" (KL divergence to be more exact) between your model and the Gaussian process. I'll explain this in more detail below. But before this, let's recall what dropout is and introduce the Gaussian process quickly, and look at some examples of what this uncertainty obtained from dropout networks looks like.
